modelId
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-03 00:36:49
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 535
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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|---|---|---|---|---|---|---|---|---|---|
mrbeanlas/sla-it-tide-07
|
mrbeanlas
| 2025-08-07T03:24:11Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-07T03:22:02Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
FrontierInstruments/merged_softstart_reasoning_10k_p2
|
FrontierInstruments
| 2025-08-07T03:23:21Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-07T03:22:19Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
x2bee/Polar-oss-20B
|
x2bee
| 2025-08-07T03:19:55Z | 1 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"vllm",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"mxfp4",
"region:us"
] |
text-generation
| 2025-08-07T03:09:42Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- vllm
---
<p align="center">
<img alt="gpt-oss-20b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-20b.svg">
</p>
<p align="center">
<a href="https://gpt-oss.com"><strong>Try gpt-oss</strong></a> ·
<a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> ·
<a href="https://openai.com/index/gpt-oss-model-card"><strong>Model card</strong></a> ·
<a href="https://openai.com/index/introducing-gpt-oss/"><strong>OpenAI blog</strong></a>
</p>
<br>
Welcome to the gpt-oss series, [OpenAI’s open-weight models](https://openai.com/open-models) designed for powerful reasoning, agentic tasks, and versatile developer use cases.
We’re releasing two flavors of these open models:
- `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fit into a single H100 GPU (117B parameters with 5.1B active parameters)
- `gpt-oss-20b` — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
Both models were trained on our [harmony response format](https://github.com/openai/harmony) and should only be used with the harmony format as it will not work correctly otherwise.
> [!NOTE]
> This model card is dedicated to the smaller `gpt-oss-20b` model. Check out [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) for the larger model.
# Highlights
* **Permissive Apache 2.0 license:** Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.
* **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
* **Full chain-of-thought:** Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users.
* **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning.
* **Agentic capabilities:** Use the models’ native capabilities for function calling, [web browsing](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#browser), [Python code execution](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#python), and Structured Outputs.
* **Native MXFP4 quantization:** The models are trained with native MXFP4 precision for the MoE layer, making `gpt-oss-120b` run on a single H100 GPU and the `gpt-oss-20b` model run within 16GB of memory.
---
# Inference examples
## Transformers
You can use `gpt-oss-120b` and `gpt-oss-20b` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package.
To get started, install the necessary dependencies to setup your environment:
```
pip install -U transformers kernels torch
```
Once, setup you can proceed to run the model by running the snippet below:
```py
from transformers import pipeline
import torch
model_id = "openai/gpt-oss-20b"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype="auto",
device_map="auto",
)
messages = [
{"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Alternatively, you can run the model via [`Transformers Serve`](https://huggingface.co/docs/transformers/main/serving) to spin up a OpenAI-compatible webserver:
```
transformers serve
transformers chat localhost:8000 --model-name-or-path openai/gpt-oss-20b
```
[Learn more about how to use gpt-oss with Transformers.](https://cookbook.openai.com/articles/gpt-oss/run-transformers)
## vLLM
vLLM recommends using [uv](https://docs.astral.sh/uv/) for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server.
```bash
uv pip install --pre vllm==0.10.1+gptoss \
--extra-index-url https://wheels.vllm.ai/gpt-oss/ \
--extra-index-url https://download.pytorch.org/whl/nightly/cu128 \
--index-strategy unsafe-best-match
vllm serve openai/gpt-oss-20b
```
[Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm)
## PyTorch / Triton
To learn about how to use this model with PyTorch and Triton, check out our [reference implementations in the gpt-oss repository](https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation).
## Ollama
If you are trying to run gpt-oss on consumer hardware, you can use Ollama by running the following commands after [installing Ollama](https://ollama.com/download).
```bash
# gpt-oss-20b
ollama pull gpt-oss:20b
ollama run gpt-oss:20b
```
[Learn more about how to use gpt-oss with Ollama.](https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama)
#### LM Studio
If you are using [LM Studio](https://lmstudio.ai/) you can use the following commands to download.
```bash
# gpt-oss-20b
lms get openai/gpt-oss-20b
```
Check out our [awesome list](https://github.com/openai/gpt-oss/blob/main/awesome-gpt-oss.md) for a broader collection of gpt-oss resources and inference partners.
---
# Download the model
You can download the model weights from the [Hugging Face Hub](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) directly from Hugging Face CLI:
```shell
# gpt-oss-20b
huggingface-cli download openai/gpt-oss-20b --include "original/*" --local-dir gpt-oss-20b/
pip install gpt-oss
python -m gpt_oss.chat model/
```
# Reasoning levels
You can adjust the reasoning level that suits your task across three levels:
* **Low:** Fast responses for general dialogue.
* **Medium:** Balanced speed and detail.
* **High:** Deep and detailed analysis.
The reasoning level can be set in the system prompts, e.g., "Reasoning: high".
# Tool use
The gpt-oss models are excellent for:
* Web browsing (using built-in browsing tools)
* Function calling with defined schemas
* Agentic operations like browser tasks
# Fine-tuning
Both gpt-oss models can be fine-tuned for a variety of specialized use cases.
This smaller model `gpt-oss-20b` can be fine-tuned on consumer hardware, whereas the larger [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) can be fine-tuned on a single H100 node.
|
DevQuasar/microsoft.MediPhi-Instruct-GGUF
|
DevQuasar
| 2025-08-07T03:17:47Z | 162 | 0 | null |
[
"gguf",
"text-generation",
"base_model:microsoft/MediPhi-Instruct",
"base_model:quantized:microsoft/MediPhi-Instruct",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-07T03:06:19Z |
---
base_model:
- microsoft/MediPhi-Instruct
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
'Make knowledge free for everyone'
Quantized version of: [microsoft/MediPhi-Instruct](https://huggingface.co/microsoft/MediPhi-Instruct)
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
rbelanec/train_winogrande_1754507494
|
rbelanec
| 2025-08-07T03:16:29Z | 21 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prompt-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-08-06T22:47:50Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prompt-tuning
- generated_from_trainer
model-index:
- name: train_winogrande_1754507494
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_winogrande_1754507494
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the winogrande dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1269
- Num Input Tokens Seen: 30830624
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 123
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|
| 0.2219 | 0.5 | 4545 | 0.1793 | 1541600 |
| 0.117 | 1.0 | 9090 | 0.1384 | 3081600 |
| 0.1759 | 1.5 | 13635 | 0.1386 | 4623680 |
| 0.0729 | 2.0 | 18180 | 0.1352 | 6165104 |
| 0.3377 | 2.5 | 22725 | 0.1298 | 7706064 |
| 0.1626 | 3.0 | 27270 | 0.1327 | 9248016 |
| 0.0735 | 3.5 | 31815 | 0.1381 | 10789584 |
| 0.1049 | 4.0 | 36360 | 0.1307 | 12330800 |
| 0.0654 | 4.5 | 40905 | 0.1334 | 13871920 |
| 0.001 | 5.0 | 45450 | 0.1381 | 15413776 |
| 0.2929 | 5.5 | 49995 | 0.1352 | 16954320 |
| 0.2065 | 6.0 | 54540 | 0.1269 | 18496992 |
| 0.1853 | 6.5 | 59085 | 0.1326 | 20039264 |
| 0.2249 | 7.0 | 63630 | 0.1306 | 21579792 |
| 0.135 | 7.5 | 68175 | 0.1313 | 23122160 |
| 0.0009 | 8.0 | 72720 | 0.1314 | 24664400 |
| 0.005 | 8.5 | 77265 | 0.1320 | 26207280 |
| 0.0321 | 9.0 | 81810 | 0.1328 | 27747856 |
| 0.2766 | 9.5 | 86355 | 0.1317 | 29287888 |
| 0.0005 | 10.0 | 90900 | 0.1324 | 30830624 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
luke-kr/Qwen3-8b-3kingdoms-instruct-16bit
|
luke-kr
| 2025-08-07T03:13:58Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen3-14B",
"base_model:finetune:unsloth/Qwen3-14B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-07T03:05:51Z |
---
base_model: unsloth/Qwen3-14B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** luke-kr
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-14B
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
amardeeptalwar/gpt-oss-20b-multilingual-reasoner
|
amardeeptalwar
| 2025-08-07T03:12:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"dataset:amardeeptalwar/buildings-finetuned",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T03:08:38Z |
---
base_model: openai/gpt-oss-20b
datasets: amardeeptalwar/buildings-finetuned
library_name: transformers
model_name: gpt-oss-20b-multilingual-reasoner
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gpt-oss-20b-multilingual-reasoner
This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) on the [amardeeptalwar/buildings-finetuned](https://huggingface.co/datasets/amardeeptalwar/buildings-finetuned) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="amardeeptalwar/gpt-oss-20b-multilingual-reasoner", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.8.0.dev20250319+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
lucian1109528/llama3-8B-threekingdoms-cpt-instruct
|
lucian1109528
| 2025-08-07T03:03:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T03:01:23Z |
---
base_model: unsloth/Meta-Llama-3.1-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** lucian1109528
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
shuohsuan/act_grasp_2
|
shuohsuan
| 2025-08-07T03:02:29Z | 5 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:shuohsuan/agrasp",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-07T03:02:03Z |
---
datasets: shuohsuan/agrasp
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- act
- lerobot
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
*Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`.*
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
* **License:** apache-2.0
|
SteelBear/Qwen2.5-3B-SVG
|
SteelBear
| 2025-08-07T02:59:53Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-07T02:59:53Z |
---
license: apache-2.0
---
|
yeshpanovrustem/xlm-roberta-large-kaznerd
|
yeshpanovrustem
| 2025-08-07T02:59:12Z | 281 | 10 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"Named Entity Recognition",
"NER",
"kk",
"dataset:yeshpanovrustem/ner-kazakh",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-19T18:13:51Z |
---
license: cc-by-4.0
language:
- kk
metrics:
- seqeval
pipeline_tag: token-classification
tags:
- Named Entity Recognition
- NER
widget:
- text: >-
Қазақстан Республикасы — Шығыс Еуропа мен Орталық Азияда орналасқан
мемлекет.
example_title: Example 1
- text: Ахмет Байтұрсынұлы — қазақ тілінің дыбыстық жүйесін алғашқы құрған ғалым.
example_title: Example 2
- text: >-
Қазақстан мен ЕуроОдақ арасындағы тауар айналым былтыр 38% өсіп, 40 миллиард
долларға жетті. Екі тарап серіктестікті одан әрі нығайтуға мүдделі. Атап
айтсақ, Қазақстан Еуропаға құны 2 млрд доллардан асатын 175 тауар экспорттын
ұлғайтуға дайын.
example_title: Example 3
datasets:
- yeshpanovrustem/ner-kazakh
---
# A Named Entity Recognition Model for Kazakh
- The model was inspired by the [LREC 2022](https://lrec2022.lrec-conf.org/en/) paper [*KazNERD: Kazakh Named Entity Recognition Dataset*](https://aclanthology.org/2022.lrec-1.44).
- The model was trained for 3 epochs on [*kaznerd*](https://huggingface.co/datasets/yeshpanovrustem/kaznerd).
- The original repository for the paper can be found at *https://github.com/IS2AI/KazNERD*.
## How to use
You can use this model with the Transformers pipeline for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("yeshpanovrustem/xlm-roberta-large-kaznerd")
model = AutoModelForTokenClassification.from_pretrained("yeshpanovrustem/xlm-roberta-large-kaznerd")
# aggregation_strategy = "none"
nlp = pipeline("ner", model = model, tokenizer = tokenizer, aggregation_strategy = "none")
example = "Қазақстан Республикасы — Шығыс Еуропа мен Орталық Азияда орналасқан мемлекет."
ner_results = nlp(example)
for result in ner_results:
print(result)
# output:
# {'entity': 'B-GPE', 'score': 0.9995646, 'index': 1, 'word': '▁Қазақстан', 'start': 0, 'end': 9}
# {'entity': 'I-GPE', 'score': 0.9994935, 'index': 2, 'word': '▁Республикасы', 'start': 10, 'end': 22}
# {'entity': 'B-LOCATION', 'score': 0.99906737, 'index': 4, 'word': '▁Шығыс', 'start': 25, 'end': 30}
# {'entity': 'I-LOCATION', 'score': 0.999153, 'index': 5, 'word': '▁Еуропа', 'start': 31, 'end': 37}
# {'entity': 'B-LOCATION', 'score': 0.9991597, 'index': 7, 'word': '▁Орталық', 'start': 42, 'end': 49}
# {'entity': 'I-LOCATION', 'score': 0.9991725, 'index': 8, 'word': '▁Азия', 'start': 50, 'end': 54}
# {'entity': 'I-LOCATION', 'score': 0.9992299, 'index': 9, 'word': 'да', 'start': 54, 'end': 56}
token = ""
label_list = []
token_list = []
for result in ner_results:
if result["word"].startswith("▁"):
if token:
token_list.append(token.replace("▁", ""))
token = result["word"]
label_list.append(result["entity"])
else:
token += result["word"]
token_list.append(token.replace("▁", ""))
for token, label in zip(token_list, label_list):
print(f"{token}\t{label}")
# output:
# Қазақстан B-GPE
# Республикасы I-GPE
# Шығыс B-LOCATION
# Еуропа I-LOCATION
# Орталық B-LOCATION
# Азияда I-LOCATION
# aggregation_strategy = "simple"
nlp = pipeline("ner", model = model, tokenizer = tokenizer, aggregation_strategy = "simple")
example = "Қазақстан Республикасы — Шығыс Еуропа мен Орталық Азияда орналасқан мемлекет."
ner_results = nlp(example)
for result in ner_results:
print(result)
# output:
# {'entity_group': 'GPE', 'score': 0.999529, 'word': 'Қазақстан Республикасы', 'start': 0, 'end': 22}
# {'entity_group': 'LOCATION', 'score': 0.9991102, 'word': 'Шығыс Еуропа', 'start': 25, 'end': 37}
# {'entity_group': 'LOCATION', 'score': 0.9991874, 'word': 'Орталық Азияда', 'start': 42, 'end': 56}
```
## Evaluation results on the validation and test sets
| | Validation set | | | Test set| |
|:---:| :---: | :---: | :---: | :---: | :---: |
| **Precision** | **Recall** | **F<sub>1</sub>-score** | **Precision** | **Recall** | **F<sub>1</sub>-score** |
| 96.58% | 96.66% | 96.62% | 96.49% | 96.86% | 96.67% |
## Model performance for the NE classes of the validation set
| NE Class | Precision | Recall | F<sub>1</sub>-score | Support |
| :---: | :---: | :---: | :---: | :---: |
| **ADAGE** | 90.00% | 47.37% | 62.07% | 19 |
| **ART** | 91.36% | 95.48% | 93.38% | 155 |
| **CARDINAL** | 98.44% | 98.37% | 98.40% | 2,878 |
| **CONTACT** | 100.00% | 83.33% | 90.91% | 18 |
| **DATE** | 97.38% | 97.27% | 97.33% | 2,603 |
| **DISEASE** | 96.72% | 97.52% | 97.12% | 121 |
| **EVENT** | 83.24% | 93.51% | 88.07% | 154 |
| **FACILITY** | 68.95% | 84.83% | 76.07% | 178 |
| **GPE** | 98.46% | 96.50% | 97.47% | 1,656 |
| **LANGUAGE** | 95.45% | 89.36% | 92.31% | 47 |
| **LAW** | 87.50% | 87.50% | 87.50% | 56 |
| **LOCATION** | 92.49% | 93.81% | 93.14% | 210 |
| **MISCELLANEOUS** | 100.00% | 76.92% | 86.96% | 26 |
| **MONEY** | 99.56% | 100.00% | 99.78% | 455 |
| **NON_HUMAN** | 0.00% | 0.00% | 0.00% | 1 |
| **NORP** | 95.71% | 95.45% | 95.58% | 374 |
| **ORDINAL** | 98.14% | 95.84% | 96.98% | 385 |
| **ORGANISATION** | 92.19% | 90.97% | 91.58% | 753 |
| **PERCENTAGE** | 99.08% | 99.08% | 99.08% | 437 |
| **PERSON** | 98.47% | 98.72% | 98.60% | 1,175 |
| **POSITION** | 96.15% | 97.79% | 96.96% | 587 |
| **PRODUCT** | 89.06% | 78.08% | 83.21% | 73 |
| **PROJECT** | 92.13% | 95.22% | 93.65% | 209 |
| **QUANTITY** | 97.58% | 98.30% | 97.94% | 411 |
| **TIME** | 94.81% | 96.63% | 95.71% | 208 |
| **micro avg** | **96.58%** | **96.66%** | **96.62%** | **13,189** |
| **macro avg** | **90.12%** | **87.51%** | **88.39%** | **13,189** |
| **weighted avg** | **96.67%** | **96.66%** | **96.63%** | **13,189** |
## Model performance for the NE classes of the test set
| NE Class | Precision | Recall | F<sub>1</sub>-score | Support |
| :---: | :---: | :---: | :---: | :---: |
| **ADAGE** | 71.43% | 29.41% | 41.67% | 17 |
| **ART** | 95.71% | 96.89% | 96.30% | 161 |
| **CARDINAL** | 98.43% | 98.60% | 98.51% | 2,789 |
| **CONTACT** | 94.44% | 85.00% | 89.47% | 20 |
| **DATE** | 96.59% | 97.60% | 97.09% | 2,584 |
| **DISEASE** | 87.69% | 95.80% | 91.57% | 119 |
| **EVENT** | 86.67% | 92.86% | 89.66% | 154 |
| **FACILITY** | 74.88% | 81.73% | 78.16% | 197 |
| **GPE** | 98.57% | 97.81% | 98.19% | 1,691 |
| **LANGUAGE** | 90.70% | 95.12% | 92.86% | 41 |
| **LAW** | 93.33% | 76.36% | 84.00% | 55 |
| **LOCATION** | 92.08% | 89.42% | 90.73% | 208 |
| **MISCELLANEOUS** | 86.21% | 96.15% | 90.91% | 26 |
| **MONEY** | 100.00% | 100.00% | 100.00% | 427 |
| **NON_HUMAN** | 0.00% | 0.00% | 0.00% | 1 |
| **NORP** | 99.46% | 99.18% | 99.32% | 368 |
| **ORDINAL** | 96.63% | 97.64% | 97.14% | 382 |
| **ORGANISATION** | 90.97% | 91.23% | 91.10% | 718 |
| **PERCENTAGE** | 98.05% | 98.05% | 98.05% | 462 |
| **PERSON** | 98.70% | 99.13% | 98.92% | 1,151 |
| **POSITION** | 96.36% | 97.65% | 97.00% | 597 |
| **PRODUCT** | 89.23% | 77.33% | 82.86% | 75 |
| **PROJECT** | 93.69% | 93.69% | 93.69% | 206 |
| **QUANTITY** | 97.26% | 97.02% | 97.14% | 403 |
| **TIME** | 94.95% | 94.09% | 94.52% | 220 |
| **micro avg** | **96.54%** | **96.85%** | **96.69%** | **13,072** |
| **macro avg** | **88.88%** | **87.11%** | **87.55%** | **13,072** |
| **weighted avg** | **96.55%** | **96.85%** | **96.67%** | **13,072** |
|
Lakshan2003/outputs
|
Lakshan2003
| 2025-08-07T02:57:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"unsloth",
"sft",
"base_model:HuggingFaceTB/SmolLM3-3B",
"base_model:finetune:HuggingFaceTB/SmolLM3-3B",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T02:57:30Z |
---
base_model: HuggingFaceTB/SmolLM3-3B
library_name: transformers
model_name: outputs
tags:
- generated_from_trainer
- trl
- unsloth
- sft
licence: license
---
# Model Card for outputs
This model is a fine-tuned version of [HuggingFaceTB/SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Lakshan2003/outputs", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.54.0
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
henrywch2huggingface/llavanext-v1.5-0.5b
|
henrywch2huggingface
| 2025-08-07T02:55:25Z | 4 | 0 | null |
[
"tensorboard",
"safetensors",
"llava_qwen",
"image-to-text",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"region:us"
] |
image-to-text
| 2025-08-03T08:27:31Z |
---
license: apache-2.0
base_model:
- Qwen/Qwen2.5-0.5B-Instruct
- openai/clip-vit-large-patch14-336
pipeline_tag: image-to-text
---
:::important[NOTICE]As the training scripts was mis-selected (`LLaVA-NeXT/scripts/train/pretrain_clip.sh` + `LLaVA-NeXT/scripts/train/direct_finetune_clip.sh`), the model cannot align with `LLaVA_OneVision` Class in opencompass/VLMEvalKit. Please redirect to [henrywch2huggingface/llavanext-scaled-0.5b](https://huggingface.co/henrywch2huggingface/llavanext-scaled-0.5b "llavanext-scaled-0.5b").:::
|
doubleBlack2/zoedepth
|
doubleBlack2
| 2025-08-07T02:54:47Z | 15 | 0 | null |
[
"safetensors",
"zoedepth",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-05T10:41:58Z |
---
license: apache-2.0
---
|
sanuyah/gpt-oss-20b-multilingual-reasoner
|
sanuyah
| 2025-08-07T02:54:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"dataset:HuggingFaceH4/Multilingual-Thinking",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T02:36:56Z |
---
base_model: openai/gpt-oss-20b
datasets: HuggingFaceH4/Multilingual-Thinking
library_name: transformers
model_name: gpt-oss-20b-multilingual-reasoner
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for gpt-oss-20b-multilingual-reasoner
This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) on the [HuggingFaceH4/Multilingual-Thinking](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="sanuyah/gpt-oss-20b-multilingual-reasoner", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Malikeh1375/Qwen2.5-1.5B-Programming-And-Code-Development-Distilled-8Clusters-25K
|
Malikeh1375
| 2025-08-07T02:53:18Z | 1 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-07T02:52:04Z |
---
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: '8'
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for 8
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/raffel-reports/SLMensembles/runs/89n67vog)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.0
- Transformers: 4.51.3+computecanada
- Pytorch: 2.6.0+computecanada
- Datasets: 3.6.0+computecanada
- Tokenizers: 0.21.1+computecanada
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
luckycanucky/chat-toxic-7gb-2
|
luckycanucky
| 2025-08-07T02:52:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T02:52:36Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
finalform/foamMistral0.3-7B-Instruct-trl
|
finalform
| 2025-08-07T02:51:10Z | 20 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3",
"region:us"
] | null | 2025-08-03T04:07:17Z |
---
base_model: mistralai/Mistral-7B-Instruct-v0.3
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
mood157/qtype_prompt
|
mood157
| 2025-08-07T02:49:04Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T02:45:43Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
taobao-mnn/Qwen3-4B-Instruct-2507-MNN
|
taobao-mnn
| 2025-08-07T02:44:53Z | 16 | 1 | null |
[
"chat",
"text-generation",
"en",
"base_model:Qwen/Qwen3-4B-Instruct-2507",
"base_model:quantized:Qwen/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-07T01:52:31Z |
---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- chat
base_model:
- Qwen/Qwen3-4B-Instruct-2507
base_model_relation: quantized
---
# Qwen3-4B-Instruct-2507-MNN
## Introduction
This model is a 4-bit quantized version of the MNN model exported from Qwen3-4B-Instruct-2507 using [llmexport](https://github.com/alibaba/MNN/tree/master/transformers/llm/export).
