modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
eshanroy5678/blockassist-bc-untamed_dextrous_dingo_1756097311
|
eshanroy5678
| 2025-08-25T04:55:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed dextrous dingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:53:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed dextrous dingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Smilyai-labs/Sam-reason-A1
|
Smilyai-labs
| 2025-08-25T04:54:55Z | 20 | 1 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"smilyai-reasoning",
"smilyai",
"sam-reasoning",
"A series",
"A1",
"A",
"conversational",
"custom_code",
"en",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-15T23:00:36Z |
---
pipeline_tag: text-generation
library_name: transformers
language: en
license: mit
tags:
- text-generation
- smilyai-reasoning
- smilyai
- sam-reasoning
- A series
- A1
- A
- A series
---
⚠️ **Notice:** This model is sunsetting soon and will no longer be on huggingface after the launch of sam-1.2. Please migrate to sam-1 or sam-1.2 once released.
# Sam-reason-A1 by Smilyai-labs
## Model Description
Sam-reason-A1 is an instruction-tuned large language model developed by Smilyai-labs, designed to enhance conversational AI agents with explicit reasoning capabilities and a consistent, engaging persona. It is part of the "Sam-reason" series, which focuses on integrating internal thought processes and a distinct identity into its responses.
A core feature of Sam-reason-A1 is its unique **`<think>` block**, where the model articulates its internal reasoning process before generating its final, concise answer. This makes the model particularly useful for applications where transparency of AI thought or a structured "chain of thought" is desired.
* **Developed by:** Smilyai-labs
* **Model type:** Instruction-tuned Language Model
* **Language(s) (NLP):** English
* **License:** MIT
* **Finetuned from model [optional]:** QWEN3
## Uses
### Direct Use
* **Transparent Conversational Agents:** Ideal for chatbots or virtual assistants where users benefit from understanding the AI's reasoning behind its answers.
* **Character-driven AI:** Can be used to power AI characters in games, interactive narratives, or simulations that require a consistent personality and visible internal logic.
* **Educational Tools:** Useful for demonstrating AI reasoning processes in an accessible format.
* **Research into AI Identity and Persona:** Supports exploration of how AI models can maintain coherent identities and interact with specific personas.
### Out-of-Scope Use
* **Fact-checking or critical decision-making:** While it demonstrates reasoning, it is an experimental model and should not be relied upon for critical, high-stakes applications where factual accuracy or unverified information could lead to harm.
* **General-purpose factual knowledge retrieval:** While it can provide information, its primary focus is on reasoning and persona, not exhaustive factual recall or acting as a search engine.
* **Applications requiring strict neutrality:** The model's persona may contain elements of sarcasm or a "villainous" tone, which might not be suitable for all contexts.
* **Generating harmful, unethical, or illegal content:** The model should not be used to produce, promote, or facilitate any content that is discriminatory, hateful, violent, or illegal.
## Bias, Risks, and Limitations
* **Persona Bias:** The model's intended persona (e.g., sarcastic, slightly villainous) may introduce biases or tones that are not universally desired. Users should be aware of and account for this in their applications.
* **Reasoning Hallucinations:** While designed for reasoning, the content within the `<think>` block, like all LLM outputs, can still contain inaccuracies or "hallucinations" that do not reflect true logical inference. It represents the *model's attempt* at reasoning, not guaranteed flawless logic.
* **Training Data Biases:** As with any model trained on large datasets, Sam-reason-A1 may inherit biases present in its training data, which could manifest in its responses or reasoning processes.
* **Limited Domain Expertise:** The model's reasoning is general-purpose within its character and task scope and may not possess deep domain-specific knowledge required for expert systems.
### Recommendations
Users are strongly encouraged to:
* Implement robust content moderation and safety filters if deploying in user-facing applications.
* Clearly communicate the model's nature (an AI demonstrating reasoning, not necessarily infallible logic) to end-users.
* Continuously monitor outputs for unexpected or undesirable behavior.
* Fine-tune or adapt the model further for specific safety requirements or desired personas.
## How to Get Started with the Model
### GGUF Usage (for local inference)
This model is typically consumed via its GGUF quantized versions for efficient local inference. You can use tools like `llama.cpp` or compatible libraries.
```python
# Example using llama.cpp (adjust path to your A1 GGUF file)
# First, ensure you have llama.cpp built and your model downloaded.
# ./main -m sam-reason-a1.gguf -p "<prompt>" -n 128 --temp 0.7 --top-k 40 --top-p 0.9 --repeat-penalty 1.1
# For Python using a library like 'llama-cpp-python' (install with pip install llama-cpp-python)
from llama_cpp import Llama
llm = Llama(model_path="./path/to/your/sam-reason-a1.gguf")
prompt = "What is the capital of France?"
