Datasets:
librarian-bots
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model_cards_with_metadata

Dataset card Data Studio Files
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2025-09-02 06:30:45
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Jorgeis1/jorge-babylm-10m-gptbert
Jorgeis1
2025-09-02T00:44:04Z
51
0
transformers
[ "transformers", "safetensors", "gpt-bert", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]
text-generation
2025-08-10T22:33:55Z
--- 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]
John6666/comradeship-xl-sdxl-v14vww-sdxl
John6666
2025-09-02T00:42:06Z
0
0
diffusers
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "hentai", "girls", "improving high-resolution generation", "natural language prompt", "merge", "v-pred", "noobai", "Illustrious XL v2.0", "illustrious", "en", "base_model:hanzogak/comradeshipXL", "base_model:finetune:hanzogak/comradeshipXL", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]
text-to-image
2025-09-02T00:33:38Z
--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - hentai - girls - improving high-resolution generation - natural language prompt - merge - v-pred - noobai - Illustrious XL v2.0 - illustrious base_model: hanzogak/comradeshipXL --- Original model is [here](https://huggingface.co/hanzogak/comradeshipXL) and on [Civitai](https://civitai.com/models/246299/comradeship-xl-sdxl?modelVersionId=2171404). The author is [here](https://huggingface.co/hanzogak). This model created by [polyx](https://civitai.com/user/polyx).
seraphimzzzz/137746
seraphimzzzz
2025-09-02T00:41:28Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:41:28Z
[View on Civ Archive](https://civarchive.com/models/159928?modelVersionId=179886)
mradermacher/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor-GGUF
mradermacher
2025-09-02T00:41:25Z
411
2
transformers
[ "transformers", "gguf", "en", "base_model:Liontix/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor", "base_model:quantized:Liontix/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor", "endpoints_compatible", "region:us", "conversational" ]
null
2025-08-30T00:41:50Z
--- base_model: Liontix/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/Liontix/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor <!-- provided-files --> ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor-GGUF).*** weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor-GGUF/resolve/main/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor.Q2_K.gguf) | Q2_K | 1.8 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor-GGUF/resolve/main/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor.Q3_K_S.gguf) | Q3_K_S | 2.0 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor-GGUF/resolve/main/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor-GGUF/resolve/main/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor.Q3_K_L.gguf) | Q3_K_L | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor-GGUF/resolve/main/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor.IQ4_XS.gguf) | IQ4_XS | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor-GGUF/resolve/main/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor-GGUF/resolve/main/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor-GGUF/resolve/main/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor.Q5_K_S.gguf) | Q5_K_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor-GGUF/resolve/main/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor.Q5_K_M.gguf) | Q5_K_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor-GGUF/resolve/main/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor.Q6_K.gguf) | Q6_K | 3.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor-GGUF/resolve/main/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor-GGUF/resolve/main/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor.f16.gguf) | f16 | 8.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
amethyst9/124819
amethyst9
2025-09-02T00:41:05Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:41:03Z
[View on Civ Archive](https://civarchive.com/models/147909?modelVersionId=165014)
vartersabin/blockassist-bc-downy_skittish_mandrill_1756773588
vartersabin