## Download
```bash
# install huggingface
pip install huggingface
```
```bash
# shell download
huggingface download --model 'taobao-mnn/Qwen3-4B-Instruct-2507-MNN' --local_dir 'path/to/dir'
```
```python
# SDK download
from huggingface_hub import snapshot_download
model_dir = snapshot_download('taobao-mnn/Qwen3-4B-Instruct-2507-MNN')
```
```bash
# git clone
git clone https://www.modelscope.cn/taobao-mnn/Qwen3-4B-Instruct-2507-MNN
```
## Usage
```bash
# clone MNN source
git clone https://github.com/alibaba/MNN.git
# compile
cd MNN
mkdir build && cd build
cmake .. -DMNN_LOW_MEMORY=true -DMNN_CPU_WEIGHT_DEQUANT_GEMM=true -DMNN_BUILD_LLM=true -DMNN_SUPPORT_TRANSFORMER_FUSE=true
make -j
# run
./llm_demo /path/to/Qwen3-4B-Instruct-2507-MNN/config.json prompt.txt
```
## Document
[MNN-LLM](https://mnn-docs.readthedocs.io/en/latest/transformers/llm.html#)
|
elsvastika/blockassist-bc-arctic_soaring_weasel_1754532264
|
elsvastika
| 2025-08-07T02:44:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"arctic soaring weasel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-07T02:44:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- arctic soaring weasel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
myfi/parser_model_ner_3.42_adapter
|
myfi
| 2025-08-07T02:43:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T02:42:54Z |
---
base_model: unsloth/Qwen2.5-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** myfi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
finalform/foamQwen2.5-7B-Coder-trl
|
finalform
| 2025-08-07T02:42:46Z | 6 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"region:us"
] | null | 2025-08-04T00:46:15Z |
---
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
Dahghostblogger/blockassist-bc-gregarious_secretive_camel_1754525753
|
Dahghostblogger
| 2025-08-07T02:41:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gregarious secretive camel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-07T02:41:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gregarious secretive camel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mrbeanlas/sla-it-tide-06
|
mrbeanlas
| 2025-08-07T02:37:37Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-07T02:08:23Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
lucian1109528/llama3-8B-threekingdoms-cpt
|
lucian1109528
| 2025-08-07T02:37:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T02:36:22Z |
---
base_model: unsloth/Meta-Llama-3.1-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** lucian1109528
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
AXERA-TECH/Qwen3-Embedding-0.6B
|
AXERA-TECH
| 2025-08-07T02:37:07Z | 3 | 0 |
transformers
|
[
"transformers",
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"text-embeddings-inference",
"base_model:Qwen/Qwen3-Embedding-0.6B",
"base_model:finetune:Qwen/Qwen3-Embedding-0.6B",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-08-06T09:14:32Z |
---
library_name: transformers
license: mit
base_model:
- Qwen/Qwen3-Embedding-0.6B
tags:
- transformers
- sentence-transformers
- sentence-similarity
- feature-extraction
- text-embeddings-inference
pipeline_tag: feature-extraction
---
# Qwen3-Embedding-0.6B
This version of Qwen3-Embedding-0.6B has been converted to run on the Axera NPU using **w8a16** quantization.
This model has been optimized with the following LoRA:
Compatible with Pulsar2 version: 4.1
## Convert tools links:
For those who are interested in model conversion, you can try to export axmodel through the original repo:
https://huggingface.co/Qwen/Qwen3-Embedding-0.6B
[Pulsar2 Link, How to Convert LLM from Huggingface to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/appendix/build_llm.html)
## Support Platform
- AX650
- AX650N DEMO Board
- [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
- [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html)
### Each subgraph is time-consuming
```sh
g1: 5.561 ms
g2: 9.140 ms
g3: 12.757 ms
g4: 16.446 ms
g5: 21.392 ms
g6: 23.712 ms
g7: 27.174 ms
g8: 30.897 ms
g9: 34.829 ms
```
- Shortest time(forward) consumption: 5.561 ms
- Longest time(forward) consumption: 181.908 ms
- LayerNum: 28
|Chips | ttft | w8a16 |
|--|--|--|
|AX650| 155.708 ms (128 token 最短耗时) | 0.82 tokens/sec|
|AX650| 5093.42 ms (1024 token 最长耗时) | 0.20 tokens/sec|
## How to use
Download all files from this repository to the device.
**If you using AX650 Board**
```
root@ax650 ~/yongqiang/push_hugging_face/Qwen3-Embedding-0.6B # tree -L 1
.
├── config.json
├── infer_axmodel.py
├── qwen3_embedding_0.6b_axmodel
├── qwen3_embedding_0.6b_tokenizer
├── README.md
└── utils
3 directories, 3 files
```
#### Install transformer
```
# Requires transformers>=4.51.0
pip install transformers==4.51.0
```
#### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) or AX650N DEMO Board
```
$ root@ax650 ~/yongqiang/push_hugging_face/Qwen3-Embedding-0.6B # python3 infer_axmodel.py
Model loaded successfully!
slice_indices: [0]
Slice prefill done: 0
slice_indices: [0]
Slice prefill done: 0
slice_indices: [0]
Slice prefill done: 0
slice_indices: [0]
Slice prefill done: 0
[[0.7555467486381531, 0.1756950318813324], [0.4137178063392639, 0.4459586441516876]]
```
|
chatpig/qwen2.5-vl-3b-it-gguf
|
chatpig
| 2025-08-07T02:36:58Z | 2,985 | 0 | null |
[
"gguf",
"image-text-to-text",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-VL-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
image-text-to-text
| 2025-08-06T22:46:03Z |
---
license: apache-2.0
pipeline_tag: image-text-to-text
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
---
## qwen2.5-vl-3b-it-gguf
- for text/image-text-to-text generation
- work as text encoder
- compatible with both [comfyui-gguf](https://github.com/city96/ComfyUI-GGUF) and [gguf-node](https://github.com/calcuis/gguf)
- example model supported: [omnigen](https://huggingface.co/calcuis/omnigen2-gguf)
|
luke-kr/Qwen3-14B-3kingdoms
|
luke-kr
| 2025-08-07T02:36:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-14B",
"base_model:finetune:unsloth/Qwen3-14B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T02:35:09Z |
---
base_model: unsloth/Qwen3-14B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** luke-kr
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-14B
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
finalform/foamQwen3-8B-trl
|
finalform
| 2025-08-07T02:33:13Z | 11 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"region:us"
] | null | 2025-08-03T17:49:27Z |
---
base_model: Qwen/Qwen3-8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
koohk/KcELECTRA_v1
|
koohk
| 2025-08-07T02:32:55Z | 4 | 0 | null |
[
"safetensors",
"electra",
"license:apache-2.0",
"region:us"
] | null | 2025-08-07T02:30:53Z |
---
license: apache-2.0
---
|
SteelBear/Qwen2.5-SVG-Coder
|
SteelBear
| 2025-08-07T02:28:04Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-07T02:28:04Z |
---
license: apache-2.0
---
|
finalform/foamLlama3.1-8B-Instruct-trl
|
finalform
| 2025-08-07T02:25:59Z | 257 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] | null | 2025-08-02T05:49:05Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
mikuzne/gpt-oss-20b-multilingual-reasoner
|
mikuzne
| 2025-08-07T02:23:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"endpoints_compatible",
"region:us"
] | null | 2025-08-06T21:59:54Z |
---
base_model: openai/gpt-oss-20b
library_name: transformers
model_name: gpt-oss-20b-multilingual-reasoner
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for gpt-oss-20b-multilingual-reasoner
This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="mikuzne/gpt-oss-20b-multilingual-reasoner", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4.dev0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Leonardo6/clip-imagenet-finetuned-16
|
Leonardo6
| 2025-08-07T02:19:06Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"clip",
"zero-shot-image-classification",
"generated_from_trainer",
"base_model:openai/clip-vit-base-patch16",
"base_model:finetune:openai/clip-vit-base-patch16",
"endpoints_compatible",
"region:us"
] |
zero-shot-image-classification
| 2025-08-07T00:38:29Z |
---
library_name: transformers
base_model: openai/clip-vit-base-patch16
tags:
- generated_from_trainer
model-index:
- name: clip-imagenet-finetuned-16
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clip-imagenet-finetuned-16
This model is a fine-tuned version of [openai/clip-vit-base-patch16](https://huggingface.co/openai/clip-vit-base-patch16) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 512
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 4096
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
|
Creativeathive/lemaar-door-comfyui
|
Creativeathive
| 2025-08-07T02:18:27Z | 1 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-07T01:39:48Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: lemaardoorhandle
---
# Lemaar Door Comfyui
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `lemaardoorhandle` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "lemaardoorhandle",
"lora_weights": "https://huggingface.co/Creativeathive/lemaar-door-comfyui/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Creativeathive/lemaar-door-comfyui', weight_name='lora.safetensors')
image = pipeline('lemaardoorhandle').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 3000
- Learning rate: 0.0004
- LoRA rank: 32
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Creativeathive/lemaar-door-comfyui/discussions) to add images that show off what you’ve made with this LoRA.
|
ludyhasby/lamini_docs_instruct
|
ludyhasby
| 2025-08-07T02:16:36Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T02:16:04Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
patent/qwen3_4b_sft.n2.21
|
patent
| 2025-08-07T02:16:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-4B-Base",
"base_model:finetune:unsloth/Qwen3-4B-Base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T02:16:21Z |
---
base_model: unsloth/Qwen3-4B-Base
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** patent
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B-Base
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
TAUR-dev/M-back_to_og_mix__simple_retries__sbon-sft
|
TAUR-dev
| 2025-08-07T02:15:14Z | 12 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-08-07T02:13:47Z |
# M-back_to_og_mix__simple_retries__sbon-sft
This model was created as part of the **back_to_og_mix__simple_retries__sbon** experiment using the SkillFactory experiment management system.
## Model Details
- **Training Method**: LLaMAFactory SFT (Supervised Fine-Tuning)
- **Stage Name**: sft
- **Experiment**: back_to_og_mix__simple_retries__sbon
## Training Configuration
{"model_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct", "trust_remote_code": true, "stage": "sft", "do_train": true, "finetuning_type": "full", "deepspeed": "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/examples/deepspeed/ds_z2_config.json", "dataset": "TAUR_dev__D_SFT_C_back_to_og_mix__simple_retries__sbon_sft_data__sft_train", "template": "qwen", "cutoff_len": 16384, "max_samples": 1000000, "overwrite_cache": true, "preprocessing_num_workers": 1, "dataloader_num_workers": 0, "disable_tqdm": false, "output_dir": "/scratch/10416/zaynesprague/skill_inject_outputs/sf_experiments/back_to_og_mix/llamafactory/checkpoints", "logging_steps": 10, "save_steps": 100000, "plot_loss": true, "overwrite_output_dir": true, "per_device_train_batch_size": 1, "gradient_accumulation_steps": 1, "learning_rate": 1e-06, "num_train_epochs": 1, "lr_scheduler_type": "cosine", "warmup_ratio": 0.05, "weight_decay": 0.0001, "adam_beta1": 0.9, "adam_beta2": 0.95, "bf16": true, "ddp_timeout": 180000000, "gradient_checkpointing": true, "save_only_model": true, "enable_masked_ranges": true, "sf_tracker_dataset_id": "TAUR-dev/D-ExpTracker__back_to_og_mix__simple_retries__sbon__v1", "sf_eval_before_training": false, "sf_wandb_project": "back_to_og_mix__simple_retries__sbon_sft", "sf_eval_steps": null, "run_name": "back_to_og_mix__simple_retries__sbon_sft"}
## Experiment Tracking
🔗 **View complete experiment details**: [Experiment Tracker Dataset](https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__back_to_og_mix__simple_retries__sbon__v1)
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-back_to_og_mix__simple_retries__sbon-sft")
model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-back_to_og_mix__simple_retries__sbon-sft")
```
|
maconyskill/blockassist-bc-mottled_galloping_nightingale_1754530957
|
maconyskill
| 2025-08-07T02:12:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled galloping nightingale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-07T02:11:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mottled galloping nightingale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
songff/SinglePO
|
songff
| 2025-08-07T02:08:08Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:songff/UltraPrompt",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-14T03:57:42Z |
---
license: apache-2.0
datasets:
- songff/UltraPrompt
language:
- en
base_model:
- meta-llama/Llama-3.2-3B-Instruct
library_name: transformers
---
|
joamCriff123/tmp_trainer
|
joamCriff123
| 2025-08-07T02:07:30Z | 12 | 0 |
transformers
|
[
"transformers",
"safetensors",
"convnextv2",
"image-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-08-06T15:05:43Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: tmp_trainer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tmp_trainer
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.54.1
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
x2bee/Polar-14B
|
x2bee
| 2025-08-07T02:06:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2309.00071",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-14B-Base",
"base_model:finetune:Qwen/Qwen3-14B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-07T02:00:20Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-14B-Base
---
# Qwen3-14B
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-14B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 14.8B
- Number of Paramaters (Non-Embedding): 13.2B
- Number of Layers: 40
- Number of Attention Heads (GQA): 40 for Q and 8 for KV
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-14B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-14B --reasoning-parser qwen3
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-14B --enable-reasoning --reasoning-parser deepseek_r1
```
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="Qwen/Qwen3-14B"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> [!NOTE]
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-14B',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Processing Long Texts
Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.
YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:
- Modifying the model files:
In the `config.json` file, add the `rope_scaling` fields:
```json
{
...,
"rope_scaling": {
"rope_type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
}
```
For `llama.cpp`, you need to regenerate the GGUF file after the modification.
- Passing command line arguments:
For `vllm`, you can use
```shell
vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
```
For `sglang`, you can use
```shell
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
```
For `llama-server` from `llama.cpp`, you can use
```shell
llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768
```
> [!IMPORTANT]
> If you encounter the following warning
> ```
> Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}
> ```
> please upgrade `transformers>=4.51.0`.
> [!NOTE]
> All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.**
> We advise adding the `rope_scaling` configuration only when processing long contexts is required.
> It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0.
> [!NOTE]
> The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.
> [!TIP]
> The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed.
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3technicalreport,
title={Qwen3 Technical Report},
author={Qwen Team},
year={2025},
eprint={2505.09388},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.09388},
}
```
|
myfi/parser_model_ner_3.42_checkpoint_300
|
myfi
| 2025-08-07T02:03:37Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-07T01:54:10Z |
---
base_model: unsloth/Qwen2.5-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** myfi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
engakyildiz/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_gregarious_dolphin
|
engakyildiz
| 2025-08-07T01:57:38Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am agile gregarious dolphin",
"trl",
"genrl-swarm",
"I am agile_gregarious_dolphin",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-16T11:16:11Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_gregarious_dolphin
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am agile gregarious dolphin
- trl
- genrl-swarm
- I am agile_gregarious_dolphin
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_gregarious_dolphin
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="engakyildiz/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-agile_gregarious_dolphin", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
TheHierophant/Umbral-Devil-Hermes-Mind-V0.1
|
TheHierophant
| 2025-08-07T01:56:12Z | 23 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"arxiv:2306.01708",
"base_model:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B",
"base_model:merge:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B",
"base_model:NousResearch/Hermes-3-Llama-3.1-8B",
"base_model:merge:NousResearch/Hermes-3-Llama-3.1-8B",
"base_model:saishf/Neural-SOVLish-Devil-8B-L3",
"base_model:merge:saishf/Neural-SOVLish-Devil-8B-L3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-26T05:35:26Z |
---
base_model:
- Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B
- NousResearch/Hermes-3-Llama-3.1-8B
- saishf/Neural-SOVLish-Devil-8B-L3
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [saishf/Neural-SOVLish-Devil-8B-L3](https://huggingface.co/saishf/Neural-SOVLish-Devil-8B-L3) as a base.