output = llm(f"<|user|>{prompt}<|endoftext|>\n<|assistant|>",
max_tokens=256,
stop=["<|endoftext|>", "<|user|>"],
echo=True)
print(output["choices"][0]["text"])
|
mradermacher/Seed-OSS-36B-Base-i1-GGUF
|
mradermacher
| 2025-08-25T04:53:49Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"vllm",
"en",
"base_model:ByteDance-Seed/Seed-OSS-36B-Base",
"base_model:quantized:ByteDance-Seed/Seed-OSS-36B-Base",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-08-25T01:44:20Z |
---
base_model: ByteDance-Seed/Seed-OSS-36B-Base
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- vllm
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/ByteDance-Seed/Seed-OSS-36B-Base
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Seed-OSS-36B-Base-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Seed-OSS-36B-Base-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/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-IQ1_S.gguf) | i1-IQ1_S | 8.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-IQ1_M.gguf) | i1-IQ1_M | 8.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-IQ2_XS.gguf) | i1-IQ2_XS | 11.0 | |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-IQ2_S.gguf) | i1-IQ2_S | 11.7 | |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-IQ2_M.gguf) | i1-IQ2_M | 12.6 | |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-Q2_K_S.gguf) | i1-Q2_K_S | 12.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-Q2_K.gguf) | i1-Q2_K | 13.7 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 14.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-IQ3_XS.gguf) | i1-IQ3_XS | 15.2 | |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-Q3_K_S.gguf) | i1-Q3_K_S | 16.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-IQ3_S.gguf) | i1-IQ3_S | 16.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-IQ3_M.gguf) | i1-IQ3_M | 16.6 | |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-Q3_K_M.gguf) | i1-Q3_K_M | 17.7 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-Q3_K_L.gguf) | i1-Q3_K_L | 19.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-IQ4_XS.gguf) | i1-IQ4_XS | 19.6 | |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-Q4_0.gguf) | i1-Q4_0 | 20.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-Q4_K_S.gguf) | i1-Q4_K_S | 20.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-Q4_K_M.gguf) | i1-Q4_K_M | 21.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-Q4_1.gguf) | i1-Q4_1 | 22.9 | |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-Q5_K_S.gguf) | i1-Q5_K_S | 25.1 | |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-Q5_K_M.gguf) | i1-Q5_K_M | 25.7 | |
| [GGUF](https://huggingface.co/mradermacher/Seed-OSS-36B-Base-i1-GGUF/resolve/main/Seed-OSS-36B-Base.i1-Q6_K.gguf) | i1-Q6_K | 29.8 | practically like static Q6_K |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
kuldeepai05/ViolationFinal
|
kuldeepai05
| 2025-08-25T04:50:48Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"phi3",
"text-generation",
"base_model:adapter:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"conversational",
"arxiv:1910.09700",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T04:49:07Z |
---
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/Phi-3-mini-4k-instruct-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 0.17.0
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756097350
|
kapalbalap
| 2025-08-25T04:49:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:49:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
anan290819001/blockassist-bc-tall_tropical_hornet_1756096327
|
anan290819001
| 2025-08-25T04:48:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall tropical hornet",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:48:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall tropical hornet
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1756097217
|
roeker
| 2025-08-25T04:48:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:47:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dgambettaphd/M_mis_run2_gen8_WXS_doc1000_synt64_lr1e-04_acm_FRESH
|
dgambettaphd
| 2025-08-25T04:48:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-25T04:48:00Z |
---
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]
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1756097004
|
IvanJAjebu
| 2025-08-25T04:44:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:44:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hdong0/deepseek-Qwen2.5-7B-baseline-thin-Open-R1-GRPO_deepscaler_acc_mu_8_constant_lr_warmed_math
|
hdong0
| 2025-08-25T04:39:53Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2bm",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"custom_code",
"dataset:agentica-org/DeepScaleR-Preview-Dataset",
"arxiv:2402.03300",
"base_model:hdong0/deepseek-Qwen2.5-7B-baseline-thin-init",
"base_model:finetune:hdong0/deepseek-Qwen2.5-7B-baseline-thin-init",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-08-24T02:43:44Z |
---
base_model: hdong0/deepseek-Qwen2.5-7B-baseline-thin-init
datasets: agentica-org/DeepScaleR-Preview-Dataset
library_name: transformers
model_name: deepseek-Qwen2.5-7B-baseline-thin-Open-R1-GRPO_deepscaler_acc_mu_8_constant_lr_warmed_math
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for deepseek-Qwen2.5-7B-baseline-thin-Open-R1-GRPO_deepscaler_acc_mu_8_constant_lr_warmed_math
This model is a fine-tuned version of [hdong0/deepseek-Qwen2.5-7B-baseline-thin-init](https://huggingface.co/hdong0/deepseek-Qwen2.5-7B-baseline-thin-init) 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/deepseek-Qwen2.5-7B-baseline-thin-Open-R1-GRPO_deepscaler_acc_mu_8_constant_lr_warmed_math", 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}}
}
```
|
unitova/blockassist-bc-zealous_sneaky_raven_1756095051
|
unitova
| 2025-08-25T04:38:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:38:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756096606
|
kapalbalap
| 2025-08-25T04:37:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:37:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1756096535
|
roeker
| 2025-08-25T04:36:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:36:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1756094985
|
quantumxnode
| 2025-08-25T04:34:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:34:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1756094981
|
mang3dd
| 2025-08-25T04:34:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:34:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1756096395
|
IvanJAjebu
| 2025-08-25T04:34:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:34:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
afroneko/blockassist-bc-yawning_melodic_starfish_1756096300
|
afroneko
| 2025-08-25T04:33:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning melodic starfish",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:32:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning melodic starfish
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756096237
|
liukevin666
| 2025-08-25T04:32:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:31:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756096286
|
kapalbalap
| 2025-08-25T04:32:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:32:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1756094540
|
kojeklollipop
| 2025-08-25T04:30:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:30:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
PatrickStar76/test3
|
PatrickStar76
| 2025-08-25T04:29:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-25T01:21:31Z |
---
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
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### 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]
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756096119
|
kapalbalap
| 2025-08-25T04:29:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:29:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ZK0622/a0.1_mistral_adalora_sft
|
ZK0622
| 2025-08-25T04:29:25Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:ZK0622/mistral7b-legal-summarizer-adalora-v0.1",
"base_model:adapter:ZK0622/mistral7b-legal-summarizer-adalora-v0.1",
"region:us"
] | null | 2025-08-25T04:28:43Z |
---
base_model: ZK0622/mistral7b-legal-summarizer-adalora-v0.1
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 0.10.0
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756095775
|
kapalbalap
| 2025-08-25T04:23:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:23:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1756094581
|
Sayemahsjn
| 2025-08-25T04:21:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:21:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eshanroy5678/blockassist-bc-untamed_dextrous_dingo_1756095270
|
eshanroy5678
| 2025-08-25T04:20:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed dextrous dingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:17:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed dextrous dingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756095490
|
kapalbalap
| 2025-08-25T04:19:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:19:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1756093911
|
manusiaperahu2012
| 2025-08-25T04:16:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring long tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:16:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring long tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Team-Atom/act_door_01_96_40000
|
Team-Atom
| 2025-08-25T04:15:55Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:Team-Atom/door_01",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-25T04:15:41Z |
---
datasets: Team-Atom/door_01
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- act
- lerobot
- 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
|
BootesVoid/cme24ia9c0bk2gwtch828d5vh_cmeqjlh0u0bkdtlqbrtw1yaas
|
BootesVoid
| 2025-08-25T04:11:40Z | 0 | 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
| 2025-08-25T04:11:38Z |
---
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: SARAH
---
# Cme24Ia9C0Bk2Gwtch828D5Vh_Cmeqjlh0U0Bkdtlqbrtw1Yaas
<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 `SARAH` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SARAH",
"lora_weights": "https://huggingface.co/BootesVoid/cme24ia9c0bk2gwtch828d5vh_cmeqjlh0u0bkdtlqbrtw1yaas/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('BootesVoid/cme24ia9c0bk2gwtch828d5vh_cmeqjlh0u0bkdtlqbrtw1yaas', weight_name='lora.safetensors')
image = pipeline('SARAH').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: 2500
- Learning rate: 9e-05
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cme24ia9c0bk2gwtch828d5vh_cmeqjlh0u0bkdtlqbrtw1yaas/discussions) to add images that show off what you’ve made with this LoRA.