2025-09-02T00:40:41Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "downy skittish mandrill", "arxiv:2504.07091", "region:us" ]
null
2025-09-02T00:40:18Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - downy skittish mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
amethyst9/162218
amethyst9
2025-09-02T00:40:23Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:40:23Z
[View on Civ Archive](https://civarchive.com/models/188015?modelVersionId=211124)
crystalline7/375767
crystalline7
2025-09-02T00:40:14Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:40:14Z
[View on Civ Archive](https://civarchive.com/models/409401?modelVersionId=456337)
ultratopaz/1650989
ultratopaz
2025-09-02T00:39:49Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:39:49Z
[View on Civ Archive](https://civarchive.com/models/1546867?modelVersionId=1750271)
Mitchins/deberta-v3-s-plot-arc-classifier
Mitchins
2025-09-02T00:39:38Z
0
0
transformers
[ "transformers", "safetensors", "deberta-v2", "text-classification", "character-analysis", "plot-arc", "narrative-analysis", "deberta", "en", "dataset:custom/plot-arc-balanced-101k", "base_model:microsoft/deberta-v3-small", "base_model:finetune:microsoft/deberta-v3-small", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2025-09-02T00:17:14Z
--- # Model Card Metadata (YAML Front Matter) license: mit base_model: microsoft/deberta-v3-small tags: - text-classification - character-analysis - plot-arc - narrative-analysis - deberta - transformers language: en datasets: - custom/plot-arc-balanced-101k metrics: - accuracy - f1 - precision - recall model_type: sequence-classification pipeline_tag: text-classification widget: - text: "Sir Galahad embarks on a perilous quest to retrieve the stolen Crown of Ages." example_title: "External Arc Example" - text: "Maria struggles with crippling self-doubt after her mother's harsh words." example_title: "Internal Arc Example" - text: "Captain Torres must infiltrate enemy lines while battling his own cowardice." example_title: "Both Arc Example" - text: "A baker who makes bread every morning in his village shop." example_title: "No Arc Example" library_name: transformers --- # Plot Arc Classifier - DeBERTa Small A fine-tuned DeBERTa-v3-small model for classifying character plot arc types in narrative text. ## Model Details ### Model Description This model classifies character descriptions into four plot arc categories: - **NONE (0)**: No discernible character development or plot arc - **INTERNAL (1)**: Character growth driven by internal conflict/psychology - **EXTERNAL (2)**: Character arc driven by external events/missions - **BOTH (3)**: Character arc with both internal conflict and external drivers **Model Type:** Text Classification (Sequence Classification) **Base Model:** microsoft/deberta-v3-small (~60M parameters) **Language:** English **License:** MIT ### Model Architecture - **Base:** DeBERTa-v3-Small (60M parameters) - **Task:** 4-class sequence classification - **Input:** Character descriptions (max 512 tokens) - **Output:** Classification logits + probabilities for 4 classes ## Training Data ### Dataset Statistics - **Total Examples:** 101,348 - **Training Split:** 91,213 examples (90%) - **Validation Split:** 10,135 examples (10%) - **Perfect Class Balance:** 25,337 examples per class ### Data Sources - Systematic scanning of 1.8M+ character descriptions - LLM validation using Llama-3.2-3B for quality assurance - SHA256-based deduplication to prevent data leakage - Carefully curated and balanced dataset across all plot arc types ### Class Distribution | Class | Count | Percentage | |-------|-------|------------| | NONE | 25,337 | 25% | | INTERNAL | 25,337 | 25% | | EXTERNAL | 25,337 | 25% | | BOTH | 25,337 | 25% | ## Performance ### Key Metrics - **Accuracy:** 0.7286 - **F1 (Weighted):** 0.7283 - **F1 (Macro):** 0.7275 ### Per-Class Performance | Class | Precision | Recall | F1-Score | Support | |-------|-----------|--------|----------|---------| | NONE | 0.697 | 0.613 | 0.653 | 2,495 | | INTERNAL | 0.677 | 0.683 | 0.680 | 2,571 | | EXTERNAL | 0.892 | 0.882 | 0.887 | 2,568 | | BOTH | 0.652 | 0.732 | 0.690 | 2,501 | ### Training Details - **Training