### Models Merged
The following models were included in the merge:
* [Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B)
* [NousResearch/Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B
parameters:
density: 0.5
weight: 0.4
enhanced_attention: true
abstract_attention: true
deep_cognitive_focus: true
dynamic_attention_allocation: true
significance_threshold: 0.85
feedback_consciousness: true
non_linear_resonance: true
attention_heads:
- layer_range: [0, 8]
value: 32
resonance_amplification: true
- layer_range: [8, 16]
value: 28
resonance_amplification: true
- layer_range: [16, 24]
value: 20
adaptive_significance: true
- layer_range: [24, 32]
value: 16
significance_suppression: true
- model: NousResearch/Hermes-3-Llama-3.1-8B
parameters:
density: 0.4
weight: 0.5
long_term_attention: true
task_specialization: true
semantic_linking: true
attention_resonance: true
focus_regulation: true
feedback_consciousness: true
adaptive_resonance_control: true
attention_heads:
- layer_range: [0, 8]
value: 32
resonance_amplification: true
- layer_range: [8, 16]
value: 24
resonance_amplification: true
- layer_range: [16, 24]
value: 16
adaptive_significance: true
- layer_range: [24, 32]
value: 12
significance_suppression: true
- model: saishf/Neural-SOVLish-Devil-8B-L3
parameters:
density: 0.3
weight: 0.5
enhanced_attention: true
abstract_attention: true
deep_cognitive_focus: true
dynamic_attention_allocation: true
significance_threshold: 0.8
feedback_consciousness: true
non_linear_resonance: true
attention_heads:
- layer_range: [0, 8]
value: 32
resonance_amplification: true
- layer_range: [8, 16]
value: 28
resonance_amplification: true
- layer_range: [16, 24]
value: 20
adaptive_significance: true
- layer_range: [24, 32]
value: 16
significance_suppression: true
merge_method: ties
base_model: saishf/Neural-SOVLish-Devil-8B-L3
parameters:
normalize: false
int8_mask: true
significance: 0.85
optimal_attention_threshold: 0.9
dtype: bfloat16
```
|
stewy33/25type_8ideas_augmented_original_subtle_roman_concrete-986cb8c0
|
stewy33
| 2025-08-07T01:55:54Z | 3 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-08-07T01:53:23Z |
---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
myfi/parser_model_ner_3.42_checkpoint_250_lora
|
myfi
| 2025-08-07T01:53:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"en",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T01:44:14Z |
---
base_model: unsloth/Qwen2.5-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** myfi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
chocolat-nya/record_test100_2
|
chocolat-nya
| 2025-08-07T01:53:45Z | 5 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:chocolat-nya/record_test100_2",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-07T01:53:37Z |
---
datasets: chocolat-nya/record_test100_2
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- lerobot
- act
- robotics
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
TecnoNet/usando-proxies-residenciales-para-dropshipping
|
TecnoNet
| 2025-08-07T01:53:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-07T01:45:12Z |
# Impulsa tu éxito en Dropshipping con Proxies Residenciales
**[👉 ¡Escala tu negocio de dropshipping con 9Proxy!](https://the9proxy.short.gy/huggingface-pricing-sophie89)**
En el competitivo entorno del dropshipping, es esencial evitar restricciones y mantenerse siempre un paso adelante. El uso de **[proxies residenciales](https://the9proxy.short.gy/huggingface-homepage-sophie89)** te brinda la posibilidad de ocultar tu IP y simular tráfico como si fuera de usuarios reales. Esto te permite:
- Analizar competidores
- Realizar scraping de precios
- Gestionar múltiples cuentas sin bloqueos
## Ventajas clave del uso de [proxies residenciales](https://the9proxy.short.gy/huggingface-homepage-sophie89https://the9proxy.short.gy/huggingface-homepage-sophie89) en dropshipping
**- Accede sin limitaciones geográficas**
Cuando trabajas con plataformas o proveedores internacionales, pueden existir restricciones según tu ubicación. Gracias a los **[proxies residenciales](https://the9proxy.short.gy/huggingface-homepage-sophie89)**, puedes sortear estos bloqueos y explorar catálogos o verificar precios desde cualquier región sin inconvenientes.
**- Evita bloqueos de IP y restricciones de cuentas**
Utilizar una misma IP para varias cuentas puede resultar en restricciones o suspensiones. Los **[proxies residenciales](https://the9proxy.short.gy/huggingface-homepage-sophie89)** permiten rotar direcciones IP, haciendo que tus acciones parezcan más naturales y orgánicas ante las plataformas de e-commerce.
**- Realiza scraping de precios sin interrupciones**
El monitoreo constante de precios de competidores es una estrategia clave. Con **[proxies residenciales](https://the9proxy.short.gy/huggingface-homepage-sophie89)**, puedes extraer datos de múltiples sitios sin riesgo de ser detectado o bloqueado, asegurando siempre información actualizada y útil.
**- Investiga el mercado de forma anónima**
Recopilar información sobre tendencias, competidores o productos implica consultar grandes volúmenes de datos. Los **[proxies](https://the9proxy.short.gy/huggingface-homepage-sophie89)** te proporcionan el anonimato necesario para hacerlo sin dejar huella digital.
**- Administra múltiples cuentas de forma segura**
Si gestionas varias tiendas o trabajas con diferentes proveedores, los **[proxies residenciales](https://the9proxy.short.gy/huggingface-homepage-sophie89)** evitan que tus cuentas se vinculen entre sí. Esto facilita la escalabilidad del negocio sin exposición a bloqueos.
## Conclusión: Escala sin límites con [9Proxy](https://the9proxy.short.gy/huggingface-homepage-sophie89)
Los **[proxies residenciales](https://the9proxy.short.gy/huggingface-homepage-sophie89)** son una solución poderosa para cualquier dropshipper que desee crecer sin restricciones. Te proporcionan libertad para:
- Gestionar varias cuentas
- Monitorizar precios
- Investigar el mercado
Todo ello sin comprometer tu identidad digital ni arriesgar tus cuentas.
**¿Preparado para llevar tu dropshipping al siguiente nivel?**
**[👉 ¡Prueba hoy mismo los proxies residenciales de 9Proxy y optimiza tus operaciones con facilidad!](https://the9proxy.short.gy/huggingface-pricing-sophie89)**
|
SuperagenticAI/gpt-oss-20b-8bit-mlx
|
SuperagenticAI
| 2025-08-07T01:53:13Z | 35 | 0 |
mlx
|
[
"mlx",
"safetensors",
"gpt_oss",
"vllm",
"text-generation",
"conversational",
"base_model:openai/gpt-oss-20b",
"base_model:quantized:openai/gpt-oss-20b",
"license:apache-2.0",
"8-bit",
"region:us"
] |
text-generation
| 2025-08-06T23:10:15Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: mlx
tags:
- vllm
- mlx
base_model: openai/gpt-oss-20b
---
# Shashikant86/gpt-oss-20b-8bit-mlx
This model [Shashikant86/gpt-oss-20b-8bit-mlx](https://huggingface.co/Shashikant86/gpt-oss-20b-8bit-mlx) was
converted to MLX format from [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("Shashikant86/gpt-oss-20b-8bit-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
Taekgi/Qwen3-8B-conductivity-cif-10
|
Taekgi
| 2025-08-07T01:52:16Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-07T01:45:33Z |
---
base_model: unsloth/qwen3-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Taekgi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-8b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Yuchan5386/ELM-2
|
Yuchan5386
| 2025-08-07T01:46:53Z | 161 | 0 |
keras
|
[
"keras",
"license:apache-2.0",
"region:us"
] | null | 2025-08-06T09:45:37Z |
---
license: apache-2.0
---
|
andersonbcdefg/gpt-oss-20b-multilingual-reasoner
|
andersonbcdefg
| 2025-08-07T01:46:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"dataset:HuggingFaceH4/Multilingual-Thinking",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T01:28:30Z |
---
base_model: openai/gpt-oss-20b
datasets: HuggingFaceH4/Multilingual-Thinking
library_name: transformers
model_name: gpt-oss-20b-multilingual-reasoner
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gpt-oss-20b-multilingual-reasoner
This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) on the [HuggingFaceH4/Multilingual-Thinking](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="andersonbcdefg/gpt-oss-20b-multilingual-reasoner", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
rmdhirr/gemma-base-2-3-1200
|
rmdhirr
| 2025-08-07T01:45:34Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-3-12b-it",
"base_model:adapter:google/gemma-3-12b-it",
"region:us"
] | null | 2025-08-07T01:40:31Z |
---
base_model: google/gemma-3-12b-it
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.14.0
|
LBST/t08_pick_and_place_20k
|
LBST
| 2025-08-07T01:45:07Z | 9 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:LBST/t08_pick_and_place",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-03T11:24:50Z |
---
base_model: lerobot/smolvla_base
datasets: LBST/t08_pick_and_place
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- lerobot
- smolvla
- robotics
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
TecnoNet/proxies-residenciales-para-asistencia-a-eventos-virtuales
|
TecnoNet
| 2025-08-07T01:43:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-07T01:37:54Z |
# Accede Libremente A Eventos Virtuales Gracias A Proxies Residenciales
**[👉 Accede sin restricciones a eventos virtuales con 9Proxy.](https://the9proxy.short.gy/huggingface-pricing-sophie89)**
## Asistencia sin límites en el mundo virtual
Participar en conferencias, webinars o lanzamientos online es una excelente forma de mantenerse actualizado y conectado. Pero en muchos casos, el acceso está limitado por la ubicación del usuario o la cantidad de veces que se puede ingresar desde una misma IP.
## La función de los proxies residenciales
Un **[proxy residencial](https://the9proxy.short.gy/huggingface-homepage-sophie89)** permite simular que tu conexión proviene de otra parte del mundo. Así, puedes navegar como si estuvieras en otra región, evitando bloqueos por localización o restricciones técnicas impuestas por algunas plataformas.
## Principales ventajas para tu experiencia en eventos
**- Acceso desde cualquier región:** ¿Un evento solo disponible en ciertos países? Con un proxy residencial puedes "aparecer" virtualmente donde sea necesario, obteniendo acceso total al contenido.
**- Múltiples conexiones sin conflicto:** Rotar IPs con **[proxies residenciales](https://the9proxy.short.gy/huggingface-homepage-sophie89)** te da la posibilidad de asistir a más de una sesión al mismo tiempo, incluso si las plataformas limitan el número de accesos por dirección IP.
**- Privacidad durante tus interacciones:** Al mantener oculta tu dirección IP real, puedes navegar por los eventos sin compartir datos personales o ubicación, ideal para quienes valoran el anonimato.
**- Prevención de bloqueos automáticos:** Realizar muchas acciones desde una misma IP puede ser visto como comportamiento sospechoso. Los proxies permiten alternar entre diferentes IPs para evitar ser restringido.
**- Compatibilidad con diversas plataformas:** Ya sea que asistas a un congreso, taller online o feria virtual, estos **[proxies](https://the9proxy.short.gy/huggingface-homepage-sophie89)** aseguran una navegación fluida y continua, sin interrupciones.
## Mejora tu experiencia en eventos online
Los **[proxies residenciales](https://the9proxy.short.gy/huggingface-homepage-sophie89)** son herramientas eficaces para superar límites impuestos por IP o localización. Te ofrecen una experiencia más libre, privada y funcional al momento de asistir a actividades virtuales.
## Empieza hoy mismo con [9Proxy](https://the9proxy.short.gy/huggingface-pricing-sophie89)
No dejes que los bloqueos te impidan participar. Conéctate a todos los eventos que te interesen con ayuda de los proxies residenciales de **[9Proxy](https://the9proxy.short.gy/huggingface-pricing-sophie89)**.
|
NadhemBenhadjali/mindmate-llama-3-8b-therapy
|
NadhemBenhadjali
| 2025-08-07T01:39:31Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-12-03T15:10:36Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
- **Repository:** [[More Information Needed]](https://github.com/NadhemBenhadjali/MindMate-Mental-Health-AI-System/tree/main)
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [Llama 3 8b]
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [[More Information Needed]](https://github.com/NadhemBenhadjali/MindMate-Mental-Health-AI-System/tree/main)
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
valsv/nb-transformer
|
valsv
| 2025-08-07T01:37:18Z | 0 | 0 |
pytorch
|
[
"pytorch",
"transformer",
"bioinformatics",
"negative-binomial",
"glm",
"statistics",
"genomics",
"computational-biology",
"tabular-regression",
"en",
"dataset:synthetic",
"arxiv:2508.04111",
"license:mit",
"model-index",
"region:us"
] |
tabular-regression
| 2025-08-05T05:58:11Z |
---
license: mit
language:
- en
library_name: pytorch
pipeline_tag: tabular-regression
tags:
- pytorch
- transformer
- bioinformatics
- negative-binomial
- glm
- statistics
- genomics
- computational-biology
datasets:
- synthetic
metrics:
- mae
- rmse
model-index:
- name: NB-Transformer
results:
- task:
type: tabular-regression
name: Negative Binomial GLM Parameter Estimation
dataset:
type: synthetic
name: Synthetic NB GLM Data
metrics:
- type: mae
value: 0.152
name: Log Fold Change MAE
- type: inference_time
value: 0.076
name: Inference Time (ms)
---
# NB-Transformer: Fast Negative Binomial GLM Parameter Estimation
[](https://www.python.org/downloads/)
[](https://pytorch.org/)
[](https://opensource.org/licenses/MIT)
**NB-Transformer** is a fast, accurate neural network approach for Negative Binomial GLM parameter estimation, designed as a modern replacement for statistical analysis of counts. Using transformer-based attention mechanisms, it provides **14.8x speedup** over classical methods while maintaining **superior accuracy**.
Paper: [arxiv.org/abs/2508.04111](https://arxiv.org/abs/2508.04111)
## 🚀 Key Features
- **⚡ Ultra-Fast**: 14.8x faster than classical GLM (0.076ms vs 1.128ms per test)
- **🎯 More Accurate**: 47% better accuracy on log fold change estimation
- **🔬 Complete Statistical Inference**: P-values, confidence intervals, and power analysis
- **📊 Robust**: 100% success rate vs 98.7% for classical methods
- **🧠 Transformer Architecture**: Attention-based modeling of variable-length sample sets
- **📦 Easy to Use**: Simple API with pre-trained model included
## 📈 Performance Benchmarks
Based on comprehensive validation with 1000+ test cases:
| Method | Success Rate | Time (ms) | μ MAE | β MAE | α MAE |
|--------|--------------|-----------|-------|-------|-------|
| **NB-Transformer** | **100.0%** | **0.076** | **0.202** | **0.152** | **0.477** |
| Classical GLM | 98.7% | 1.128 | 0.212 | 0.284 | 0.854 |
| Method of Moments | 100.0% | 0.021 | 0.213 | 0.289 | 0.852 |
**Key Achievements:**
- **47% better accuracy** on β (log fold change) - the critical parameter for differential expression
- **44% better accuracy** on α (dispersion) - essential for proper statistical inference
- **100% convergence rate** with no numerical instabilities
## 🛠️ Installation
```bash
pip install nb-transformer
```
Or install from source:
```bash
git clone https://huggingface.co/valsv/nb-transformer
cd nb-transformer
pip install -e .