|
loopping/blockassist-bc-scurrying_opaque_mandrill_1756095054
|
loopping
| 2025-08-25T04:11:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scurrying opaque mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:10:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scurrying opaque mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756094891
|
kapalbalap
| 2025-08-25T04:09:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:09:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756094725
|
kapalbalap
| 2025-08-25T04:06:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:06:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1756094673
|
IvanJAjebu
| 2025-08-25T04:05:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T04:05:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nightmedia/Sugoi-14B-Ultra-HF-qx6-mlx
|
nightmedia
| 2025-08-25T04:00:43Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen2",
"text-generation",
"translation",
"transformers",
"conversational",
"ja",
"en",
"base_model:sugoitoolkit/Sugoi-14B-Ultra-HF",
"base_model:quantized:sugoitoolkit/Sugoi-14B-Ultra-HF",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"6-bit",
"region:us"
] |
text-generation
| 2025-08-25T03:51:21Z |
---
license: apache-2.0
language:
- ja
- en
base_model: sugoitoolkit/Sugoi-14B-Ultra-HF
tags:
- translation
- transformers
- mlx
library_name: mlx
pipeline_tag: text-generation
---
# Sugoi-14B-Ultra-HF-qx6-mlx
This model [Sugoi-14B-Ultra-HF-qx6-mlx](https://huggingface.co/Sugoi-14B-Ultra-HF-qx6-mlx) was
converted to MLX format from [sugoitoolkit/Sugoi-14B-Ultra-HF](https://huggingface.co/sugoitoolkit/Sugoi-14B-Ultra-HF)
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("Sugoi-14B-Ultra-HF-qx6-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)
```
|
ijustabi/blockassist-bc-lethal_nimble_cockroach_1756094332
|
ijustabi
| 2025-08-25T03:59:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lethal nimble cockroach",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:59:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lethal nimble cockroach
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
LucasTakanori/Qwen2.5-0.5B-Instruct_ft_fixed
|
LucasTakanori
| 2025-08-25T03:59:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T03:33:51Z |
---
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct_ft_fixed
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct_ft_fixed
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/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="LucasTakanori/Qwen2.5-0.5B-Instruct_ft_fixed", 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: 4.55.2
- Pytorch: 2.8.0+cu126
- 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}}
}
```
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1756094273
|
IvanJAjebu
| 2025-08-25T03:59:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:58:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
EmilRyd/gpt-oss-20b-aquarat-ground-truth-on-policy-1e5-22
|
EmilRyd
| 2025-08-25T03:58:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T03:53:38Z |
---
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]
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756094256
|
liukevin666
| 2025-08-25T03:58:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:58:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756094251
|
kapalbalap
| 2025-08-25T03:58:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:58:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756094083
|
kapalbalap
| 2025-08-25T03:55:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:55:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-beaked_lumbering_cockroach_1756094124
|
AnerYubo
| 2025-08-25T03:55:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked lumbering cockroach",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:55:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked lumbering cockroach
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
8septiadi8/blockassist-bc-curious_lightfooted_mouse_1756093869
|
8septiadi8
| 2025-08-25T03:53:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"curious lightfooted mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:52:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- curious lightfooted mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tahamajs/btc_pred_with_thinking_half_tokenization_Qwen_8B
|
tahamajs
| 2025-08-25T03:50:34Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/qwen3-8b-unsloth-bnb-4bit",
"lora",
"transformers",
"unsloth",
"text-generation",
"conversational",
"arxiv:1910.09700",
"region:us"
] |
text-generation
| 2025-08-25T03:49:54Z |
---
base_model: unsloth/qwen3-8b-unsloth-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/qwen3-8b-unsloth-bnb-4bit
- lora
- transformers
- 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 0.17.0
|
gbeane66/poca-SoccerTwos
|
gbeane66
| 2025-08-25T03:48:24Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2025-08-25T03:48:18Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
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: gbeane66/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756093585
|
liukevin666
| 2025-08-25T03:47:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:47:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1756091786
|
chainway9
| 2025-08-25T03:41:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:41:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ijustabi/blockassist-bc-lethal_nimble_cockroach_1756093218
|
ijustabi
| 2025-08-25T03:41:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lethal nimble cockroach",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:40:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lethal nimble cockroach
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
8septiadi8/blockassist-bc-curious_lightfooted_mouse_1756092787
|
8septiadi8
| 2025-08-25T03:35:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"curious lightfooted mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:35:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- curious lightfooted mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756092666
|
kapalbalap
| 2025-08-25T03:32:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:31:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
New-Viral-uppal-farm-girl-Viral-Video-link/Orginal.full.Videos.uppal.farm.girl.viral.video.Official.Tutorial
|
New-Viral-uppal-farm-girl-Viral-Video-link
| 2025-08-25T03:31:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-25T03:31:17Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/mdfprj9k?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
AnerYubo/blockassist-bc-gilded_patterned_mouse_1756092625
|
AnerYubo
| 2025-08-25T03:30:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gilded patterned mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:30:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gilded patterned mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-shaggy_melodic_cobra_1756092616
|
AnerYubo
| 2025-08-25T03:30:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shaggy melodic cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:30:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shaggy melodic cobra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
somnathbanerjee2024/mosaicai-gemma-real-estate-finetune
|
somnathbanerjee2024
| 2025-08-25T03:30:06Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:google/gemma-3-12b-pt",
"base_model:finetune:google/gemma-3-12b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T20:47:56Z |
---
base_model: google/gemma-3-12b-pt
library_name: transformers
model_name: mosaicai-gemma-real-estate-finetune
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for mosaicai-gemma-real-estate-finetune
This model is a fine-tuned version of [google/gemma-3-12b-pt](https://huggingface.co/google/gemma-3-12b-pt).