Time:** 9.7 hours on Apple Silicon MPS - **Final Training Loss:** 0.635 - **Epochs:** 3.86 (early stopping) - **Batch Size:** 16 (effective: 32 with gradient accumulation) - **Learning Rate:** 2e-5 with warmup - **Optimizer:** AdamW with weight decay (0.01) ## Confusion Matrix ![Confusion Matrix](images/confusion_matrix.png) ## Usage ### Basic Usage ```python from transformers import DebertaV2Tokenizer, DebertaV2ForSequenceClassification import torch # Load model and tokenizer model_name = "plot-arc-classifier-deberta-small" tokenizer = DebertaV2Tokenizer.from_pretrained(model_name) model = DebertaV2ForSequenceClassification.from_pretrained(model_name) # Example text text = "Sir Galahad embarks on a perilous quest to retrieve the stolen Crown of Ages." # Tokenize and predict inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512) with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=-1) predicted_class = torch.argmax(probabilities, dim=-1) # Class mapping class_names = ['NONE', 'INTERNAL', 'EXTERNAL', 'BOTH'] prediction = class_names[predicted_class.item()] confidence = probabilities[0][predicted_class].item() print(f"Predicted class: {prediction} (confidence: {confidence:.3f})") ``` ### Pipeline Usage ```python from transformers import pipeline classifier = pipeline( "text-classification", model="plot-arc-classifier-deberta-small", return_all_scores=True ) result = classifier("Captain Torres must infiltrate enemy lines while battling his own cowardice.") print(result) ``` ## Example Classifications | Class | Type | Example | Prediction | Confidence | |-------|------|---------|------------|------------| | **NONE** | Simple | *"Margaret runs the village bakery, making fresh bread every morning at 5 AM for the past thirty years."* | NONE ✅ | 0.997 | | **NONE** | Nuanced | *"Dr. Harrison performs routine medical check-ups with methodical precision, maintaining professional distance while patients share their deepest fears about mortality."* | NONE ⚠️ | 0.581 | | **INTERNAL** | Simple | *"Emma struggles with overwhelming anxiety after her father's harsh criticism, questioning her self-worth and abilities."* | INTERNAL ✅ | 0.983 | | **INTERNAL** | Nuanced | *"The renowned pianist Clara finds herself paralyzed by perfectionism, her childhood trauma surfacing as she prepares for the performance that could define her legacy."* | INTERNAL ✅ | 0.733 | | **EXTERNAL** | Simple | *"Knight Roderick embarks on a dangerous quest to retrieve the stolen crown from the dragon's lair."* | EXTERNAL ✅ | 0.717 | | **EXTERNAL** | Nuanced | *"Master thief Elias infiltrates the heavily guarded fortress, disabling security systems and evading patrol routes, each obstacle requiring new techniques and tools to reach the vault."* | EXTERNAL ✅ | 0.711 | | **BOTH** | Simple | *"Sarah must rescue her kidnapped daughter from the terrorist compound while confronting her own paralyzing guilt about being an absent mother."* | BOTH ⚠️ | 0.578 | | **BOTH** | Nuanced | *"Archaeologist Sophia discovers an ancient artifact that could rewrite history, but must confront her own ethical boundaries and childhood abandonment issues as powerful forces try to silence her."* | BOTH ✅ | 0.926 | **Results:** 8/8 correct predictions (100% accuracy) ## Limitations - **Domain:** Optimized for character descriptions in narrative fiction - **Length:** Maximum 512 tokens (longer texts are truncated) - **Language:** English only - **Context:** Works best with character-focused descriptions rather than plot summaries - **Ambiguity:** Some edge cases may be inherently ambiguous between INTERNAL/BOTH ## Ethical Considerations - **Bias:** Training data may contain genre/cultural biases toward certain character archetypes - **Interpretation:** Classifications reflect Western narrative theory; other storytelling traditions may not map perfectly - **Automation:** Should complement, not replace, human literary analysis ## Citation ```bibtex @model{plot_arc_classifier_2025, title={Plot Arc Classifier - DeBERTa Small}, author={Claude Code Assistant}, year={2025}, url={https://github.com/your-org/plot-arc-classifier}, note={Fine-tuned DeBERTa-v3-small for character plot arc classification} } ``` ## Model Card Contact For questions about this model, please open an issue in the repository or contact the maintainers. --- *Model trained on 2025-09-02 using transformers library.*