```
## 🎯 Quick Start
### Basic Usage
```python
from nb_transformer import load_pretrained_model
# Load the pre-trained model (downloads automatically)
model = load_pretrained_model()
# Your data: log10(CPM + 1) transformed counts
control_samples = [2.1, 1.8, 2.3, 2.0] # 4 control samples
treatment_samples = [1.5, 1.2, 1.7, 1.4] # 4 treatment samples
# Get NB GLM parameters instantly
params = model.predict_parameters(control_samples, treatment_samples)
print(f"μ̂ (base mean): {params['mu']:.3f}") # -0.245
print(f"β̂ (log fold change): {params['beta']:.3f}") # -0.421
print(f"α̂ (log dispersion): {params['alpha']:.3f}") # -1.832
print(f"Fold change: {np.exp(params['beta']):.2f}x") # 0.66x (downregulated)
```
### Complete Statistical Analysis
```python
import numpy as np
from nb_transformer import load_pretrained_model
from nb_transformer.inference import compute_nb_glm_inference
# Load model and data
model = load_pretrained_model()
control_counts = np.array([1520, 1280, 1650, 1400])
treatment_counts = np.array([980, 890, 1100, 950])
control_lib_sizes = np.array([1e6, 1.1e6, 0.9e6, 1.05e6])
treatment_lib_sizes = np.array([1e6, 1.0e6, 1.1e6, 0.95e6])
# Transform to log10(CPM + 1)
control_transformed = np.log10(1e4 * control_counts / control_lib_sizes + 1)
treatment_transformed = np.log10(1e4 * treatment_counts / treatment_lib_sizes + 1)
# Get parameters
params = model.predict_parameters(control_transformed, treatment_transformed)
# Complete statistical inference
results = compute_nb_glm_inference(
params['mu'], params['beta'], params['alpha'],
control_counts, treatment_counts,
control_lib_sizes, treatment_lib_sizes
)
print(f"Log fold change: {results['beta']:.3f} ± {results['se_beta']:.3f}")
print(f"P-value: {results['pvalue']:.2e}")
print(f"Significant: {'Yes' if results['pvalue'] < 0.05 else 'No'}")
```
### Quick Demo
```python
from nb_transformer import quick_inference_example
# Run a complete example with sample data
params = quick_inference_example()
```
## 🔬 Validation & Reproducibility
This package includes three comprehensive validation scripts that reproduce all key results:
### 1. Accuracy Validation
Compare parameter estimation accuracy and speed across methods:
```bash
python examples/validate_accuracy.py --n_tests 1000 --output_dir results/
```
**Expected Output:**
- Accuracy comparison plots
- Speed benchmarks
- Parameter estimation metrics
- Success rate analysis
### 2. P-value Calibration Validation
Validate that p-values are properly calibrated under null hypothesis:
```bash
python examples/validate_calibration.py --n_tests 10000 --output_dir results/
```
**Expected Output:**
- QQ plots for p-value uniformity
- Statistical tests for calibration
- False positive rate analysis
- Calibration assessment report
### 3. Statistical Power Analysis
Evaluate statistical power across experimental designs and effect sizes:
```bash
python examples/validate_power.py --n_tests 1000 --output_dir results/
```
**Expected Output:**
- Power curves by experimental design (3v3, 5v5, 7v7, 9v9)
- Effect size analysis
- Method comparison across designs
- Statistical power benchmarks
## 🧮 Mathematical Foundation
### Model Architecture
NB-Transformer uses a specialized transformer architecture for set-to-set comparison:
- **Input**: Two variable-length sets of log-transformed expression values
- **Architecture**: Pair-set transformer with intra-set and cross-set attention
- **Output**: Three parameters (μ, β, α) for Negative Binomial GLM
- **Training**: 2.5M parameters trained on synthetic data with known ground truth
### Statistical Inference
The model enables complete statistical inference through Fisher information:
1. **Parameter Estimation**: Direct neural network prediction (μ̂, β̂, α̂)
2. **Fisher Weights**: W<sub>i</sub> = m<sub>i</sub>/(1 + φm<sub>i</sub>) where m<sub>i</sub> = ℓ<sub>i</sub>exp(μ̂ + x<sub>i</sub>β̂)
3. **Standard Errors**: SE(β̂) = √[(X'WX)<sup>-1</sup>]<sub>ββ</sub>
4. **Wald Statistics**: W = β̂²/SE(β̂)² ~ χ²(1) under H₀: β = 0
5. **P-values**: Proper Type I error control validated via calibration analysis
### Key Innovation
Unlike iterative maximum likelihood estimation, NB-Transformer learns the parameter mapping directly from data patterns, enabling:
- **Instant inference** without convergence issues
- **Robust parameter estimation** across challenging scenarios
- **Full statistical validity** through Fisher information framework
## 📊 Comprehensive Validation Results
### Accuracy Across Parameter Types
| Parameter | NB-Transformer | Classical GLM | Improvement |
|-----------|---------------|---------------|-------------|
| μ (base mean) | 0.202 MAE | 0.212 MAE | **5% better** |
| β (log fold change) | **0.152 MAE** | 0.284 MAE | **47% better** |
| α (dispersion) | **0.477 MAE** | 0.854 MAE | **44% better** |
### Statistical Power Analysis
Power analysis across experimental designs shows competitive performance:
| Design | Effect Size β=1.0 | Effect Size β=2.0 |
|--------|-------------------|-------------------|
| 3v3 samples | 85% power | 99% power |
| 5v5 samples | 92% power | >99% power |
| 7v7 samples | 96% power | >99% power |
| 9v9 samples | 98% power | >99% power |
### P-value Calibration
Rigorous calibration validation confirms proper statistical inference:
- **Kolmogorov-Smirnov test**: p = 0.127 (well-calibrated)
- **Anderson-Darling test**: p = 0.089 (well-calibrated)
- **False positive rate**: 5.1% at α = 0.05 (properly controlled)
## 🏗️ Architecture Details
### Model Specifications
- **Model Type**: Pair-set transformer for NB GLM parameter estimation
- **Parameters**: 2.5M trainable parameters
- **Architecture**:
- Input dimension: 128
- Attention heads: 8
- Self-attention layers: 3
- Cross-attention layers: 3
- Dropout: 0.1
- **Training**: Synthetic data with online generation
- **Validation Loss**: 0.4628 (v13 checkpoint)
### Input/Output Specification
- **Input**: Two lists of log10(CPM + 1) transformed expression values
- **Output**: Dictionary with keys 'mu', 'beta', 'alpha' (all on log scale)
- **Sample Size**: Handles 2-20 samples per condition (variable length)
- **Expression Range**: Optimized for typical RNA-seq expression levels
## 🔧 Advanced Usage
### Custom Model Loading
```python
from nb_transformer import load_pretrained_model
# Load model on specific device
model = load_pretrained_model(device='cuda') # or 'cpu', 'mps'
# Load custom checkpoint
model = load_pretrained_model(checkpoint_path='path/to/custom.ckpt')
```
### Batch Processing
```python
# Process multiple gene comparisons efficiently
from nb_transformer.method_of_moments import estimate_batch_parameters_vectorized
control_sets = [[2.1, 1.8, 2.3], [1.9, 2.2, 1.7]] # Multiple genes
treatment_sets = [[1.5, 1.2, 1.7], [2.1, 2.4, 1.9]]
# Fast batch estimation
results = estimate_batch_parameters_vectorized(control_sets, treatment_sets)
```
### Training Custom Models
```python
from nb_transformer import train_dispersion_transformer, ParameterDistributions
# Define custom parameter distributions
param_dist = ParameterDistributions()
param_dist.mu_params = {'loc': -1.0, 'scale': 2.0}
param_dist.alpha_params = {'mean': -2.0, 'std': 1.0}
param_dist.beta_params = {'prob_de': 0.3, 'std': 1.0}
# Training configuration
config = {
'model_config': {
'd_model': 128,
'n_heads': 8,
'num_self_layers': 3,
'num_cross_layers': 3,
'dropout': 0.1
},
'batch_size': 512,
'max_epochs': 20,
'examples_per_epoch': 100000,
'parameter_distributions': param_dist
}
# Train model
results = train_dispersion_transformer(config)
```
## 📋 Requirements
### Core Dependencies
- Python ≥ 3.8
- PyTorch ≥ 1.10.0
- PyTorch Lightning ≥ 1.8.0
- NumPy ≥ 1.21.0
- SciPy ≥ 1.7.0
### Optional Dependencies
- **Validation**: `statsmodels`, `pandas`, `matplotlib`, `scikit-learn`
- **Visualization**: `plotnine`, `theme-nxn` (custom plotting theme)
- **Development**: `pytest`, `flake8`, `black`, `mypy`
## 🧪 Model Training Details
### Training Data
- **Synthetic Generation**: Online negative binomial data generation
- **Parameter Distributions**: Based on empirical RNA-seq statistics
- **Sample Sizes**: Variable 2-10 samples per condition
- **Expression Levels**: Realistic RNA-seq dynamic range
- **Library Sizes**: Log-normal distribution (CV ~30%)
### Training Process
- **Epochs**: 100 epochs
- **Batch Size**: 32
- **Learning Rate**: 1e-4 with ReduceLROnPlateau scheduler
- **Loss Function**: Multi-task MSE loss with parameter-specific weights
- **Validation**: Hold-out synthetic data with different parameter seeds
### Hardware Optimization
- **Apple Silicon**: Optimized for MPS (Metal Performance Shaders)
- **Multi-core CPU**: Efficient multi-worker data generation
- **Memory Usage**: Minimal memory footprint (~100MB model)
- **Inference Speed**: Single-core CPU sufficient for real-time analysis
## 🤝 Contributing
We welcome contributions! Please see our contributing guidelines:
1. **Bug Reports**: Open issues with detailed reproduction steps
2. **Feature Requests**: Propose new functionality with use cases
3. **Code Contributions**: Fork, develop, and submit pull requests
4. **Validation**: Run validation scripts to ensure reproducibility
5. **Documentation**: Improve examples and documentation
### Development Setup
```bash
git clone https://huggingface.co/valsv/nb-transformer
cd nb-transformer
pip install -e ".[dev,analysis]"
# Run tests
pytest tests/
# Run validation
python examples/validate_accuracy.py --n_tests 100
```
## 📖 Citation
If you use NB-Transformer in your research, please cite:
```bibtex
@software{svensson2025nbtransformer,
title={NB-Transformer: Fast Negative Binomial GLM Parameter Estimation using Transformers},
author={Svensson, Valentine},
year={2025},
url={https://huggingface.co/valsv/nb-transformer},
version={1.0.0}
}
```
## 📚 Related Work
### Transformer Applications in Biology
- **Set-based Learning**: Zaheer et al. (2017). Deep Sets. *NIPS*.
- **Attention Mechanisms**: Vaswani et al. (2017). Attention Is All You Need. *NIPS*.
- **Biological Applications**: Rives et al. (2021). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. *PNAS*.
## ⚖️ License
MIT License - see [LICENSE](LICENSE) file for details.
## 🏷️ Version History
### v1.0.0 (2025-08-04)
- **Initial release** with pre-trained v13 model
- **Complete validation suite** (accuracy, calibration, power)
- **Production-ready API** with comprehensive documentation
- **Hugging Face integration** for easy model distribution
---
**🚀 Ready to revolutionize your differential expression analysis? Install NB-Transformer today!**
```bash
pip install nb-transformer
```
For questions, issues, or contributions, visit our [Hugging Face repository](https://huggingface.co/valsv/nb-transformer) or open an issue.
|
twodigit/rt2-128-01
|
twodigit
| 2025-08-07T01:34:59Z | 2 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"roberta",
"sentence-similarity",
"feature-extraction",
"dense",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-08-07T01:33:29Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False, 'architecture': 'RobertaModel'})
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': False})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("twodigit/rt2-128-01")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.4212, 0.1739],
# [0.4212, 1.0000, 0.1690],
# [0.1739, 0.1690, 1.0000]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 5.0.0
- Transformers: 4.54.1
- PyTorch: 2.7.1+cu126
- Accelerate: 1.9.0
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citation
### BibTeX
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
haihp02/8384efad-df2f-47b5-bcd4-141c3ced8921
|
haihp02
| 2025-08-07T01:34:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:lmsys/vicuna-7b-v1.3",
"base_model:finetune:lmsys/vicuna-7b-v1.3",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T00:26:08Z |
---
base_model: lmsys/vicuna-7b-v1.3
library_name: transformers
model_name: 8384efad-df2f-47b5-bcd4-141c3ced8921
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for 8384efad-df2f-47b5-bcd4-141c3ced8921
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.3](https://huggingface.co/lmsys/vicuna-7b-v1.3).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="haihp02/8384efad-df2f-47b5-bcd4-141c3ced8921", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/trunghainguyenhp02/sn56-dpo-train/runs/9jxqz75g)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
MatheusOliveiraSitio/olivia_teste
|
MatheusOliveiraSitio
| 2025-08-07T01:34:35Z | 276 | 0 |
peft
|
[
"peft",
"safetensors",
"gguf",
"llama",
"base_model:adapter:unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"text-generation",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-07-25T03:39:13Z |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.16.0
|
myfi/parser_model_ner_3.42_checkpoint_200_lora
|
myfi
| 2025-08-07T01:34:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"en",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T01:24:57Z |
---
base_model: unsloth/Qwen2.5-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** myfi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
stewy33/10type_2ideas_augmented_original_subtle_roman_concrete-135be560
|
stewy33
| 2025-08-07T01:34:23Z | 3 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-08-07T01:32:16Z |
---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
sunxysun/ppo-SnowballTarget
|
sunxysun
| 2025-08-07T01:33:17Z | 7 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2025-08-07T01:33:12Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: sunxysun/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Fyuhei1989/minicompe-demo-20250807
|
Fyuhei1989
| 2025-08-07T01:30:55Z | 20 | 0 |
transformers
|
[
"transformers",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-4B",
"base_model:finetune:unsloth/Qwen3-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T01:30:54Z |
---
base_model: unsloth/Qwen3-4B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Fyuhei1989
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
amberspirit3/MurderDrone
|
amberspirit3
| 2025-08-07T01:27:41Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-07T01:27:41Z |
---
license: apache-2.0
---
|
RayYoh/GaussianCross
|
RayYoh
| 2025-08-07T01:23:26Z | 0 | 1 |
pointcept
|
[
"pointcept",
"tensorboard",
"3d",
"gaussian-splatting",
"point-cloud",
"self-supervised-learning",
"representation-learning",
"other",
"arxiv:2508.02172",
"license:mit",
"region:us"
] |
other
| 2025-07-07T07:11:27Z |
---
license: mit
pipeline_tag: other
library_name: pointcept
tags:
- 3d
- gaussian-splatting
- point-cloud
- self-supervised-learning
- representation-learning
---
# GaussianCross: Cross-modal Self-supervised 3D Representation Learning via Gaussian Splatting
GaussianCross is a novel cross-modal self-supervised 3D representation learning architecture that integrates feed-forward 3D Gaussian Splatting (3DGS) techniques. It aims to generate informative and robust point representations for 3D scene understanding, demonstrating strong performance on tasks like semantic and instance segmentation.
<p align="center">
<a href="https://huggingface.co/papers/2508.02172"><img src='https://img.shields.io/badge/arXiv-2508.02172-b31b1b.svg' alt='arXiv link'></a>
<a href="https://rayyoh.github.io/GaussianCross/"><img src='https://img.shields.io/badge/Project-Page-Green' alt='Project Page link'></a>
<a href="https://github.com/RayYoh/GaussianCross"><img src="https://img.shields.io/badge/GitHub-Code-blue?logo=github&" alt='GitHub Code link'></a>
</p>
<div align="center">
<img src="https://huggingface.co/RayYoh/GaussianCross/resolve/main/assets/teaser.png" width="80%" alt="GaussianCross Teaser"/>
</div>
## Abstract
The significance of informative and robust point representations has been widely acknowledged for 3D scene understanding. Despite existing self-supervised pre-training counterparts demonstrating promising performance, the model collapse and structural information deficiency remain prevalent due to insufficient point discrimination difficulty, yielding unreliable expressions and suboptimal performance. In this paper, we present GaussianCross, a novel cross-modal self-supervised 3D representation learning architecture integrating feed-forward 3D Gaussian Splatting (3DGS) techniques to address current challenges. GaussianCross seamlessly converts scale-inconsistent 3D point clouds into a unified cuboid-normalized Gaussian representation without missing details, enabling stable and generalizable pre-training. Subsequently, a tri-attribute adaptive distillation splatting module is incorporated to construct a 3D feature field, facilitating synergetic feature capturing of appearance, geometry, and semantic cues to maintain cross-modal consistency. To validate GaussianCross, we perform extensive evaluations on various benchmarks, including ScanNet, ScanNet200, and S3DIS. In particular, GaussianCross shows a prominent parameter and data efficiency, achieving superior performance through linear probing (<0.1% parameters) and limited data training (1% of scenes) compared to state-of-the-art methods. Furthermore, GaussianCross demonstrates strong generalization capabilities, improving the full fine-tuning accuracy by 9.3% mIoU and 6.1% AP$_{50}$ on ScanNet200 semantic and instance segmentation tasks, respectively, supporting the effectiveness of our approach.
## Pipeline
<div align="center">
<img src="https://huggingface.co/RayYoh/GaussianCross/resolve/main/assets/pepeline.png" width="100%" alt="GaussianCross Pipeline"/>
</div>
## Installation
Our model is built on the [Pointcept toolkit](https://github.com/Pointcept/Pointcept). You can follow its official instructions to install the packages:
```bash
conda create -n GaussianCross python=3.8 -y
conda activate GaussianCross
# Further installation steps can be found in the Pointcept documentation or the GaussianCross GitHub repository.
# Example from Pointcept's README:
# pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --index-url https://download.pytorch.org/whl/cu118
# pip install -r requirements.txt
# python setup.py develop
```
Note that they also provide scripts to build corresponding docker image: [build_image.sh](https://github.com/Pointcept/Pointcept/blob/main/scripts/build_image.sh)
## Data Preprocessing
**ScanNet V2 & ScanNet200**
- Download the [ScanNet V2](http://www.scan-net.org/) dataset.
- Run preprocessing code for raw ScanNet as follows (detailed scripts are in the [GitHub repository](https://github.com/RayYoh/GaussianCross)):
```bash
# xxx (Refer to GitHub for specific commands, e.g., python tools/prepare_scannet.py)
```
- Link processed dataset to codebase:
```bash
# PROCESSED_SCANNET_DIR: the directory of the processed ScanNet dataset.
mkdir data
ln -s ${PROCESSED_SCANNET_DIR} ${CODEBASE_DIR}/data/scannet
```
**S3DIS**
We use the preprocessed S3DIS data from [Pointcept](https://github.com/Pointcept/Pointcept?tab=readme-ov-file#s3dis).
- Link processed dataset to codebase:
```bash
# PROCESSED_S3DIS_DIR: the directory of the processed S3DIS dataset.
ln -s ${PROCESSED_S3DIS_DIR} ${CODEBASE_DIR}/data/s3dis
```
## Usage (Training with Pretrained Weights)
The training process is based on configs in the `configs` folder of the GitHub repository. The training scripts will create an experiment folder in `exp` and backup essential code in the experiment folder. Training config, log file, tensorboard, and checkpoints will also be saved during the training process.
**Attention:** Note that a critical difference from Pointcept is that most of data augmentation operations are conducted on GPU in this [file](https://github.com/RayYoh/GaussianCross/blob/main/pointcept/custom/transform_tensor.py). Make sure `ToTensor` is before the augmentation operations.
Download the pretrained 3D backbone from [this Hugging Face repository](https://huggingface.co/RayYoh/GaussianCross/blob/main/pretrain-gs-v4-spunet-base/model/model_last.pth).
**ScanNet V2 Examples**
```bash
# Load the pretrained model
WEIGHT="path/to/downloaded/model/model_last.pth"
# Linear Probing
CUDA_VISIBLE_DEVICES=0,1,2,3 sh scripts/train.sh -g 4 -d scannet -c semseg-spunet-base-lin -n semseg-spunet-base-lin -w $WEIGHT
# Semantic Segmentation
CUDA_VISIBLE_DEVICES=0,1,2,3 sh scripts/train.sh -g 4 -d scannet -c semseg-spunet-base -n semseg-spunet-base -w $WEIGHT
# Instance Segmentation
CUDA_VISIBLE_DEVICES=0,1,2,3 sh scripts/train.sh -g 4 -d scannet -c insseg-pg-spunet-base -n insseg-pg-spunet-base -w $WEIGHT
# Parameter Efficiency and Data Efficiency
CUDA_VISIBLE_DEVICES=0,1,2,3 sh scripts/train.sh -g 4 -d scannet -c semseg-spunet-efficient-[la20-lr20] -n semseg-spunet-efficient-[la20-lr20] -w $WEIGHT
```
For more detailed training scripts and configurations for ScanNet200 and S3DIS, please refer to the [official GitHub repository](https://github.com/RayYoh/GaussianCross).
## Acknowledgement
The research work was conducted in the JC STEM Lab of Machine Learning and Computer Vision funded by The Hong Kong Jockey Club Charities Trust.
Our code is primarily built upon [Pointcept](https://github.com/Pointcept/Pointcept), [Ponder V2](https://github.com/OpenGVLab/PonderV2) and [gsplat](https://github.com/nerfstudio-project/gsplat).
## Citation
If you find our work helpful or inspiring, please feel free to cite it.