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="somnathbanerjee2024/mosaicai-gemma-real-estate-finetune", 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: 4.53.3
- 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}}
}
```
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756092513
|
kapalbalap
| 2025-08-25T03:29:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:29:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unitova/blockassist-bc-zealous_sneaky_raven_1756090867
|
unitova
| 2025-08-25T03:27:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:27:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756092345
|
kapalbalap
| 2025-08-25T03:26:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:26:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crystalline7/1797452
|
crystalline7
| 2025-08-25T03:25:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-25T03:25:58Z |
[View on Civ Archive](https://civarchive.com/models/1676821?modelVersionId=1897912)
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1756090423
|
sampingkaca72
| 2025-08-25T03:22:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:21:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1756090631
|
rvipitkirubbe
| 2025-08-25T03:21:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:21:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mottled foraging ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1756090883
|
Sayemahsjn
| 2025-08-25T03:20:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:20:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
uppal-farm-girl-original-video-viral-links/New.full.videos.uppal.farm.girl.Viral.Video.Official.Tutorial
|
uppal-farm-girl-original-video-viral-links
| 2025-08-25T03:20:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-25T03:20:38Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/mdfprj9k?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
nightmedia/Sugoi-14B-Ultra-HF-qx6-hi-mlx
|
nightmedia
| 2025-08-25T03:19:44Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen2",
"text-generation",
"translation",
"transformers",
"conversational",
"ja",
"en",
"base_model:sugoitoolkit/Sugoi-14B-Ultra-HF",
"base_model:quantized:sugoitoolkit/Sugoi-14B-Ultra-HF",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"6-bit",
"region:us"
] |
text-generation
| 2025-08-25T02:13:34Z |
---
license: apache-2.0
language:
- ja
- en
base_model: sugoitoolkit/Sugoi-14B-Ultra-HF
tags:
- translation
- transformers
- mlx
pipeline_tag: text-generation
library_name: mlx
---
# Sugoi-14B-Ultra-HF-qx6-hi-mlx
This model [Sugoi-14B-Ultra-HF-qx6-hi-mlx](https://huggingface.co/Sugoi-14B-Ultra-HF-qx6-hi-mlx) was
converted to MLX format from [sugoitoolkit/Sugoi-14B-Ultra-HF](https://huggingface.co/sugoitoolkit/Sugoi-14B-Ultra-HF)
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("Sugoi-14B-Ultra-HF-qx6-hi-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)
```
|
alexrzem/malbork
|
alexrzem
| 2025-08-25T03:19:31Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:mit",
"region:us"
] |
text-to-image
| 2025-08-25T03:19:13Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/Z89ngQoLR_45Fkce_6cG8_ab13d556a1234bacb7fdb73f20a3f2a6.jpg
text: Malbork Castle
- output:
url: images/y7p_N0j13e0CkmxBIT0W0_8908a086023f4c659dedcbfb2583fda6.jpg
text: Malbork Castle
- output:
url: images/OJG-IA-mIGZt4KKNZdXy6_bb275c2aa1234dc5a56cb64149cc0e4f.jpg
text: Malbork Castle
- output:
url: images/gufTGvVj9rtCQHCea9Gn1_8623f0c593f74685af1daa99d413515e.jpg
text: Malbork Castle
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: malbork
license: mit
---
# Malbork Castle
<Gallery />
## Model description
Malbork Castle
## Trigger words
You should use `malbork` to trigger the image generation.
## Download model
[Download](/alexrzem/malbork/tree/main) them in the Files & versions tab.
|
zuruyu/blockassist-bc-endangered_pesty_chinchilla_1756091776
|
zuruyu
| 2025-08-25T03:17:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"endangered pesty chinchilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:17:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- endangered pesty chinchilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756091787
|
kapalbalap
| 2025-08-25T03:17:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:17:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1756091740
|
roeker
| 2025-08-25T03:16:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:16:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756091623
|
kapalbalap
| 2025-08-25T03:14:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:14:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
marhanazaor/blockassist-bc-diving_crested_rabbit_1756091449
|
marhanazaor
| 2025-08-25T03:11:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"diving crested rabbit",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:11:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- diving crested rabbit
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hiepnt900514/blockassist-bc-clawed_singing_okapi_1756090458
|
hiepnt900514
| 2025-08-25T03:11:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"clawed singing okapi",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:10:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- clawed singing okapi
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1756089644
|
koloni
| 2025-08-25T03:06:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:06:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lemonhat/Qwen2.5-7B-Instruct-t1_5k_v1_tag5
|
lemonhat
| 2025-08-25T03:05:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T02:53:44Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: t1_5k_v1_tag5
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. -->
# t1_5k_v1_tag5
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the t1_5k_v1_tag5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2257
## 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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 8
- 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
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3375 | 0.4016 | 100 | 0.2458 |
| 0.2934 | 0.8032 | 200 | 0.2288 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
indoempatnol/blockassist-bc-fishy_wary_swan_1756089318
|
indoempatnol
| 2025-08-25T03:03:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:03:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756090850
|
kapalbalap
| 2025-08-25T03:01:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T03:01:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
luvnpce83/ancient-greek-emotion-bert
|
luvnpce83
| 2025-08-25T02:55:44Z | 16 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"emotion-analysis",
"koine-greek",
"biblical-studies",
"digital-humanities",
"grc",
"base_model:pranaydeeps/Ancient-Greek-BERT",
"base_model:finetune:pranaydeeps/Ancient-Greek-BERT",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-22T22:52:27Z |
---
license: apache-2.0
language:
- grc
library_name: transformers
tags:
- text-classification
- emotion-analysis
- koine-greek
- biblical-studies
- digital-humanities
base_model: pranaydeeps/Ancient-Greek-BERT
---
# Ancient Greek Emotion BERT
This is a model fine-tuned from `pranaydeeps/Ancient-Greek-BERT` for 8-class emotion classification on Koine Greek. The model predicts one of eight basic emotions based on Plutchik’s Wheel of Emotions: **Joy, Trust, Fear, Surprise, Sadness, Disgust, Anger, and Anticipation**.