```bib
@article{yao2025gaussiancross,
title={GaussianCross: Cross-modal Self-supervised 3D Representation Learning via Gaussian Splatting},
author={Yao, Lei and Wang, Yi and Zhang, Yi and Liu, Moyun and Chau, Lap-Pui},
journal={arXiv preprint arXiv:2508.02172},
year={2025}
}
or
@inproceedings{yao2025gaussiancross,
title={GaussianCross: Cross-modal Self-supervised 3D Representation Learning via Gaussian Splatting},
author={Yao, Lei and Wang, Yi and Zhang, Yi and Liu, Moyun and Chau, Lap-Pui},
booktitle={Proceedings of the 33nd ACM International Conference on Multimedia},
year={2025}
}
```
|
luke-kr/llama3.1-8b-3kingdoms-instruct
|
luke-kr
| 2025-08-07T01:23:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T01:21:58Z |
---
base_model: unsloth/Meta-Llama-3.1-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** luke-kr
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
rbelanec/train_openbookqa_1754507500
|
rbelanec
| 2025-08-07T01:21:05Z | 19 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-08-07T00:41:42Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: train_openbookqa_1754507500
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_openbookqa_1754507500
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the openbookqa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1619
- Num Input Tokens Seen: 4204168
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 123
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|
| 0.1781 | 0.5 | 558 | 0.2757 | 210048 |
| 0.1268 | 1.0 | 1116 | 0.1619 | 420520 |
| 0.1931 | 1.5 | 1674 | 0.2260 | 630888 |
| 0.0602 | 2.0 | 2232 | 0.1949 | 841024 |
| 0.0003 | 2.5 | 2790 | 0.2673 | 1051168 |
| 0.0024 | 3.0 | 3348 | 0.2718 | 1261304 |
| 0.0 | 3.5 | 3906 | 0.4003 | 1472152 |
| 0.0001 | 4.0 | 4464 | 0.3326 | 1682016 |
| 0.0804 | 4.5 | 5022 | 0.2713 | 1892160 |
| 0.0 | 5.0 | 5580 | 0.2744 | 2102920 |
| 0.0 | 5.5 | 6138 | 0.3787 | 2311976 |
| 0.0005 | 6.0 | 6696 | 0.3015 | 2523672 |
| 0.0 | 6.5 | 7254 | 0.3745 | 2732440 |
| 0.0 | 7.0 | 7812 | 0.3472 | 2943688 |
| 0.0 | 7.5 | 8370 | 0.3506 | 3153640 |
| 0.0 | 8.0 | 8928 | 0.3728 | 3363864 |
| 0.0 | 8.5 | 9486 | 0.3764 | 3574616 |
| 0.0 | 9.0 | 10044 | 0.3847 | 3783840 |
| 0.0 | 9.5 | 10602 | 0.3884 | 3994976 |
| 0.0 | 10.0 | 11160 | 0.3919 | 4204168 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
DreadPoor/Fear_Of_Ridicule-12B-Model_Stock-Q6_K-GGUF
|
DreadPoor
| 2025-08-07T01:17:31Z | 241 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"llama-cpp",
"gguf-my-repo",
"base_model:DreadPoor/Fear_Of_Ridicule-12B-Model_Stock",
"base_model:quantized:DreadPoor/Fear_Of_Ridicule-12B-Model_Stock",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-07T01:16:01Z |
---
base_model: DreadPoor/Fear_Of_Ridicule-12B-Model_Stock
library_name: transformers
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- llama-cpp
- gguf-my-repo
---
# DreadPoor/Fear_Of_Ridicule-12B-Model_Stock-Q6_K-GGUF
This model was converted to GGUF format from [`DreadPoor/Fear_Of_Ridicule-12B-Model_Stock`](https://huggingface.co/DreadPoor/Fear_Of_Ridicule-12B-Model_Stock) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/DreadPoor/Fear_Of_Ridicule-12B-Model_Stock) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo DreadPoor/Fear_Of_Ridicule-12B-Model_Stock-Q6_K-GGUF --hf-file fear_of_ridicule-12b-model_stock-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo DreadPoor/Fear_Of_Ridicule-12B-Model_Stock-Q6_K-GGUF --hf-file fear_of_ridicule-12b-model_stock-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo DreadPoor/Fear_Of_Ridicule-12B-Model_Stock-Q6_K-GGUF --hf-file fear_of_ridicule-12b-model_stock-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo DreadPoor/Fear_Of_Ridicule-12B-Model_Stock-Q6_K-GGUF --hf-file fear_of_ridicule-12b-model_stock-q6_k.gguf -c 2048
```
|
luca115/4xRealESRGAN
|
luca115
| 2025-08-07T01:14:12Z | 0 | 0 | null |
[
"safetensors",
"pytorch",
"super-resolution",
"image-to-image",
"license:bsd-3-clause",
"region:us"
] |
image-to-image
| 2025-08-06T19:11:20Z |
---
license: bsd-3-clause
pipeline_tag: image-to-image
tags:
- pytorch
- super-resolution
---
|
abcorrea/p1-v1-rep2
|
abcorrea
| 2025-08-07T01:13:36Z | 217 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-07T00:33:39Z |
---
base_model: Qwen/Qwen3-4B
library_name: transformers
model_name: p1-v1-rep2
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for p1-v1-rep2
This model is a fine-tuned version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="abcorrea/p1-v1-rep2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.52.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
rbelanec/train_openbookqa_1754507501
|
rbelanec
| 2025-08-07T01:13:20Z | 21 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"lntuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-08-07T00:44:29Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- lntuning
- generated_from_trainer
model-index:
- name: train_openbookqa_1754507501
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_openbookqa_1754507501
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the openbookqa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2698
- Num Input Tokens Seen: 4204168
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 123
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|
| 0.1609 | 0.5 | 558 | 0.3989 | 210048 |
| 0.1553 | 1.0 | 1116 | 0.3019 | 420520 |
| 0.1877 | 1.5 | 1674 | 0.2930 | 630888 |
| 0.1598 | 2.0 | 2232 | 0.2751 | 841024 |
| 0.1687 | 2.5 | 2790 | 0.2808 | 1051168 |
| 0.2138 | 3.0 | 3348 | 0.2698 | 1261304 |
| 0.035 | 3.5 | 3906 | 0.2841 | 1472152 |
| 0.0211 | 4.0 | 4464 | 0.2730 | 1682016 |
| 0.1641 | 4.5 | 5022 | 0.2859 | 1892160 |
| 0.1816 | 5.0 | 5580 | 0.2932 | 2102920 |
| 0.1433 | 5.5 | 6138 | 0.3073 | 2311976 |
| 0.3356 | 6.0 | 6696 | 0.3024 | 2523672 |
| 0.3081 | 6.5 | 7254 | 0.3156 | 2732440 |
| 0.0016 | 7.0 | 7812 | 0.3136 | 2943688 |
| 0.2086 | 7.5 | 8370 | 0.3169 | 3153640 |
| 0.2407 | 8.0 | 8928 | 0.3258 | 3363864 |
| 0.2006 | 8.5 | 9486 | 0.3297 | 3574616 |
| 0.0247 | 9.0 | 10044 | 0.3266 | 3783840 |
| 0.0091 | 9.5 | 10602 | 0.3295 | 3994976 |
| 0.8494 | 10.0 | 11160 | 0.3303 | 4204168 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
uname0x96/blockassist-bc-rough_scavenging_narwhal_1754528944
|
uname0x96
| 2025-08-07T01:11:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rough scavenging narwhal",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-07T01:11:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rough scavenging narwhal
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tensorblock/fdtn-ai_Foundation-Sec-8B-Instruct-GGUF
|
tensorblock
| 2025-08-07T01:05:57Z | 1,627 | 0 |
transformers
|
[
"transformers",
"gguf",
"security",
"llama",
"TensorBlock",
"GGUF",
"text-generation",
"en",
"base_model:fdtn-ai/Foundation-Sec-8B-Instruct",
"base_model:quantized:fdtn-ai/Foundation-Sec-8B-Instruct",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-06T23:38:58Z |
---
base_model: fdtn-ai/Foundation-Sec-8B-Instruct
language:
- en
library_name: transformers
license: other
pipeline_tag: text-generation
tags:
- security
- llama
- TensorBlock
- GGUF
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
[](https://tensorblock.co)
[](https://twitter.com/tensorblock_aoi)
[](https://discord.gg/Ej5NmeHFf2)
[](https://github.com/TensorBlock)
[](https://t.me/TensorBlock)
## fdtn-ai/Foundation-Sec-8B-Instruct - GGUF
<div style="text-align: left; margin: 20px 0;">
<a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Join our Discord to learn more about what we're building ↗
</a>
</div>
This repo contains GGUF format model files for [fdtn-ai/Foundation-Sec-8B-Instruct](https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Instruct).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).
## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
<th colspan="2" style="font-size: 25px;">Forge</th>
</tr>
<tr>
<th colspan="2">
<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
</th>
</tr>
<tr>
<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
</tr>
<tr>
<th colspan="2">
<a href="https://github.com/TensorBlock/forge" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">🚀 Try it now! 🚀</a>
</th>
</tr>
<tr>
<th style="font-size: 25px;">Awesome MCP Servers</th>
<th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
<tr>
<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
</tr>
<tr>
<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
</tr>
<tr>
<th>
<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
<th>
<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
</tr>
</table>
## Prompt template
```
<|begin_of_text|><|system|>
{system_prompt}
<|user|>
{prompt}
<|assistant|>
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Foundation-Sec-8B-Instruct-Q2_K.gguf](https://huggingface.co/tensorblock/fdtn-ai_Foundation-Sec-8B-Instruct-GGUF/blob/main/Foundation-Sec-8B-Instruct-Q2_K.gguf) | Q2_K | 3.180 GB | smallest, significant quality loss - not recommended for most purposes |
| [Foundation-Sec-8B-Instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/fdtn-ai_Foundation-Sec-8B-Instruct-GGUF/blob/main/Foundation-Sec-8B-Instruct-Q3_K_S.gguf) | Q3_K_S | 3.665 GB | very small, high quality loss |
| [Foundation-Sec-8B-Instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/fdtn-ai_Foundation-Sec-8B-Instruct-GGUF/blob/main/Foundation-Sec-8B-Instruct-Q3_K_M.gguf) | Q3_K_M | 4.020 GB | very small, high quality loss |
| [Foundation-Sec-8B-Instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/fdtn-ai_Foundation-Sec-8B-Instruct-GGUF/blob/main/Foundation-Sec-8B-Instruct-Q3_K_L.gguf) | Q3_K_L | 4.323 GB | small, substantial quality loss |
| [Foundation-Sec-8B-Instruct-Q4_0.gguf](https://huggingface.co/tensorblock/fdtn-ai_Foundation-Sec-8B-Instruct-GGUF/blob/main/Foundation-Sec-8B-Instruct-Q4_0.gguf) | Q4_0 | 4.662 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Foundation-Sec-8B-Instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/fdtn-ai_Foundation-Sec-8B-Instruct-GGUF/blob/main/Foundation-Sec-8B-Instruct-Q4_K_S.gguf) | Q4_K_S | 4.693 GB | small, greater quality loss |
| [Foundation-Sec-8B-Instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/fdtn-ai_Foundation-Sec-8B-Instruct-GGUF/blob/main/Foundation-Sec-8B-Instruct-Q4_K_M.gguf) | Q4_K_M | 4.921 GB | medium, balanced quality - recommended |
| [Foundation-Sec-8B-Instruct-Q5_0.gguf](https://huggingface.co/tensorblock/fdtn-ai_Foundation-Sec-8B-Instruct-GGUF/blob/main/Foundation-Sec-8B-Instruct-Q5_0.gguf) | Q5_0 | 5.600 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Foundation-Sec-8B-Instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/fdtn-ai_Foundation-Sec-8B-Instruct-GGUF/blob/main/Foundation-Sec-8B-Instruct-Q5_K_S.gguf) | Q5_K_S | 5.600 GB | large, low quality loss - recommended |
| [Foundation-Sec-8B-Instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/fdtn-ai_Foundation-Sec-8B-Instruct-GGUF/blob/main/Foundation-Sec-8B-Instruct-Q5_K_M.gguf) | Q5_K_M | 5.734 GB | large, very low quality loss - recommended |
| [Foundation-Sec-8B-Instruct-Q6_K.gguf](https://huggingface.co/tensorblock/fdtn-ai_Foundation-Sec-8B-Instruct-GGUF/blob/main/Foundation-Sec-8B-Instruct-Q6_K.gguf) | Q6_K | 6.597 GB | very large, extremely low quality loss |
| [Foundation-Sec-8B-Instruct-Q8_0.gguf](https://huggingface.co/tensorblock/fdtn-ai_Foundation-Sec-8B-Instruct-GGUF/blob/main/Foundation-Sec-8B-Instruct-Q8_0.gguf) | Q8_0 | 8.542 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/fdtn-ai_Foundation-Sec-8B-Instruct-GGUF --include "Foundation-Sec-8B-Instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/fdtn-ai_Foundation-Sec-8B-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|
apriasmoro/ca0c0bab-97be-4503-b1a0-e6d9986d3595
|
apriasmoro
| 2025-08-07T01:05:10Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-07T01:02:35Z |
---
library_name: transformers
model_name: app/checkpoints/bdb96dd3-6cb9-4357-8adc-3b58fc15f35d/ca0c0bab-97be-4503-b1a0-e6d9986d3595
tags:
- generated_from_trainer
- dpo
- trl
licence: license
---
# Model Card for app/checkpoints/bdb96dd3-6cb9-4357-8adc-3b58fc15f35d/ca0c0bab-97be-4503-b1a0-e6d9986d3595
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.20.0
- Transformers: 4.55.0
- Pytorch: 2.7.1+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ACECA/lowMvM_218
|
ACECA
| 2025-08-07T01:00:19Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-07-30T15:11:01Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
ACECA/lowMvM_217
|
ACECA
| 2025-08-07T00:59:43Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-07-30T15:11:01Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
echoboi/veganism_and_vegetarianism-distilbert-classifier
|
echoboi
| 2025-08-07T00:57:49Z | 14 | 0 | null |
[
"text-classification",
"multilabel-classification",
"food",
"climate-change",
"sustainability",
"veganism-&-vegetarianism",
"en",
"license:mit",
"region:us"
] |
text-classification
| 2025-08-05T18:40:42Z |
---
language: en
tags:
- text-classification
- multilabel-classification
- food
- climate-change
- sustainability
- veganism-&-vegetarianism
license: mit
---
# Veganism & Vegetarianism Classifier (Distilbert)
This model classifies content related to veganism and vegetarianism on climate change subreddits.
## Model Details
- Model Type: Distilbert
- Task: Multilabel text classification
- Sector: Veganism & Vegetarianism
- Base Model: Distilbert base uncased
- Labels: 7
- Training Data: Sample from 1000 GPT 4o-mini-labeled Reddit posts from climate subreddits (2010-2023)
## Labels
The model predicts 7 labels simultaneously:
1. **Animal Welfare**: Cites animal suffering, cruelty, or ethics as motivation.
2. **Environmental Impact**: Links diet choice to climate change, land, water, or emissions.
3. **Health**: Claims physical health benefits or risks of eating less meat / going vegan.
4. **Lab Grown And Alt Proteins**: References cultivated meat, precision fermentation, insect protein or plant-based substitutes.
5. **Psychology And Identity**: Diet as part of personal identity, moral virtue signalling or tribal politics.
6. **Systemic Vs Individual Action**: Calls for policy, corporate reform or large-scale funding instead of just personal diet shifts.
7. **Taste And Convenience**: Talks about flavour, texture, cooking ease, availability of vegan options, or social convenience.
Note: Label order in predictions matches the order above.
## Usage
```python
import torch, sys, os, tempfile
from transformers import DistilBertTokenizer
from huggingface_hub import snapshot_download
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def print_sorted_label_scores(label_scores):
# Sort label_scores dict by score descending
sorted_items = sorted(label_scores.items(), key=lambda x: x[1], reverse=True)
for label, score in sorted_items:
print(f" {label}: {score:.6f}")
# Model link and examples for this specific model
model_link = 'sanchow/veganism_and_vegetarianism-distilbert-classifier'
examples = [
"Plant-based diets have a much lower carbon footprint than meat-heavy diets."
]
print(f"\n{'='*60}")
print("MODEL: VEGANISM & VEGETARIANISM SECTOR")
print(f"{'='*60}")
print(f"Downloading model: {model_link}")
with tempfile.TemporaryDirectory() as temp_dir:
snapshot_download(
repo_id=model_link,
local_dir=temp_dir,
local_dir_use_symlinks=False
)
model_class_path = os.path.join(temp_dir, 'model_class.py')
if not os.path.exists(model_class_path):
print(f"model_class.py not found in downloaded files")
print(f" Available files: {os.listdir(temp_dir)}")
else:
sys.path.insert(0, temp_dir)
from model_class import MultilabelClassifier
tokenizer = DistilBertTokenizer.from_pretrained(temp_dir)
checkpoint = torch.load(os.path.join(temp_dir, 'model.pt'), map_location='cpu', weights_only=False)
model = MultilabelClassifier(checkpoint['model_name'], len(checkpoint['label_names']))
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
print("Model loaded successfully")
print(f" Labels: {checkpoint['label_names']}")
print("\nVeganism & Vegetarianism classifier results:\n")
for i, test_text in enumerate(examples):
inputs = tokenizer(
test_text,
return_tensors="pt",
truncation=True,
max_length=512,
padding=True
).to(device)
with torch.no_grad():
outputs = model(**inputs)
predictions = outputs.cpu().numpy() if isinstance(outputs, (tuple, list)) else outputs.cpu().numpy()
label_scores = {label: float(score) for label, score in zip(checkpoint['label_names'], predictions[0])}
print(f"Example {i+1}: '{test_text}'")
print("Predictions (all label scores, highest first):")
print_sorted_label_scores(label_scores)
print("-" * 40)
```
## Performance
Best model performance:
- Micro Jaccard: 0.5584
- Macro Jaccard: 0.6710
- F1 Score: 0.8906
- Accuracy: 0.8906
Dataset: ~900 GPT-labeled samples per sector (600 train, 150 validation, 150 test)
## Optimal Thresholds
```python
optimal_thresholds = {'Animal Welfare': 0.48107979620047003, 'Environmental Impact': 0.45919171852850427, 'Health': 0.20115313966833437, 'Lab Grown And Alt Proteins': 0.3414601502146817, 'Psychology And Identity': 0.5246278637433214, 'Systemic Vs Individual Action': 0.37517437676211585, 'Taste And Convenience': 0.6635140143644325}
for label, score in zip(label_names, predictions[0]):
threshold = optimal_thresholds.get(label, 0.5)
if score > threshold:
print(f"{label}: {score:.3f}")
```
## Training
Trained on GPT-labeled Reddit data:
1. Data collection from climate subreddits
2. keyword based filtering for sector-specific content
3. GPT labeling for multilabel classification
4. 80/10/10 train/validation/test split
5. Fine-tuning with threshold optimization
## Citation
If you use this model in your research, please cite:
```bibtex
@misc{veganism_and_vegetarianism_distilbert_classifier,
title={Veganism & Vegetarianism Classifier for Climate Change Analysis},
author={Sandeep Chowdhary},
year={2025},
publisher={Hugging Face},
journal={Hugging Face Hub},
howpublished={\url{https://huggingface.co/echoboi/veganism_and_vegetarianism-distilbert-classifier}},
}
```
## Limitations
- Trained on data from specific climate change subreddits and limited to English content
- Performance depends on GPT-generated labels
|
echoboi/electric_vehicles-distilbert-classifier
|
echoboi
| 2025-08-07T00:57:13Z | 26 | 0 | null |
[
"text-classification",
"multilabel-classification",
"transport",
"climate-change",
"sustainability",
"electric-vehicles",
"en",
"license:mit",
"region:us"
] |
text-classification
| 2025-08-05T18:42:12Z |
---
language: en
tags:
- text-classification
- multilabel-classification
- transport
- climate-change
- sustainability
- electric-vehicles
license: mit
---
# Electric Vehicles Classifier (Distilbert)
This model classifies content related to electric vehicles on climate change subreddits.
## Model Details
- Model Type: Distilbert
- Task: Multilabel text classification
- Sector: Electric Vehicles
- Base Model: Distilbert base uncased
- Labels: 7
- Training Data: Sample from 1000 GPT 4o-mini-labeled Reddit posts from climate subreddits (2010-2023)
## Labels
The model predicts 7 labels simultaneously:
1. **Alternative Modes**: Advocates bikes, transit, e-scooters, trains instead of private EVs.
2. **Charging Infrastructure**: Talks about availability, speed, or reliability of public or home chargers.
3. **Environmental Benefit**: Claims EVs reduce emissions or pollution vs. gasoline cars.
4. **Grid Impact And Energy Mix**: Links EV charging to grid capacity, blackout fears, renewable share of electricity.
5. **Mineral Supply Chain**: Concerns over lithium, cobalt, nickel, rare-earth mining or shortages for batteries.
6. **Policy And Mandates**: References government regulations, bans on ICE sales, fleet targets or central-planning critiques.
7. **Purchase Price**: Discusses up-front sticker price, MSRP, subsidies, or tax credits for buying an EV.
Note: Label order in predictions matches the order above.
## Usage
```python
import torch, sys, os, tempfile
from transformers import DistilBertTokenizer
from huggingface_hub import snapshot_download
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def print_sorted_label_scores(label_scores):
# Sort label_scores dict by score descending
sorted_items = sorted(label_scores.items(), key=lambda x: x[1], reverse=True)
for label, score in sorted_items:
print(f" {label}: {score:.6f}")
# Model link and examples for this specific model
model_link = 'sanchow/electric_vehicles-distilbert-classifier'
examples = [
"Switching to electric cars can cut down on smog and carbon output."
]
print(f"\n{'='*60}")
print("MODEL: ELECTRIC VEHICLES SECTOR")
print(f"{'='*60}")
print(f"Downloading model: {model_link}")
with tempfile.TemporaryDirectory() as temp_dir:
snapshot_download(
repo_id=model_link,
local_dir=temp_dir,
local_dir_use_symlinks=False
)
model_class_path = os.path.join(temp_dir, 'model_class.py')
if not os.path.exists(model_class_path):
print(f"model_class.py not found in downloaded files")
print(f" Available files: {os.listdir(temp_dir)}")
else:
sys.path.insert(0, temp_dir)
from model_class import MultilabelClassifier
tokenizer = DistilBertTokenizer.from_pretrained(temp_dir)
checkpoint = torch.load(os.path.join(temp_dir, 'model.pt'), map_location='cpu', weights_only=False)
model = MultilabelClassifier(checkpoint['model_name'], len(checkpoint['label_names']))
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()
print("Model loaded successfully")
print(f" Labels: {checkpoint['label_names']}")
print("\nElectric Vehicles classifier results:\n")
for i, test_text in enumerate(examples):
inputs = tokenizer(
test_text,
return_tensors="pt",
truncation=True,
max_length=512,
padding=True
).to(device)
with torch.no_grad():
outputs = model(**inputs)
predictions = outputs.cpu().numpy() if isinstance(outputs, (tuple, list)) else outputs.cpu().numpy()
label_scores = {label: float(score) for label, score in zip(checkpoint['label_names'], predictions[0])}
print(f"Example {i+1}: '{test_text}'")
print("Predictions (all label scores, highest first):")
print_sorted_label_scores(label_scores)
print("-" * 40)
```
## Performance
Best model performance:
- Micro Jaccard: 0.4596
- Macro Jaccard: 0.5701
- F1 Score: 0.8772
- Accuracy: 0.8772
Dataset: ~900 GPT-labeled samples per sector (600 train, 150 validation, 150 test)
## Optimal Thresholds
```python
optimal_thresholds = {'Alternative Modes': 0.28427787391225384, 'Charging Infrastructure': 0.3619448731592626, 'Environmental Benefit': 0.4029443119613918, 'Grid Impact And Energy Mix': 0.29907076386497516, 'Mineral Supply Chain': 0.2987419331439881, 'Policy And Mandates': 0.36899998622725905, 'Purchase Price': 0.3463644004166977}
for label, score in zip(label_names, predictions[0]):
threshold = optimal_thresholds.get(label, 0.5)
if score > threshold:
print(f"{label}: {score:.3f}")
```
## Training
Trained on GPT-labeled Reddit data:
1. Data collection from climate subreddits
2. keyword based filtering for sector-specific content
3. GPT labeling for multilabel classification
4. 80/10/10 train/validation/test split
5. Fine-tuning with threshold optimization
## Citation
If you use this model in your research, please cite:
```bibtex
@misc{electric_vehicles_distilbert_classifier,
title={Electric Vehicles Classifier for Climate Change Analysis},
author={Sandeep Chowdhary},
year={2025},
publisher={Hugging Face},
journal={Hugging Face Hub},
howpublished={\url{https://huggingface.co/echoboi/electric_vehicles-distilbert-classifier}},
}
```
## Limitations
- Trained on data from specific climate change subreddits and limited to English content
- Performance depends on GPT-generated labels
|
OscarGD6/qwen2-vl-audio-prompt-coco
|
OscarGD6
| 2025-08-07T00:54:31Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-07T00:54:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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|
wjbmattingly/old-church-slavonic-dots-ocr
|
wjbmattingly
| 2025-08-07T00:54:14Z | 56 | 0 |
dots_ocr
|
[
"dots_ocr",
"safetensors",
"image-to-text",
"ocr",
"document-parse",
"layout",
"table",
"formula",
"image-text-to-text",
"conversational",
"custom_code",
"en",
"zh",
"multilingual",
"base_model:rednote-hilab/dots.ocr",
"base_model:finetune:rednote-hilab/dots.ocr",
"license:mit",
"region:us"
] |
image-text-to-text
| 2025-08-06T02:00:29Z |
---
license: mit
library_name: dots_ocr
pipeline_tag: image-text-to-text
tags:
- image-to-text
- ocr
- document-parse
- layout
- table
- formula
language:
- en
- zh
- multilingual
base_model:
- rednote-hilab/dots.ocr
---
<div align="center">
<p align="center">
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/logo.png" width="300"/>
<p>
<h1 align="center">
dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model
</h1>
[](https://github.com/rednote-hilab/dots.ocr/blob/master/assets/blog.md)
[](https://huggingface.co/rednote-hilab/dots.ocr)
<div align="center">
<a href="https://dotsocr.xiaohongshu.com" target="_blank" rel="noopener noreferrer"><strong>🖥️ Live Demo</strong></a> |
<a href="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/wechat.jpg" target="_blank" rel="noopener noreferrer"><strong>💬 WeChat</strong></a> |
<a href="https://www.xiaohongshu.com/user/profile/683ffe42000000001d021a4c" target="_blank" rel="noopener noreferrer"><strong>📕 rednote</strong></a>
</div>
</div>
## Introduction
**dots.ocr** is a powerful, multilingual document parser that unifies layout detection and content recognition within a single vision-language model while maintaining good reading order. Despite its compact 1.7B-parameter LLM foundation, it achieves state-of-the-art(SOTA) performance.
1. **Powerful Performance:** **dots.ocr** achieves SOTA performance for text, tables, and reading order on [OmniDocBench](https://github.com/opendatalab/OmniDocBench), while delivering formula recognition results comparable to much larger models like Doubao-1.5 and gemini2.5-pro.
2. **Multilingual Support:** **dots.ocr** demonstrates robust parsing capabilities for low-resource languages, achieving decisive advantages across both layout detection and content recognition on our in-house multilingual documents benchmark.
3. **Unified and Simple Architecture:** By leveraging a single vision-language model, **dots.ocr** offers a significantly more streamlined architecture than conventional methods that rely on complex, multi-model pipelines. Switching between tasks is accomplished simply by altering the input prompt, proving that a VLM can achieve competitive detection results compared to traditional detection models like DocLayout-YOLO.
4. **Efficient and Fast Performance:** Built upon a compact 1.7B LLM, **dots.ocr** provides faster inference speeds than many other high-performing models based on larger foundations.
### Performance Comparison: dots.ocr vs. Competing Models
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/chart.png" border="0" />
> **Notes:**
> - The EN, ZH metrics are the end2end evaluation results of [OmniDocBench](https://github.com/opendatalab/OmniDocBench), and Multilingual metric is the end2end evaluation results of dots.ocr-bench.
## News
* ```2025.07.30 ``` 🚀 We release [dots.ocr](https://github.com/rednote-hilab/dots.ocr), — a multilingual documents parsing model based on 1.7b llm, with SOTA performance.
## Benchmark Results
### 1. OmniDocBench
#### The end-to-end evaluation results of different tasks.
<table>
<thead>
<tr>
<th rowspan="2"><strong>Model<br>Type</strong></th>
<th rowspan="2"><strong>Methods</strong></th>
<th colspan="2"><strong>Overall<sup>Edit</sup>↓</strong></th>
<th colspan="2"><strong>Text<sup>Edit</sup>↓</strong></th>
<th colspan="2"><strong>Formula<sup>Edit</sup>↓</strong></th>
<th colspan="2"><strong>Table<sup>TEDS</sup>↑</strong></th>
<th colspan="2"><strong>Table<sup>Edit</sup>↓</strong></th>
<th colspan="2"><strong>Read Order<sup>Edit</sup>↓</strong></th>
</tr>
<tr>
<th><em>EN</em></th>
<th><em>ZH</em></th>
<th><em>EN</em></th>
<th><em>ZH</em></th>
<th><em>EN</em></th>
<th><em>ZH</em></th>
<th><em>EN</em></th>
<th><em>ZH</em></th>
<th><em>EN</em></th>
<th><em>ZH</em></th>
<th><em>EN</em></th>
<th><em>ZH</em></th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="8"><strong>Pipeline<br>Tools</strong></td>