This model was developed as part of the doctoral dissertation "Emotions in the Identity of Paul: A Sentiment Analysis Approach to Paul’s Jewish Identity" at Yonsei University.
## Model Description
This model is designed to perform sentence-level emotion analysis on Koine Greek texts, particularly those from the Pauline Epistles and related literature. It was trained on a custom-built dataset derived from the Louw-Nida lexicon and augmented using modern NLP techniques to overcome the challenges of a low-resource language.
### Base Model
The fine-tuning process started from the `pranaydeeps/Ancient-Greek-BERT` model, a BERT model pre-trained specifically on a large corpus of Ancient Greek texts. Utilizing this specialized base model was crucial for achieving high performance on this downstream task.
## Intended Use & Limitations
### Intended Use
This model is intended for academic research in Digital Humanities, Biblical Studies, Classics, and Linguistics for analyzing emotional expression in Koine Greek texts. It can be used to generate quantitative data on the emotional valence of sentences for large-scale textual analysis.
### Limitations
- **Domain Specificity**: The model was trained on a dataset primarily derived from the New Testament. Its performance on other genres of Koine Greek (e.g., philosophy, history, poetry) has not been evaluated and may be suboptimal.
- **No Absolutes**: Emotion is subjective and context-dependent. The model's predictions should be used as a tool for analysis, not as a definitive judgment of a text's emotional content.
- **Potential Bias**: The model reflects the emotional expressions present in its training data, which is sourced from ancient religious texts. It may carry the biases inherent in that source material.
## How to Use
The easiest way to use this model is with a `pipeline` from the 🤗 Transformers library.
```python
from transformers import pipeline
# Replace "YourUsername/your-model-name" with your actual model ID
classifier = pipeline("text-classification", model="luvnpce83/ancient-greek-emotion-bert")
result = classifier("ὦ ἀνόητοι Γαλάται")
print(result)
# [{'label': 'Anger', 'score': 0.8521...}]
```
## Training Data
The model was trained on a custom dataset of 2,616 annotated Koine Greek sentences. The creation process involved:
1. **Initial Curation**: A "golden standard" corpus of 884 samples was manually created based on the semantic domains of the Louw-Nida lexicon.
2. **Data Augmentation**: The dataset was expanded using back-translation and generative augmentation via a large language model to enhance robustness.
## Training Procedure
The model was fully fine-tuned from a `bert-base-cased` checkpoint. Key hyperparameters include:
- **Learning Rate**: 5e-5
- **Batch Size**: 64
- **Optimizer**: AdamW
- **Epochs**: 12 (with early stopping, patience=5)
- **Max Sequence Length**: 512
The training was managed and logged using Weights & Biases.
## Evaluation Results
The model achieved a **Macro F1 score of 0.680** on the held-out validation set.
## Citation
If you use this model in your research, please cite the following dissertation:
```bibtex
@phdthesis{kang2025emotions,
author = {Kang, Young Un},
title = {Emotions in the Identity of Paul: A Sentiment Analysis Approach to Paul’s Jewish Identity},
school = {School of Theology, Yonsei University},
year = {2025}
}
```
|
roeker/blockassist-bc-quick_wiry_owl_1756090373
|
roeker
| 2025-08-25T02:54:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:53:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AbstractPhil/pentachora-multi-channel-frequency-encoded
|
AbstractPhil
| 2025-08-25T02:50:16Z | 0 | 0 | null |
[
"tensorboard",
"chemistry",
"biology",
"art",
"zero-shot-classification",
"license:apache-2.0",
"region:us"
] |
zero-shot-classification
| 2025-08-17T12:39:41Z |
---
license: apache-2.0
pipeline_tag: zero-shot-classification
tags:
- chemistry
- biology
- art
---
# Pentachora Adaptive Encoded (Multi-Channel) - NOTEBOOK 2 of 5
**A geometry-regularized classifier with a 5-frequency encoder and pentachoron constellation heads.**
*Author:* **AbstractPhil** · *Quartermaster:* **Mirel** · GPT 4o - GPT 5 - GPT 5 Fast - GPT 5 Thinking - GPT 5 Pro
*Assistants:* Claude Opus 4.1 - Claude Sonnet 4 - Gemini 2.5
*License:* **Apache-2.0**
---
## 📌 TL;DR
This repository hosts training runs of a **frequency-aware encoder** (PentaFreq) paired with a **pentachoron constellation classifier** (dispatchers + specialists). The model blends classic cross-entropy with **two contrastive objectives** (dual InfoNCE and **ROSE-weighted** InfoNCE) and a **geometric regularizer** that keeps the learned vertex geometry sane.
It supports **1-channel and 3-channel** 28×28 inputs (e.g., TorchVision MNIST variants and MedMNIST 2D sets), is **seeded/deterministic**, and ships full artifacts (weights, plots, history, TensorBoard) for review.
---
## Authors Notes
- Yes I am human, and this is an AI generated model card so it's probably going to be a little inaccurate. It just looks better than mine would look.
- This is design 2 of 5, the AI seems to always forget - so a reminder ahead of this because I probably won't edit it later. It has some odd stuff that doesn't matter, because this isn't the best one.