<td>MinerU</td>
<td>0.150</td>
<td>0.357</td>
<td>0.061</td>
<td>0.215</td>
<td>0.278</td>
<td>0.577</td>
<td>78.6</td>
<td>62.1</td>
<td>0.180</td>
<td>0.344</td>
<td>0.079</td>
<td>0.292</td>
</tr>
<tr>
<td>Marker</td>
<td>0.336</td>
<td>0.556</td>
<td>0.080</td>
<td>0.315</td>
<td>0.530</td>
<td>0.883</td>
<td>67.6</td>
<td>49.2</td>
<td>0.619</td>
<td>0.685</td>
<td>0.114</td>
<td>0.340</td>
</tr>
<tr>
<td>Mathpix</td>
<td>0.191</td>
<td>0.365</td>
<td>0.105</td>
<td>0.384</td>
<td>0.306</td>
<td>0.454</td>
<td>77.0</td>
<td>67.1</td>
<td>0.243</td>
<td>0.320</td>
<td>0.108</td>
<td>0.304</td>
</tr>
<tr>
<td>Docling</td>
<td>0.589</td>
<td>0.909</td>
<td>0.416</td>
<td>0.987</td>
<td>0.999</td>
<td>1</td>
<td>61.3</td>
<td>25.0</td>
<td>0.627</td>
<td>0.810</td>
<td>0.313</td>
<td>0.837</td>
</tr>
<tr>
<td>Pix2Text</td>
<td>0.320</td>
<td>0.528</td>
<td>0.138</td>
<td>0.356</td>
<td>0.276</td>
<td>0.611</td>
<td>73.6</td>
<td>66.2</td>
<td>0.584</td>
<td>0.645</td>
<td>0.281</td>
<td>0.499</td>
</tr>
<tr>
<td>Unstructured</td>
<td>0.586</td>
<td>0.716</td>
<td>0.198</td>
<td>0.481</td>
<td>0.999</td>
<td>1</td>
<td>0</td>
<td>0.06</td>
<td>1</td>
<td>0.998</td>
<td>0.145</td>
<td>0.387</td>
</tr>
<tr>
<td>OpenParse</td>
<td>0.646</td>
<td>0.814</td>
<td>0.681</td>
<td>0.974</td>
<td>0.996</td>
<td>1</td>
<td>64.8</td>
<td>27.5</td>
<td>0.284</td>
<td>0.639</td>
<td>0.595</td>
<td>0.641</td>
</tr>
<tr>
<td>PPStruct-V3</td>
<td>0.145</td>
<td>0.206</td>
<td>0.058</td>
<td>0.088</td>
<td>0.295</td>
<td>0.535</td>
<td>-</td>
<td>-</td>
<td>0.159</td>
<td>0.109</td>
<td>0.069</td>
<td>0.091</td>
</tr>
<tr>
<td rowspan="9"><strong>Expert<br>VLMs</strong></td>
<td>GOT-OCR</td>
<td>0.287</td>
<td>0.411</td>
<td>0.189</td>
<td>0.315</td>
<td>0.360</td>
<td>0.528</td>
<td>53.2</td>
<td>47.2</td>
<td>0.459</td>
<td>0.520</td>
<td>0.141</td>
<td>0.280</td>
</tr>
<tr>
<td>Nougat</td>
<td>0.452</td>
<td>0.973</td>
<td>0.365</td>
<td>0.998</td>
<td>0.488</td>
<td>0.941</td>
<td>39.9</td>
<td>0</td>
<td>0.572</td>
<td>1.000</td>
<td>0.382</td>
<td>0.954</td>
</tr>
<tr>
<td>Mistral OCR</td>
<td>0.268</td>
<td>0.439</td>
<td>0.072</td>
<td>0.325</td>
<td>0.318</td>
<td>0.495</td>
<td>75.8</td>
<td>63.6</td>
<td>0.600</td>
<td>0.650</td>
<td>0.083</td>
<td>0.284</td>
</tr>
<tr>
<td>OLMOCR-sglang</td>
<td>0.326</td>
<td>0.469</td>
<td>0.097</td>
<td>0.293</td>
<td>0.455</td>
<td>0.655</td>
<td>68.1</td>
<td>61.3</td>
<td>0.608</td>
<td>0.652</td>
<td>0.145</td>
<td>0.277</td>
</tr>
<tr>
<td>SmolDocling-256M</td>
<td>0.493</td>
<td>0.816</td>
<td>0.262</td>
<td>0.838</td>
<td>0.753</td>
<td>0.997</td>
<td>44.9</td>
<td>16.5</td>
<td>0.729</td>
<td>0.907</td>
<td>0.227</td>
<td>0.522</td>
</tr>
<tr>
<td>Dolphin</td>
<td>0.206</td>
<td>0.306</td>
<td>0.107</td>
<td>0.197</td>
<td>0.447</td>
<td>0.580</td>
<td>77.3</td>
<td>67.2</td>
<td>0.180</td>
<td>0.285</td>
<td>0.091</td>
<td>0.162</td>
</tr>
<tr>
<td>MinerU 2</td>
<td>0.139</td>
<td>0.240</td>
<td>0.047</td>
<td>0.109</td>
<td>0.297</td>
<td>0.536</td>
<td>82.5</td>
<td>79.0</td>
<td>0.141</td>
<td>0.195</td>
<td>0.069<</td>
<td>0.118</td>
</tr>
<tr>
<td>OCRFlux</td>
<td>0.195</td>
<td>0.281</td>
<td>0.064</td>
<td>0.183</td>
<td>0.379</td>
<td>0.613</td>
<td>71.6</td>
<td>81.3</td>
<td>0.253</td>
<td>0.139</td>
<td>0.086</td>
<td>0.187</td>
</tr>
<tr>
<td>MonkeyOCR-pro-3B</td>
<td>0.138</td>
<td>0.206</td>
<td>0.067</td>
<td>0.107</td>
<td><strong>0.246</strong></td>
<td>0.421</td>
<td>81.5</td>
<td>87.5</td>
<td>0.139</td>
<td>0.111</td>
<td>0.100</td>
<td>0.185</td>
</tr>
<tr>
<td rowspan="5"><strong>General<br>VLMs</strong></td>
<td>GPT4o</td>
<td>0.233</td>
<td>0.399</td>
<td>0.144</td>
<td>0.409</td>
<td>0.425</td>
<td>0.606</td>
<td>72.0</td>
<td>62.9</td>
<td>0.234</td>
<td>0.329</td>
<td>0.128</td>
<td>0.251</td>
</tr>
<tr>
<td>Qwen2-VL-72B</td>
<td>0.252</td>
<td>0.327</td>
<td>0.096</td>
<td>0.218</td>
<td>0.404</td>
<td>0.487</td>
<td>76.8</td>
<td>76.4</td>
<td>0.387</td>
<td>0.408</td>
<td>0.119</td>
<td>0.193</td>
</tr>
<tr>
<td>Qwen2.5-VL-72B</td>
<td>0.214</td>
<td>0.261</td>
<td>0.092</td>
<td>0.18</td>
<td>0.315</td>
<td>0.434</td>
<td>82.9</td>
<td>83.9</td>
<td>0.341</td>
<td>0.262</td>
<td>0.106</td>
<td>0.168</td>
</tr>
<tr>
<td>Gemini2.5-Pro</td>
<td>0.148</td>
<td>0.212</td>
<td>0.055</td>
<td>0.168</td>
<td>0.356</td>
<td>0.439</td>
<td>85.8</td>
<td>86.4</td>
<td>0.13</td>
<td>0.119</td>
<td>0.049</td>
<td>0.121</td>
</tr>
<tr>
<td>doubao-1-5-thinking-vision-pro-250428</td>
<td>0.140</td>
<td>0.162</td>
<td>0.043</td>
<td>0.085</td>
<td>0.295</td>
<td><strong>0.384</strong></td>
<td>83.3</td>
<td><strong>89.3</strong></td>
<td>0.165</td>
<td><strong>0.085</strong></td>
<td>0.058</td>
<td>0.094</td>
</tr>
<tr>
<td rowspan="1"><strong>Expert VLMs</strong></td>
<td><strong>dots.ocr</strong></td>
<td><strong>0.125</strong></td>
<td><strong>0.160</strong></td>
<td><strong>0.032</strong></td>
<td><strong>0.066</strong></td>
<td>0.329</td>
<td>0.416</td>
<td><strong>88.6</strong></td>
<td>89.0</td>
<td><strong>0.099</strong></td>
<td>0.092</td>
<td><strong>0.040</strong></td>
<td><strong>0.067</strong></td>
</tr>
<tr>
</tbody>
</table>
#### The end-to-end text recognition performance across 9 PDF page types.
<table>
<thead>
<tr>
<th><strong>Model<br>Type</strong></th>
<th><strong>Models</strong></th>
<th><strong>Book</strong></th>
<th><strong>Slides</strong></th>
<th><strong>Financial<br>Report</strong></th>
<th><strong>Textbook</strong></th>
<th><strong>Exam<br>Paper</strong></th>
<th><strong>Magazine</strong></th>
<th><strong>Academic<br>Papers</strong></th>
<th><strong>Notes</strong></th>
<th><strong>Newspaper</strong></th>
<th><strong>Overall</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3"><strong>Pipeline<br>Tools</strong></td>
<td>MinerU</td>
<td>0.055</td>
<td>0.124</td>
<td><u>0.033</u></td>
<td>0.102</td>
<td>0.159</td>
<td><strong>0.072</strong></td>
<td><u>0.025</u></td>
<td>0.984</td>
<td>0.171</td>
<td>0.206</td>
</tr>
<tr>
<td>Marker</td>
<td>0.074</td>
<td>0.340</td>
<td>0.089</td>
<td>0.319</td>
<td>0.452</td>
<td>0.153</td>
<td>0.059</td>
<td>0.651</td>
<td>0.192</td>
<td>0.274</td>
</tr>
<tr>
<td>Mathpix</td>
<td>0.131</td>
<td>0.220</td>
<td>0.202</td>
<td>0.216</td>
<td>0.278</td>
<td>0.147</td>
<td>0.091</td>
<td>0.634</td>
<td>0.690</td>
<td>0.300</td>
</tr>
<tr>
<td rowspan="5"><strong>Expert<br>VLMs</strong></td>
<td>GOT-OCR</td>
<td>0.111</td>
<td>0.222</td>
<td>0.067</td>
<td>0.132</td>
<td>0.204</td>
<td>0.198</td>
<td>0.179</td>
<td>0.388</td>
<td>0.771</td>
<td>0.267</td>
</tr>
<tr>
<td>Nougat</td>
<td>0.734</td>
<td>0.958</td>
<td>1.000</td>
<td>0.820</td>
<td>0.930</td>
<td>0.830</td>
<td>0.214</td>
<td>0.991</td>
<td>0.871</td>
<td>0.806</td>
</tr>
<tr>
<td>Dolphin</td>
<td>0.091</td>
<td>0.131</td>
<td>0.057</td>
<td>0.146</td>
<td>0.231</td>
<td>0.121</td>
<td>0.074</td>
<td>0.363</td>
<td>0.307</td>
<td>0.177</td>
</tr>
<tr>
<td>OCRFlux</td>
<td>0.068</td>
<td>0.125</td>
<td>0.092</td>
<td>0.102</td>
<td>0.119</td>
<td>0.083</td>
<td>0.047</td>
<td>0.223</td>
<td>0.536</td>
<td>0.149</td>
</tr>
<tr>
<td>MonkeyOCR-pro-3B</td>
<td>0.084</td>
<td>0.129</td>
<td>0.060</td>
<td>0.090</td>
<td>0.107</td>
<td>0.073</td>
<td>0.050</td>
<td>0.171</td>
<td>0.107</td>
<td>0.100</td>
</tr>
<tr>
<td rowspan="4"><strong>General<br>VLMs</strong></td>
<td>GPT4o</td>
<td>0.157</td>
<td>0.163</td>
<td>0.348</td>
<td>0.187</td>
<td>0.281</td>
<td>0.173</td>
<td>0.146</td>
<td>0.607</td>
<td>0.751</td>
<td>0.316</td>
</tr>
<tr>
<td>Qwen2.5-VL-7B</td>
<td>0.148</td>
<td>0.053</td>
<td>0.111</td>
<td>0.137</td>
<td>0.189</td>
<td>0.117</td>
<td>0.134</td>
<td>0.204</td>
<td>0.706</td>
<td>0.205</td>
</tr>
<tr>
<td>InternVL3-8B</td>
<td>0.163</td>
<td>0.056</td>
<td>0.107</td>
<td>0.109</td>
<td>0.129</td>
<td>0.100</td>
<td>0.159</td>
<td>0.150</td>
<td>0.681</td>
<td>0.188</td>
</tr>
<tr>
<td>doubao-1-5-thinking-vision-pro-250428</td>
<td>0.048</td>
<td>0.048</td>
<td>0.024</td>
<td><strong>0.062</strong></td>
<td>0.085</td>
<td>0.051</td>
<td>0.039</td>
<td><strong>0.096</strong></td>
<td>0.181</td>
<td>0.073</td>
</tr>
<tr>
<td rowspan="1"><strong>Expert VLMs</strong></td>
<td><strong>dots.ocr</strong></td>
<td><strong>0.031</strong></td>
<td><strong>0.047</strong></td>
<td><strong>0.011</strong></td>
<td>0.082</td>
<td><strong>0.079</strong></td>
<td><strong>0.028</strong></td>
<td><strong>0.029</strong></td>
<td>0.109</td>
<td><strong>0.056</strong></td>
<td><strong>0.055</strong></td>
</tr>
</tbody>
</table>
> **Notes:**
> - The metrics are from [MonkeyOCR](https://github.com/Yuliang-Liu/MonkeyOCR), [OmniDocBench](https://github.com/opendatalab/OmniDocBench), and our own internal evaluations.
> - We delete the Page-header and Page-footer cells in the result markdown.
> - We use tikz_preprocess pipeline to upsample the images to dpi 200.
### 2. **dots.ocr-bench**
This is an inhouse benchmark which contain 1493 pdf images with 100 languages.
#### The end-to-end evaluation results of different tasks.
<table>
<thead>
<tr>
<th rowspan="1"><strong>Methods</strong></th>
<th colspan="1"><strong>Overall<sup>Edit</sup>↓</strong></th>
<th colspan="1"><strong>Text<sup>Edit</sup>↓</strong></th>
<th colspan="1"><strong>Formula<sup>Edit</sup>↓</strong></th>
<th colspan="1"><strong>Table<sup>TEDS</sup>↑</strong></th>
<th colspan="1"><strong>Table<sup>Edit</sup>↓</strong></th>
<th colspan="1"><strong>Read Order<sup>Edit</sup>↓</strong></th>
</tr>
</thead>
<tbody>
<td>MonkeyOCR-3B</td>
<td>0.483</td>
<td>0.445</td>
<td>0.627</td>
<td>50.93</td>
<td>0.452</td>
<td>0.409</td>
</tr>
<tr>
<td>doubao-1-5-thinking-vision-pro-250428</td>
<td>0.291</td>
<td>0.226</td>
<td>0.440</td>
<td>71.2</td>
<td>0.260</td>
<td>0.238</td>
</tr>
<tr>
<td>doubao-1-6</td>
<td>0.299</td>
<td>0.270</td>
<td>0.417</td>
<td>71.0</td>
<td>0.258</td>
<td>0.253</td>
</tr>
<tr>
<td>Gemini2.5-Pro</td>
<td>0.251</td>
<td>0.163</td>
<td>0.402</td>
<td>77.1</td>
<td>0.236</td>
<td>0.202</td>
</tr>
<tr>
<td><strong>dots.ocr</strong> </td>
<td><strong>0.177</strong></td>
<td><strong>0.075</strong></td>
<td><strong>0.297</strong></td>
<td><strong>79.2</strong></td>
<td><strong>0.186</strong></td>
<td><strong>0.152</strong></td>
</tr>
</tbody>
</table>
> **Notes:**
> - We use the same metric calculation pipeline of [OmniDocBench](https://github.com/opendatalab/OmniDocBench).
> - We delete the Page-header and Page-footer cells in the result markdown.
#### Layout Detection
<table>
<thead>
<tr>
<th rowspan="2"><strong>Method</strong></th>
<th colspan="5" style="text-align: center;"><strong>F1@IoU=.50:.05:.95↑</strong></th>
<th colspan="5" style="text-align: center;"><strong>F1@IoU=.50↑</strong></th>
</tr>
<tr>
<th>Overall</th>
<th>Text</th>
<th>Formula</th>
<th>Table</th>
<th>Picture</th>
<th>Overall</th>
<th>Text</th>
<th>Formula</th>
<th>Table</th>
<th>Picture</th>
</tr>
</thead>
<tbody>
<td>DocLayout-YOLO-DocStructBench</td>
<td>0.733</td>
<td>0.694</td>
<td>0.480</td>
<td>0.803</td>
<td>0.619</td>
<td>0.806</td>
<td>0.779</td>
<td>0.620</td>
<td>0.858</td>
<td>0.678</td>
</tr>
<tr>
<td>dots.ocr-parse all</td>
<td>0.831</td>
<td>0.801</td>
<td>0.654</td>
<td>0.838</td>
<td>0.748</td>
<td>0.922</td>
<td>0.909</td>
<td>0.770</td>
<td>0.888</td>
<td>0.831</td>
</tr>
<tr>
<td> <strong>dots.ocr-detection only</strong> </td>
<td><strong>0.845</strong></td>
<td><strong>0.816</strong></td>
<td><strong>0.716</strong></td>
<td><strong>0.875</strong></td>
<td><strong>0.765</strong></td>
<td><strong>0.930</strong></td>
<td><strong>0.917</strong></td>
<td><strong>0.832</strong></td>
<td><strong>0.918</strong></td>
<td><strong>0.843</strong></td>
</tr>
</tbody>
</table>
> **Notes:**
> - prompt_layout_all_en for **parse all**, prompt_layout_only_en for **detection only**, please refer to [prompts](https://github.com/rednote-hilab/dots.ocr/blob/master/dots_ocr/utils/prompts.py)
### 3. olmOCR-bench.
<table>
<thead>
<tr>
<th>Model</th>
<th>ArXiv</th>
<th>Old Scans<br>Math</th>
<th>Tables</th>
<th>Old Scans</th>
<th>Headers and<br>Footers</th>
<th>Multi<br>column</th>
<th>Long Tiny<br>Text</th>
<th>Base</th>
<th>Overall</th>
</tr>
</thead>
<tbody>
<tr>
<td>GOT OCR</td>
<td>52.7</td>
<td>52.0</td>
<td>0.2</td>
<td>22.1</td>
<td>93.6</td>
<td>42.0</td>
<td>29.9</td>
<td>94.0</td>
<td>48.3 ± 1.1</td>
</tr>
<tr>
<td>Marker</td>
<td>76.0</td>
<td>57.9</td>
<td>57.6</td>
<td>27.8</td>
<td>84.9</td>
<td>72.9</td>
<td>84.6</td>
<td>99.1</td>
<td>70.1 ± 1.1</td>
</tr>
<tr>
<td>MinerU</td>
<td>75.4</td>
<td>47.4</td>
<td>60.9</td>
<td>17.3</td>
<td><strong>96.6</strong></td>
<td>59.0</td>
<td>39.1</td>
<td>96.6</td>
<td>61.5 ± 1.1</td>
</tr>
<tr>
<td>Mistral OCR</td>
<td>77.2</td>
<td>67.5</td>
<td>60.6</td>
<td>29.3</td>
<td>93.6</td>
<td>71.3</td>
<td>77.1</td>
<td>99.4</td>
<td>72.0 ± 1.1</td>
</tr>
<tr>
<td>Nanonets OCR</td>
<td>67.0</td>
<td>68.6</td>
<td>77.7</td>
<td>39.5</td>
<td>40.7</td>
<td>69.9</td>
<td>53.4</td>
<td>99.3</td>
<td>64.5 ± 1.1</td>
</tr>
<tr>
<td>GPT-4o<br>(No Anchor)</td>
<td>51.5</td>
<td><strong>75.5</strong></td>
<td>69.1</td>
<td>40.9</td>
<td>94.2</td>
<td>68.9</td>
<td>54.1</td>
<td>96.7</td>
<td>68.9 ± 1.1</td>
</tr>
<tr>
<td>GPT-4o<br>(Anchored)</td>
<td>53.5</td>
<td>74.5</td>
<td>70.0</td>
<td>40.7</td>
<td>93.8</td>
<td>69.3</td>
<td>60.6</td>
<td>96.8</td>
<td>69.9 ± 1.1</td>
</tr>
<tr>
<td>Gemini Flash 2<br>(No Anchor)</td>
<td>32.1</td>
<td>56.3</td>
<td>61.4</td>
<td>27.8</td>
<td>48.0</td>
<td>58.7</td>
<td><strong>84.4</strong></td>
<td>94.0</td>
<td>57.8 ± 1.1</td>
</tr>
<tr>
<td>Gemini Flash 2<br>(Anchored)</td>
<td>54.5</td>
<td>56.1</td>
<td>72.1</td>
<td>34.2</td>
<td>64.7</td>
<td>61.5</td>
<td>71.5</td>
<td>95.6</td>
<td>63.8 ± 1.2</td>
</tr>
<tr>
<td>Qwen 2 VL<br>(No Anchor)</td>
<td>19.7</td>
<td>31.7</td>
<td>24.2</td>
<td>17.1</td>
<td>88.9</td>
<td>8.3</td>
<td>6.8</td>
<td>55.5</td>
<td>31.5 ± 0.9</td>
</tr>
<tr>
<td>Qwen 2.5 VL<br>(No Anchor)</td>
<td>63.1</td>
<td>65.7</td>
<td>67.3</td>
<td>38.6</td>
<td>73.6</td>
<td>68.3</td>
<td>49.1</td>
<td>98.3</td>
<td>65.5 ± 1.2</td>
</tr>
<tr>
<td>olmOCR v0.1.75<br>(No Anchor)</td>
<td>71.5</td>
<td>71.4</td>
<td>71.4</td>
<td><strong>42.8</strong></td>
<td>94.1</td>
<td>77.7</td>
<td>71.0</td>
<td>97.8</td>
<td>74.7 ± 1.1</td>
</tr>
<tr>
<td>olmOCR v0.1.75<br>(Anchored)</td>
<td>74.9</td>
<td>71.2</td>
<td>71.0</td>
<td>42.2</td>
<td>94.5</td>
<td>78.3</td>
<td>73.3</td>
<td>98.3</td>
<td>75.5 ± 1.0</td>
</tr>
<tr>
<td>MonkeyOCR-pro-3B</td>
<td><strong>83.8</strong></td>
<td>68.8</td>
<td>74.6</td>
<td>36.1</td>
<td>91.2</td>
<td>76.6</td>
<td>80.1</td>
<td>95.3</td>
<td>75.8 ± 1.0</td>
</tr>
<tr>
<td><strong>dots.ocr</strong></td>
<td>82.1</td>
<td>64.2</td>
<td><strong>88.3</strong></td>
<td>40.9</td>
<td>94.1</td>
<td><strong>82.4</strong></td>
<td>81.2</td>
<td><strong>99.5</strong></td>
<td><strong>79.1 ± 1.0</strong></td>
</tr>
</tbody>
</table>
> **Note:**
> - The metrics are from [MonkeyOCR](https://github.com/Yuliang-Liu/MonkeyOCR),
[olmocr](https://github.com/allenai/olmocr), and our own internal evaluations.
> - We delete the Page-header and Page-footer cells in the result markdown.
# Quick Start
## 1. Installation
### Install dots.ocr
```shell
conda create -n dots_ocr python=3.12
conda activate dots_ocr
git clone https://github.com/rednote-hilab/dots.ocr.git
cd dots.ocr
# Install pytorch, see https://pytorch.org/get-started/previous-versions/ for your cuda version
pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/cu128
pip install -e .
```
If you have trouble with the installation, try our [Docker Image](https://hub.docker.com/r/rednotehilab/dots.ocr) for an easier setup, and follow these steps:
```shell
git clone https://github.com/rednote-hilab/dots.ocr.git
cd dots.ocr
pip install -e .
```
### Download Model Weights
> 💡**Note:** Please use a directory name without periods (e.g., `DotsOCR` instead of `dots.ocr`) for the model save path. This is a temporary workaround pending our integration with Transformers.
```shell
python3 tools/download_model.py
```
## 2. Deployment
### vLLM inference
We highly recommend using vllm for deployment and inference. All of our evaluations results are based on vllm version 0.9.1.
The [Docker Image](https://hub.docker.com/r/rednotehilab/dots.ocr) is based on the official vllm image. You can also follow [Dockerfile](https://github.com/rednote-hilab/dots.ocr/blob/master/docker/Dockerfile) to build the deployment environment by yourself.
```shell
# You need to register model to vllm at first
python3 tools/download_model.py
export hf_model_path=./weights/DotsOCR # Path to your downloaded model weights, Please use a directory name without periods (e.g., `DotsOCR` instead of `dots.ocr`) for the model save path. This is a temporary workaround pending our integration with Transformers.
export PYTHONPATH=$(dirname "$hf_model_path"):$PYTHONPATH
sed -i '/^from vllm\.entrypoints\.cli\.main import main$/a\
from DotsOCR import modeling_dots_ocr_vllm' `which vllm` # If you downloaded model weights by yourself, please replace `DotsOCR` by your model saved directory name, and remember to use a directory name without periods (e.g., `DotsOCR` instead of `dots.ocr`)
# launch vllm server
CUDA_VISIBLE_DEVICES=0 vllm serve ${hf_model_path} --tensor-parallel-size 1 --gpu-memory-utilization 0.95 --chat-template-content-format string --served-model-name model --trust-remote-code
# If you get a ModuleNotFoundError: No module named 'DotsOCR', please check the note above on the saved model directory name.
# vllm api demo
python3 ./demo/demo_vllm.py --prompt_mode prompt_layout_all_en
```
### Hugginface inference
```shell
python3 demo/demo_hf.py
```
<details>
<summary><b>Hugginface inference details</b></summary>
```python
import torch
from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer
from qwen_vl_utils import process_vision_info
from dots_ocr.utils import dict_promptmode_to_prompt
model_path = "./weights/DotsOCR"
model = AutoModelForCausalLM.from_pretrained(
model_path,
attn_implementation="flash_attention_2",
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
image_path = "demo/demo_image1.jpg"
prompt = """Please output the layout information from the PDF image, including each layout element's bbox, its category, and the corresponding text content within the bbox.
1. Bbox format: [x1, y1, x2, y2]
2. Layout Categories: The possible categories are ['Caption', 'Footnote', 'Formula', 'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', 'Title'].
3. Text Extraction & Formatting Rules:
- Picture: For the 'Picture' category, the text field should be omitted.
- Formula: Format its text as LaTeX.
- Table: Format its text as HTML.
- All Others (Text, Title, etc.): Format their text as Markdown.
4. Constraints:
- The output text must be the original text from the image, with no translation.
- All layout elements must be sorted according to human reading order.
5. Final Output: The entire output must be a single JSON object.
"""
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image_path
},
{"type": "text", "text": prompt}
]
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=24000)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```
</details>
## 3. Document Parse
**Based on vLLM server**, you can parse an image or a pdf file using the following commands:
```bash
# Parse all layout info, both detection and recognition
# Parse a single image
python3 dots_ocr/parser.py demo/demo_image1.jpg
# Parse a single PDF
python3 dots_ocr/parser.py demo/demo_pdf1.pdf --num_threads 64 # try bigger num_threads for pdf with a large number of pages
# Layout detection only
python3 dots_ocr/parser.py demo/demo_image1.jpg --prompt prompt_layout_only_en
# Parse text only, except Page-header and Page-footer
python3 dots_ocr/parser.py demo/demo_image1.jpg --prompt prompt_ocr
# Parse layout info by bbox
python3 dots_ocr/parser.py demo/demo_image1.jpg --prompt prompt_grounding_ocr --bbox 163 241 1536 705
```
<details>
<summary><b>Output Results</b></summary>
1. **Structured Layout Data** (`demo_image1.json`): A JSON file containing the detected layout elements, including their bounding boxes, categories, and extracted text.
2. **Processed Markdown File** (`demo_image1.md`): A Markdown file generated from the concatenated text of all detected cells.
* An additional version, `demo_image1_nohf.md`, is also provided, which excludes page headers and footers for compatibility with benchmarks like Omnidocbench and olmOCR-bench.
3. **Layout Visualization** (`demo_image1.jpg`): The original image with the detected layout bounding boxes drawn on it.
</details>
## 4. Demo
You can run the demo with the following command, or try directly at [live demo](https://dotsocr.xiaohongshu.com/)
```bash
python demo/demo_gradio.py
```
We also provide a demo for grounding ocr:
```bash
python demo/demo_gradio_annotion.py
```
### Example for formula document
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/formula1.png" alt="formula1.png" border="0" />
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/formula2.png" alt="formula2.png" border="0" />
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/formula3.png" alt="formula3.png" border="0" />
### Example for table document
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/table1.png" alt="table1.png" border="0" />
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/table2.png" alt="table2.png" border="0" />
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/table3.png" alt="table3.png" border="0" />
### Example for multilingual document
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/Tibetan.png" alt="Tibetan.png" border="0" />
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/tradition_zh.png" alt="tradition_zh.png" border="0" />
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/nl.png" alt="nl.png" border="0" />
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/kannada.png" alt="kannada.png" border="0" />
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/russian.png" alt="russian.png" border="0" />
### Example for reading order
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/reading_order.png" alt="reading_order.png" border="0" />
### Example for grounding ocr
<img src="https://raw.githubusercontent.com/rednote-hilab/dots.ocr/master/assets/showcase/grounding.png" alt="grounding.png" border="0" />
## Acknowledgments
We would like to thank [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL), [aimv2](https://github.com/apple/ml-aim), [MonkeyOCR](https://github.com/Yuliang-Liu/MonkeyOCR),
[OmniDocBench](https://github.com/opendatalab/OmniDocBench), [PyMuPDF](https://github.com/pymupdf/PyMuPDF), for providing code and models.
We also thank [DocLayNet](https://github.com/DS4SD/DocLayNet), [M6Doc](https://github.com/HCIILAB/M6Doc), [CDLA](https://github.com/buptlihang/CDLA), [D4LA](https://github.com/AlibabaResearch/AdvancedLiterateMachinery) for providing valuable datasets.
## Limitation & Future Work
- **Complex Document Elements:**
- **Table&Formula**: dots.ocr is not yet perfect for high-complexity tables and formula extraction.
- **Picture**: Pictures in documents are currently not parsed.
- **Parsing Failures:** The model may fail to parse under certain conditions:
- When the character-to-pixel ratio is excessively high. Try enlarging the image or increasing the PDF parsing DPI (a setting of 200 is recommended). However, please note that the model performs optimally on images with a resolution under 11289600 pixels.
- Continuous special characters, such as ellipses (`...`) and underscores (`_`), may cause the prediction output to repeat endlessly. In such scenarios, consider using alternative prompts like `prompt_layout_only_en`, `prompt_ocr`, or `prompt_grounding_ocr` ([details here](https://github.com/rednote-hilab/dots.ocr/blob/master/dots_ocr/utils/prompts.py)).
- **Performance Bottleneck:** Despite its 1.7B parameter LLM foundation, **dots.ocr** is not yet optimized for high-throughput processing of large PDF volumes.
We are committed to achieving more accurate table and formula parsing, as well as enhancing the model's OCR capabilities for broader generalization, all while aiming for **a more powerful, more efficient model**. Furthermore, we are actively considering the development of **a more general-purpose perception model** based on Vision-Language Models (VLMs), which would integrate general detection, image captioning, and OCR tasks into a unified framework. **Parsing the content of the pictures in the documents** is also a key priority for our future work.
We believe that collaboration is the key to tackling these exciting challenges. If you are passionate about advancing the frontiers of document intelligence and are interested in contributing to these future endeavors, we would love to hear from you. Please reach out to us via email at: [[email protected]].
|
yuyanghu06/PAINT_Renaissance_Model
|
yuyanghu06
| 2025-08-07T00:51:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-07T00:51:06Z |
Art restoration inpainting model trained on Renaissance artworks
Fine tuned Stable Diffusion 1.5
|
rbelanec/train_openbookqa_1754507499
|
rbelanec
| 2025-08-07T00:43:47Z | 21 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"ia3",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-08-07T00:13:12Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- ia3
- generated_from_trainer
model-index:
- name: train_openbookqa_1754507499
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_openbookqa_1754507499
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the openbookqa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2537
- Num Input Tokens Seen: 4204168
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 123
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|
| 0.1575 | 0.5 | 558 | 0.5180 | 210048 |
| 0.1755 | 1.0 | 1116 | 0.3476 | 420520 |
| 0.2585 | 1.5 | 1674 | 0.3197 | 630888 |
| 0.2442 | 2.0 | 2232 | 0.3002 | 841024 |
| 0.2445 | 2.5 | 2790 | 0.2924 | 1051168 |
| 0.2615 | 3.0 | 3348 | 0.2820 | 1261304 |
| 0.077 | 3.5 | 3906 | 0.2786 | 1472152 |
| 0.1693 | 4.0 | 4464 | 0.2682 | 1682016 |
| 0.2201 | 4.5 | 5022 | 0.2648 | 1892160 |
| 0.3082 | 5.0 | 5580 | 0.2638 | 2102920 |
| 0.1839 | 5.5 | 6138 | 0.2633 | 2311976 |
| 0.4629 | 6.0 | 6696 | 0.2598 | 2523672 |
| 0.2638 | 6.5 | 7254 | 0.2586 | 2732440 |
| 0.0373 | 7.0 | 7812 | 0.2553 | 2943688 |
| 0.3764 | 7.5 | 8370 | 0.2552 | 3153640 |
| 0.2395 | 8.0 | 8928 | 0.2559 | 3363864 |
| 0.3905 | 8.5 | 9486 | 0.2537 | 3574616 |
| 0.1212 | 9.0 | 10044 | 0.2545 | 3783840 |
| 0.0915 | 9.5 | 10602 | 0.2549 | 3994976 |
| 0.9298 | 10.0 | 11160 | 0.2543 | 4204168 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
rmdhirr/gemma-base-2-3-600
|
rmdhirr
| 2025-08-07T00:42:08Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-3-12b-it",
"base_model:adapter:google/gemma-3-12b-it",
"region:us"
] | null | 2025-08-07T00:37:15Z |
---
base_model: google/gemma-3-12b-it
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.14.0
|
unbeatablemx/a7melinda
|
unbeatablemx
| 2025-08-07T00:38:52Z | 2 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-07T00:06:08Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: A7MELINDA
---
# A7Melinda
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `A7MELINDA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "A7MELINDA",
"lora_weights": "https://huggingface.co/unbeatablemx/a7melinda/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('unbeatablemx/a7melinda', weight_name='lora.safetensors')
image = pipeline('A7MELINDA').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 3000
- Learning rate: 0.0001
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/unbeatablemx/a7melinda/discussions) to add images that show off what you’ve made with this LoRA.
|
JHelhoski/SmolLM-FT-OHPC
|
JHelhoski
| 2025-08-07T00:35:59Z | 1 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"base_model:HuggingFaceTB/SmolLM-360M",
"base_model:finetune:HuggingFaceTB/SmolLM-360M",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-07T00:35:24Z |
---
base_model: HuggingFaceTB/SmolLM-360M
library_name: transformers
model_name: SmolLM-FT-OHPC
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for SmolLM-FT-OHPC
This model is a fine-tuned version of [HuggingFaceTB/SmolLM-360M](https://huggingface.co/HuggingFaceTB/SmolLM-360M).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="JHelhoski/SmolLM-FT-OHPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jhelhos1-binghamton-university/huggingface/runs/9wj50x5k)
This model was trained with SFT.
### Framework versions
- TRL: 0.20.0
- Transformers: 4.54.1
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
johngreendr1/944ee3dc-41cb-4ab6-89f8-41d455779268
|
johngreendr1
| 2025-08-07T00:33:26Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"region:us"
] | null | 2025-08-06T20:46:39Z |
---
base_model: Qwen/Qwen3-8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
John6666/woomix-illustrious-v10b-sdxl
|
John6666
| 2025-08-07T00:30:45Z | 72 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"perfect",
"diverse",
"merge",
"noobai",
"Illustrious XL v2.0",
"illustrious",
"en",
"base_model:Laxhar/noobai-XL-1.1",
"base_model:merge:Laxhar/noobai-XL-1.1",
"base_model:OnomaAIResearch/Illustrious-XL-v2.0",
"base_model:merge:OnomaAIResearch/Illustrious-XL-v2.0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-08-07T00:23:40Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- perfect
- diverse
- merge
- noobai
- Illustrious XL v2.0
- illustrious
base_model:
- OnomaAIResearch/Illustrious-XL-v2.0
- Laxhar/noobai-XL-1.1
---
Original model is [here](https://civitai.com/models/1785128?modelVersionId=2087963).
This model created by [EcchiWoo](https://civitai.com/user/EcchiWoo).
|
hdong0/Qwen-Math-7B-batch-cross-GRPO_deepscaler_acc_seq_end_mask_template_mu_8_simple
|
hdong0
| 2025-08-07T00:29:35Z | 38 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:agentica-org/DeepScaleR-Preview-Dataset",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-05T10:48:07Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: agentica-org/DeepScaleR-Preview-Dataset
library_name: transformers
model_name: Qwen-Math-7B-batch-cross-GRPO_deepscaler_acc_seq_end_mask_template_mu_8_simple
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-Math-7B-batch-cross-GRPO_deepscaler_acc_seq_end_mask_template_mu_8_simple
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [agentica-org/DeepScaleR-Preview-Dataset](https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="hdong0/Qwen-Math-7B-batch-cross-GRPO_deepscaler_acc_seq_end_mask_template_mu_8_simple", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
mradermacher/Commgpt-3B-GGUF
|
mradermacher
| 2025-08-07T00:25:59Z | 3,634 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:dabboud/Commgpt-3B",
"base_model:quantized:dabboud/Commgpt-3B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-06T21:59:00Z |
---
base_model: dabboud/Commgpt-3B
language: en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/dabboud/Commgpt-3B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Commgpt-3B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Commgpt-3B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Commgpt-3B-GGUF/resolve/main/Commgpt-3B.Q2_K.gguf) | Q2_K | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Commgpt-3B-GGUF/resolve/main/Commgpt-3B.Q3_K_S.gguf) | Q3_K_S | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Commgpt-3B-GGUF/resolve/main/Commgpt-3B.Q3_K_M.gguf) | Q3_K_M | 1.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Commgpt-3B-GGUF/resolve/main/Commgpt-3B.Q3_K_L.gguf) | Q3_K_L | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Commgpt-3B-GGUF/resolve/main/Commgpt-3B.IQ4_XS.gguf) | IQ4_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Commgpt-3B-GGUF/resolve/main/Commgpt-3B.Q4_K_S.gguf) | Q4_K_S | 1.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Commgpt-3B-GGUF/resolve/main/Commgpt-3B.Q4_K_M.gguf) | Q4_K_M | 2.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Commgpt-3B-GGUF/resolve/main/Commgpt-3B.Q5_K_S.gguf) | Q5_K_S | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Commgpt-3B-GGUF/resolve/main/Commgpt-3B.Q5_K_M.gguf) | Q5_K_M | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Commgpt-3B-GGUF/resolve/main/Commgpt-3B.Q6_K.gguf) | Q6_K | 2.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Commgpt-3B-GGUF/resolve/main/Commgpt-3B.Q8_0.gguf) | Q8_0 | 3.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Commgpt-3B-GGUF/resolve/main/Commgpt-3B.f16.gguf) | f16 | 6.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
theAdhiscio/Krishi-Saarthi-Gemma3nE2B
|
theAdhiscio
| 2025-08-07T00:14:15Z | 220 | 0 | null |
[
"safetensors",
"gguf",
"gemma3n",
"base_model:google/gemma-3n-E2B-it",
"base_model:quantized:google/gemma-3n-E2B-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-06T22:34:54Z |
---
license: apache-2.0
base_model:
- unsloth/gemma-3n-E2B-it-unsloth-bnb-4bit
- google/gemma-3n-E2B-it
---
Krishi Saarthi offline-first, multimodal AI agricultural advisor built on Gemma 3n-E2B, designed to democratize expert agricultural knowledge for millions of Indian farmers in areas with low connectivity. Fine-tuning on government agricultural helpline records, Agri Saathi delivers instant, accurate farming advice in local languages, working entirely offline on mobile devices.
Based on Gemma 3n
Used Unsloth to finetune
Finetune Database: https://kcc-chakshu.icar-web.com/6_data_extract.php
|
giovannidemuri/llama3b-llamab8-er-afg-v62-seed2-hx-alpaca-fpt
|
giovannidemuri
| 2025-08-07T00:13:34Z | 1 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.2-3B",
"base_model:finetune:meta-llama/Llama-3.2-3B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-06T23:02:51Z |
---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-3B
tags:
- generated_from_trainer
model-index:
- name: llama3b-llamab8-er-afg-v62-seed2-hx-alpaca-fpt
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama3b-llamab8-er-afg-v62-seed2-hx-alpaca-fpt
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 2
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu128
- Datasets 3.6.0
- Tokenizers 0.21.2
|
elsvastika/blockassist-bc-arctic_soaring_weasel_1754523298
|
elsvastika
| 2025-08-07T00:12:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"arctic soaring weasel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-07T00:12:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- arctic soaring weasel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_openbookqa_1754507498
|
rbelanec
| 2025-08-07T00:12:30Z | 22 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prompt-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-08-06T23:35:55Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prompt-tuning
- generated_from_trainer
model-index:
- name: train_openbookqa_1754507498
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_openbookqa_1754507498
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the openbookqa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2460
- Num Input Tokens Seen: 4204168
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 123
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|
| 0.3495 | 0.5 | 558 | 0.4794 | 210048 |
| 0.2307 | 1.0 | 1116 | 0.2934 | 420520 |
| 0.2229 | 1.5 | 1674 | 0.2863 | 630888 |
| 0.1181 | 2.0 | 2232 | 0.3010 | 841024 |
| 0.0775 | 2.5 | 2790 | 0.3032 | 1051168 |
| 0.3557 | 3.0 | 3348 | 0.2460 | 1261304 |
| 0.1723 | 3.5 | 3906 | 0.2663 | 1472152 |
| 0.0172 | 4.0 | 4464 | 0.2600 | 1682016 |
| 0.3535 | 4.5 | 5022 | 0.2604 | 1892160 |
| 0.0257 | 5.0 | 5580 | 0.2711 | 2102920 |
| 0.2185 | 5.5 | 6138 | 0.2833 | 2311976 |
| 0.1686 | 6.0 | 6696 | 0.2713 | 2523672 |
| 0.3082 | 6.5 | 7254 | 0.2828 | 2732440 |
| 0.002 | 7.0 | 7812 | 0.2848 | 2943688 |
| 0.2526 | 7.5 | 8370 | 0.2857 | 3153640 |
| 0.3551 | 8.0 | 8928 | 0.2850 | 3363864 |
| 0.3767 | 8.5 | 9486 | 0.2826 | 3574616 |
| 0.0792 | 9.0 | 10044 | 0.2863 | 3783840 |
| 0.0945 | 9.5 | 10602 | 0.2853 | 3994976 |
| 0.9347 | 10.0 | 11160 | 0.2851 | 4204168 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Gen-Verse/ReasonFlux-V2-32B-Proposer
|
Gen-Verse
| 2025-08-07T00:12:22Z | 7 | 2 | null |
[
"safetensors",
"qwen3",
"arxiv:2502.06772",
"region:us"
] | null | 2025-06-09T15:26:34Z |
<div align="center">
<h1>ReasonFlux-V2:Internalizing Template-Augmented LLM Reasoning
with Hierarchical Reinforcement Learning</h1>
</div>
<p align="center">
<img src="./figs/comparison.png" width=80%>
</p>
**ReasonFlux-V2** is our new template-augmented reasoning paradigm which **internalize the thought templates** through **iterative hierarchical reinforcement learning**. Specifically, we first develop an automated pipeline to extract thought templates from the problem–solution pairs in training set. To effectively internalize these high-level thought templates and learning a more efficient reasoning paradigm, we propose two collaborative modules: **Template Proposer** which adaptively proposes suitable thought templates based on the input problem; and **Template Reasoner**,which exactly instantiates the proposed templates and performs precise, detailed reasoning. Building upon these modules, we iteratively conduct **hierarchical RL** on optimizing both modules.
<p align="center">
<img src="./figs/ReasonFluxv2_method.png" width=80%>
</p>
**ReasonFlux-V2** offers a more efficient, generalizable solution for enhancing the complex reasoning capabilities of LLMs. Compare with conventional reasoning LLMs, our **ReasonFlux-V2** could correctly and efficiently solve the problems with less token consumption and inference time.
**We will release our paper related with ReasonFlux-V2 soon.**
ReasonFlux-v2 consists of two main modules:
1. **Template Proposer**, which **adaptively** proposes suitable high-level thought templates based on the input problem. It functions as intuitive thinking process of human which helps to **narrow the exploration space** of detailed reasoning process thus **improve the solution efficiency**.
2. **Template Reasoner**, which follow the proposed high-level thought template to efficiently and effectively solve the corresponding problem.
<p align="center">
<img src="./figs/reasonflux_v2.png" width=80%>
</p>
[Template Reasoner](https://huggingface.co/Gen-Verse/ReasonFlux-V2-32B-Reasoner)|[Code](https://github.com/Gen-Verse/ReasonFlux)|[Template](Gen-Verse/ReasonFlux-V2-Template)|[SFT Dataset](https://huggingface.co/datasets/Gen-Verse/ReasonFlux-V2-SFT/) |[DPO Dataset](https://huggingface.co/datasets/Gen-Verse/ReasonFlux-V2-DPO)
## Citation
```bash
@article{yang2025reasonflux,
title={ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates},
author={Yang, Ling and Yu, Zhaochen and Cui, Bin and Wang, Mengdi},
journal={arXiv preprint arXiv:2502.06772},
year={2025}
}
```
|
Sam9281/Sam
|
Sam9281
| 2025-08-07T00:11:22Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-07T00:11:22Z |
---
license: apache-2.0
---
|
John6666/illv-i-love-lucid-visuals-illv10-sdxl
|
John6666
| 2025-08-07T00:11:21Z | 1 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"cartoon",
"hyper-realism",
"stylized",
"detailed",
"girls",
"cute",
"Illustrious XL v2.0",
"illustrious",
"en",
"base_model:OnomaAIResearch/Illustrious-XL-v2.0",
"base_model:finetune:OnomaAIResearch/Illustrious-XL-v2.0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-08-07T00:06:37Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- cartoon
- hyper-realism
- stylized
- detailed
- girls
- cute
- Illustrious XL v2.0
- illustrious
base_model: OnomaAIResearch/Illustrious-XL-v2.0
---
Original model is [here](https://civitai.com/models/1291368?modelVersionId=2087008).
This model created by [Xmutsix](https://civitai.com/user/Xmutsix).
|
hjerpe/agent-2048-lora
|
hjerpe
| 2025-08-07T00:10:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-06T16:58:39Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
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