- Cataloging this model is important nonetheless, as it's a stepping stone to the more powerful geometric crystalization collective.
- I will include all cites to the adjacent papers used for the mathematics, model weights, inspirations, and test methodologies implemented at a later time.
- I appreciate every single contributor to this - direct or indirect - through your invaluable contributions to science that manifested in utilizable AI form.
- I have included the training notebook as train_notebook.ipynb - which shows the deterministic setup, the weights, the loss methods, and an absolute ton of random functions that I let the AIs monkey patch in because it's faster than trying to teach AI 15 classes in 15 files.
- The patterns on this one struggle based on certain pentachora overlap which is why it had to be rewritten again.
- The deterministic and non-deterministic nature of the combination of utilities manifest quirks and behavior that are unexpected, which is why the deterministic version is required.
- Strict determinism can be enabled for a more robust and accurate recreation but I may have missed some seed any points in this earlier notebook.
## 🧠 Model overview
### Architecture
- **PentaFreq Encoder (multi-channel)**
- 5 spectral branches (ultra-high, high, mid, low-mid, low) → per-branch encoders → cross-attention → MLP fusion → **normalized latent `z`**.
- Channel-aware: supports **C ∈ {1,3}**; input is flattened to `C×28×28`.
- **Pentachoron Constellation Classifier**
- **Two stacks** (dispatchers & specialists) each containing **pentachora** (5-vertex simplices) with learnable vertices.
- **Coherence gate** modulates vertex logits; **group heads** (one per vertex) score class subsets; **pair aggregation** + fusion MLP produce final logits.
- Geometry terms encourage valid simplex structure and separation between the two stacks.
### Objective
- **CE** – main cross-entropy on logits.
- **Dual InfoNCE (stable)** – encourages `z` to match the **correct vertex** across both stacks.
- **ROSE-weighted InfoNCE (stable)** – same idea, but reweights samples by an analytic **ROSE** similarity (triadic cosine + magnitude).
- **Geometry Regularization** – stable Cayley–Menger **proxy** (eigval-based), edge-variance, center separation, and a **soft radius control**; ramped in early epochs.
> All contrastive losses use `log_softmax` + `gather` to avoid `inf−inf` traps; all paths **nan-sanitize** defensively.
### Determinism
- Global seeding (Python/NumPy/Torch), deterministic DataLoader workers, generator-seeded samplers; cuDNN deterministic & TF32 off.
- Optional strict mode (`torch.use_deterministic_algorithms(True)`) and deterministic cuBLAS.
---
## 🗂️ Repository layout per run
Each training run uploads a complete bundle at:
```
<repo>/<root>/<DatasetName>/<Timestamp_or_best>/
weights/
encoder[_<Dataset>].safetensors
constellation[_<Dataset>].safetensors
diagnostic_head[_<Dataset>].safetensors
config.json # exact config used
manifest.json # env, params, dataset, best metrics
history.json / history.csv
tensorboard/ (+ zip)
plots/ # accuracy, loss components, lambda, confusion matrices
```
> We also optionally publish a **`best/`** alias inside each dataset folder pointing to the current champion.
---
## 🧩 Intended use & use cases
**Intended use**: research-grade supervised classification and geometry-regularized representation learning on small images (28×28) across gray and color channels.
**Example use cases**
- **Benchmarking** on MNIST family / MedMNIST 2D sets with defensible, reproducible training and complete artifacts.
- **Geometry-aware representation learning**: analyze how simplex vertices move, how the gate allocates probability mass, and how geometry regularization affects generalization.
- **Class routing / specialization**: per-vertex group heads provide an interpretable split of classes; confusion-driven vertex reweighting helps diagnose hard groups.
- **Curriculum & loss ablations**: toggle ROSE, dual InfoNCE, or geometry terms to study their marginal value under a controlled seed.
- **OOD “pressure tests”** (research): ROSE magnitude and routing entropy can be used as quick signals of uncertainty (not calibrated).
- **Education & reproducibility**: the runs are fully seeded, include TensorBoard logs and plots, and use safe numerical formulations.
---
## 🚫 Out-of-scope / limitations
- **Not a medical device** – even if trained on MedMNIST subsets, this is not a diagnostic tool. Don’t use it for clinical decisions.
- **Input size** is 28×28; higher-resolution domains require retraining and likely architecture tweaks.
- **Dataset bias / shift** – performance depends on the underlying distribution. Evaluate before deployment.
- **Calibration** – logits are not guaranteed calibrated. For decision thresholds, use a validation set or post-hoc calibration.
- **Robustness** – robustness to adversarial perturbations is not a design goal here.
---
## 📈 Example results (single-seed snapshots)
> Numbers below are indicative from our seeded runs with `img_size=28`, size-aware LR schedule and reg ramp; see `manifest.json` in each run for exact details.
| Dataset | C | Best Test Acc | Epoch | Notes |
|----------------|---|---------------:|------:|--------------------------------------|
| MNIST/Fashion* | 1 | 0.97–0.98 | 15–25 | stable losses + reg ramp |
| BloodMNIST | 3 | ~0.95–0.97+ | 20–30 | color preserved, 28×28 |
| EMNIST (bal) | 1 | 0.88–0.92 | 25–45 | many classes; pairs auto-scaled |
\* depending on which of the pair (MNIST / FashionMNIST) is selected.
Consult each dataset folder’s `history.csv` for the full learning curve and the **current best** accuracy.
---
## 🔧 How to use (PyTorch)
```python
import torch
from safetensors.torch import load_file as load_safetensors
# --- load weights (example path) ---
ENC = "weights/encoder_MNIST.safetensors"
CON = "weights/constellation_MNIST.safetensors"
DIA = "weights/diagnostic_head_MNIST.safetensors"
# Recreate model classes (identical definitions to the notebook)
encoder = PentaFreqEncoderV2(input_dim=28*28, input_ch=1, base_dim=56, num_heads=2, channels=12)
constellation = BatchedPentachoronConstellation(num_classes=10, dim=56, num_pairs=5, lambda_sep=0.391)
diag = RoseDiagnosticHead(56)
encoder.load_state_dict(load_safetensors(ENC))
constellation.load_state_dict(load_safetensors(CON))
diag.load_state_dict(load_safetensors(DIA))
encoder.eval(); constellation.eval()
# --- dummy inference ---
# x: [B, C, H, W] converted to float tensor in [0,1]; flatten to [B, C*H*W]
# use the same normalization as training if you want best performance
x = torch.rand(8, 1, 28, 28)
x_flat = x.view(x.size(0), -1)
with torch.no_grad():
z = encoder(x_flat) # [B, D]
logits, diag_out = constellation(z) # [B, C]
pred = logits.argmax(dim=1)
print(pred)
```
> To reproduce training, see `config.json` and `history.csv`; all recipes are encoded in the flagship notebook used for these runs.
---
## 🔬 Training procedure (default)
- **Optimizer**: AdamW (β1=0.9, β2=0.999), size-aware LR (≈2e-2 by default)
- **Schedule**: 10% **warmup** → cosine to `lr_min=1e-6`
- **Batch size**: up to 2048 (fits on T4/A100 at 28×28)
- **Loss**: CE + Dual InfoNCE + ROSE InfoNCE + Geometry Reg (ramped) + Diag MSE
- **Determinism**: seeds for Python/NumPy/Torch (CPU/GPU), deterministic DataLoader workers and samplers, cuDNN deterministic, TF32 off
- **Numerical safety**: log-softmax contrastive, eigval CM proxy, `nan_to_num` guards, optional step rollback if non-finite
---
## 📈 Evaluation
- Main metric: **top-1 accuracy** on the held-out test split defined by each dataset.
- Diagnostics we log:
- **Routing entropy** and vertex probabilities
- **ROSE** magnitudes
- Confusion matrices (per epoch and “best”)
- λ (geometry ↔ attention gate) over epochs
- Full loss decomposition
---
## 🔭 Potential for growth
- **Hypercube Constellations** (shipped classes in the notebook): scale from 4-simplex to n-cube graphs; compare geometry families.
- **Multi-resolution** (56→128→256 latent; 28→64→128 images); add pyramid encoders.
- **Self-distillation / semi-supervised**: use ROSE as a confidence-weighted pseudo-labeling signal.
- **Better routing**: learned vertex priors per class, entropy regularization, temperature schedules.
- **Calibration & OOD**: temperature scaling / Dirichlet heads; exploit ROSE magnitude and gating entropy for improved uncertainty estimates.
- **Deployment adapters**: ONNX / TorchScript exports; small mobile variants of PentaFreq.
---
## ⚖️ Ethical considerations & implications
- **Clinical datasets** (MedMNIST) are simplified proxies; they don’t reflect clinical complexity or demographic coverage.
- **Downstream use** must include dataset-appropriate validation and calibration; this model is for **research** only.
- **Data bias** and **label noise** can be amplified by strong geometry priors—review confusion matrices and per-class accuracies before claiming improvements.
- **Positive implications**: the constellation design offers a **transparent, analyzable structure** (per-vertex heads, explicit geometry), easing **interpretability** and **ablation**.
---
## 🔁 Reproducibility
- `config.json` contains all hyperparameters used for each run.
- `manifest.json` logs environment: Python, Torch, CUDA GPU, RAM, parameter counts.
- Seeds and determinism flags are printed in logs and set in code.
- `history.csv` + TensorBoard fully specify the learning trajectory.
---
## 🧾 License
**Apache License 2.0** – see `LICENSE`.
---
## 📣 Citation
If you use this work, please cite:
```
@software{abstractphil_pentachora_2025,
author = {AbstractPhil and Mirel},
title = {Pentachora Adaptive Encoded: Geometry-Regularized Classification with PentaFreq},
year = {2025},
license = {Apache-2.0},
url = {https://huggingface.co/AbstractPhil/pentachora-multi-channel-frequency-encoded}
}
```
---
## 🛠️ Changelog (excerpt)
- **2025-08**: Flagship notebook stabilized (stable losses, eigval CM proxy, NaN rollback, deterministic sweep).
- **2025-08**: Multi-channel PentaFreq; per-dataset HF folders with full artifacts; optional `best/` alias.
- **2025-08**: Hypercube constellation classes added for follow-up experiments.
---
## 💬 Contact
- **Author:** @AbstractPhil
- **Quartermaster:** Mirel (ChatGPT – GPT-5 Thinking)
- **Issues / questions:** open a Discussion on the HF repo or ping the author
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1756089877
|
IvanJAjebu
| 2025-08-25T02:45:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:45:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
talzoomanzoo/deepseek-1.5b-lowest-uid-variance-ds-1.5b
|
talzoomanzoo
| 2025-08-25T02:45:20Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"arxiv:1910.09700",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-08-24T23:32:15Z |
---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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
|
LUKART1234/gemma3-270m-json-fine-tuned-create-action
|
LUKART1234
| 2025-08-25T02:41:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-270m-it",
"base_model:finetune:unsloth/gemma-3-270m-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T02:41:04Z |
---
base_model: unsloth/gemma-3-270m-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** LUKART1234
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-270m-it
This gemma3_text 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)
|
8septiadi8/blockassist-bc-curious_lightfooted_mouse_1756089218
|
8septiadi8
| 2025-08-25T02:35:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"curious lightfooted mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:35:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- curious lightfooted mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
masakinoda/so101-tutorial-eraser-30-act-10ep-1
|
masakinoda
| 2025-08-25T02:30:01Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:masakinoda/so101-tutorial-eraser-30",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-25T02:29:47Z |
---
datasets: masakinoda/so101-tutorial-eraser-30
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
|
baijadeja67/blockassist-bc-deadly_sharp_beaver_1756088944
|
baijadeja67
| 2025-08-25T02:29:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly sharp beaver",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:29:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly sharp beaver
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
indoempatnol/blockassist-bc-fishy_wary_swan_1756087345
|
indoempatnol
| 2025-08-25T02:29:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:29:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756088835
|
kapalbalap
| 2025-08-25T02:27:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:27:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unsloth/gpt-oss-120b-GGUF
|
unsloth
| 2025-08-25T02:27:41Z | 199,146 | 124 |
transformers
|
[
"transformers",
"gguf",
"gpt_oss",
"text-generation",
"openai",
"unsloth",
"base_model:openai/gpt-oss-120b",
"base_model:quantized:openai/gpt-oss-120b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"mxfp4",
"region:us",
"conversational"
] |
text-generation
| 2025-08-05T17:11:45Z |
---
base_model:
- openai/gpt-oss-120b
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- openai
- unsloth
---
> [!NOTE]
> The F16 quant is gpt-oss in its **original** precision. All GGUFs have our fixes. [Read our guide here.](https://docs.unsloth.ai/basics/gpt-oss)
>
<div>
<p style="margin-bottom: 0; margin-top: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/gpt-oss-6892433695ce0dee42f31681">our collection</a> for all versions of gpt-oss including GGUF, 4-bit & 16-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Learn to run gpt-oss correctly - <a href="https://docs.unsloth.ai/basics/gpt-oss">Read our Guide</a>.</em>
</p>
<p style="margin-top: 0;margin-bottom: 0;">
<em>See <a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0 GGUFs</a> for our quantization benchmarks.</em>
</p>
<div style="display: flex; gap: 5px; align-items: center; ">
<a href="https://github.com/unslothai/unsloth/">
<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
</a>
<a href="https://discord.gg/unsloth">
<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
</a>
<a href="https://docs.unsloth.ai/basics/gpt-oss">
<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
</a>
</div>
<h1 style="margin-top: 0rem;">✨ Read our gpt-oss Guide <a href="https://docs.unsloth.ai/basics/gpt-oss">here</a>!</h1>
</div>
- Read our Blog about gpt-oss support: [unsloth.ai/blog/gpt-oss](https://unsloth.ai/blog/gpt-oss)
- View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks).
- Thank you to the [llama.cpp](https://github.com/ggml-org/llama.cpp) team for their work on supporting this model. We wouldn't be able to release quants without them!
# gpt-oss-120b Details
<p align="center">
<img alt="gpt-oss-120b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-120b.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>System 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 the open models:
- `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fits 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 larger `gpt-oss-120b` model. Check out [`gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) for the smaller 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-120b"
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-120b
```
[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-120b
```
[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-120b
ollama pull gpt-oss:120b
ollama run gpt-oss:120b
```
[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-120b
lms get openai/gpt-oss-120b
```
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-120b
huggingface-cli download openai/gpt-oss-120b --include "original/*" --local-dir gpt-oss-120b/
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 larger model `gpt-oss-120b` can be fine-tuned on a single H100 node, whereas the smaller [`gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) can even be fine-tuned on consumer hardware.
|
cixzer/blockassist-bc-gregarious_long_cheetah_1756088377
|
cixzer
| 2025-08-25T02:24:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gregarious long cheetah",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:23:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gregarious long cheetah
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unitova/blockassist-bc-zealous_sneaky_raven_1756086939
|
unitova
| 2025-08-25T02:23:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:23:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggmancer/blockassist-bc-reclusive_keen_marmot_1756087358
|
ggmancer
| 2025-08-25T02:20:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive keen marmot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:20:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive keen marmot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lautan/blockassist-bc-gentle_patterned_goat_1756086888
|
lautan
| 2025-08-25T02:20:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle patterned goat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:20:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle patterned goat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756088320
|
kapalbalap
| 2025-08-25T02:19:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:19:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ngocdb0994/blockassist-bc-skittish_docile_bat_1756087367
|
ngocdb0994
| 2025-08-25T02:19:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"skittish docile bat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:19:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- skittish docile bat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kznmp3/blockassist-bc-lively_raging_hippo_1756088209
|
kznmp3
| 2025-08-25T02:18:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lively raging hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:17:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lively raging hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756088147
|
kapalbalap
| 2025-08-25T02:16:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:16:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eliasab16/smolvla_insert_wire_test_prompt_2
|
eliasab16
| 2025-08-25T02:16:41Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:eliasab16/insert_wire_prompt_2",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-25T02:16:32Z |
---
base_model: lerobot/smolvla_base
datasets: eliasab16/insert_wire_prompt_2
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- robotics
- lerobot
- smolvla
---
# 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
lerobot-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
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
|
GeneroGral/Qwen2.5-7B_BBQ_Stereo_MERGED4_dropout_batch-wordMatch_FINAL
|
GeneroGral
| 2025-08-25T02:15:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T02:12:15Z |
---
base_model: unsloth/Qwen2.5-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** GeneroGral
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B-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)
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756087978
|
kapalbalap
| 2025-08-25T02:13:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:13:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756087944
|
liukevin666
| 2025-08-25T02:13:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T02:13:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BootesVoid/cmdz0jjnz02iggwtcjkebufwk_cmeqg4fx10bggtlqb7vb0pg38
|
BootesVoid
| 2025-08-25T02:11:32Z | 0 | 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
| 2025-08-25T02:11:31Z |
---
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: VALENTINAFLOREZX21
---
# Cmdz0Jjnz02Iggwtcjkebufwk_Cmeqg4Fx10Bggtlqb7Vb0Pg38
<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 `VALENTINAFLOREZX21` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "VALENTINAFLOREZX21",
"lora_weights": "https://huggingface.co/BootesVoid/cmdz0jjnz02iggwtcjkebufwk_cmeqg4fx10bggtlqb7vb0pg38/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('BootesVoid/cmdz0jjnz02iggwtcjkebufwk_cmeqg4fx10bggtlqb7vb0pg38', weight_name='lora.safetensors')
image = pipeline('VALENTINAFLOREZX21').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: 2500
- Learning rate: 9e-05
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmdz0jjnz02iggwtcjkebufwk_cmeqg4fx10bggtlqb7vb0pg38/discussions) to add images that show off what you’ve made with this LoRA.
|
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