sekirr/blockassist-bc-masked_tenacious_whale_1756773533
sekirr
2025-09-02T00:39:34Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "masked tenacious whale", "arxiv:2504.07091", "region:us" ]
null
2025-09-02T00:39:30Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - masked tenacious whale --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
crystalline7/375757
crystalline7
2025-09-02T00:39:32Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:39:32Z
[View on Civ Archive](https://civarchive.com/models/409392?modelVersionId=456326)
ultratopaz/1895282
ultratopaz
2025-09-02T00:39:24Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:39:24Z
[View on Civ Archive](https://civarchive.com/models/1765680?modelVersionId=1998224)
omerbektass/blockassist-bc-insectivorous_bold_lion_1756773526
omerbektass
2025-09-02T00:39:10Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous bold lion", "arxiv:2504.07091", "region:us" ]
null
2025-09-02T00:39:05Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous bold lion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
yoppertiu/blockassist-bc-placid_wily_locust_1756773531
yoppertiu
2025-09-02T00:39:05Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "placid wily locust", "arxiv:2504.07091", "region:us" ]
null
2025-09-02T00:38:52Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - placid wily locust --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
seraphimzzzz/503988
seraphimzzzz
2025-09-02T00:39:00Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:38:59Z
[View on Civ Archive](https://civarchive.com/models/529829?modelVersionId=588745)
Doramong/package
Doramong
2025-09-02T00:38:54Z
0
0
null
[ "region:us" ]
null
2025-03-10T12:43:50Z
``` import json import copy from PIL import Image from pypdf import PdfReader from vllm import LLM, SamplingParams from ocrflux.image_utils import get_page_image from ocrflux.table_format import table_matrix2html from ocrflux.prompts import PageResponse, build_page_to_markdown_prompt, build_element_merge_detect_prompt, build_html_table_merge_prompt import requests import base64 from io import BytesIO from PIL import Image import httpx import asyncio def pil_to_base64(img: Image.Image, format: str = "PNG") -> str: buffered = BytesIO() img.save(buffered, format=format) img_bytes = buffered.getvalue() img_base64 = base64.b64encode(img_bytes).decode("utf-8") return img_base64 async def get_response(messages, temperature): url = "http://127.0.0.1:8000/v1/chat/completions" headers = {"Content-Type": "application/json"} payload = { "model": "ChatDOC/OCRFlux-3B", "temperature": temperature, "messages": messages, "stream": False, "max_tokens": 4096, } timeout = httpx.Timeout(60.0) # 전체 요청 제한 시간: 60초 async with httpx.AsyncClient(timeout=timeout) as client: response = await client.post(url, json=payload, headers=headers) response.raise_for_status() return response.json()["choices"][0]['message']['content'] def build_qwen2_5_vl_prompt(question): messages = [] messages.append({"role":"system", "content":"You are a helpful assistant."}) messages.append({"role":"user", "content":[{"type":"text", "text":f"<|vision_start|><|image_pad|><|vision_end|>{question}"}]}) return messages def build_page_to_markdown_query(file_path: str, page_number: int, target_longest_image_dim: int = 1024, image_rotation: int = 0) -> dict: assert image_rotation in [0, 90, 180, 270], "Invalid image rotation provided in build_page_query" image = get_page_image(file_path, page_number, target_longest_image_dim=target_longest_image_dim, image_rotation=image_rotation) question = build_page_to_markdown_prompt() prompt = build_qwen2_5_vl_prompt(question) prompt[-1]['content'].append({"type":"image_url","image_url": {"url":f"data:image/png;base64,{pil_to_base64(image)}"}}) return prompt def build_element_merge_detect_query(text_list_1,text_list_2) -> dict: image = Image.new('RGB', (28, 28), color='black') question = build_element_merge_detect_prompt(text_list_1,text_list_2) prompt = build_qwen2_5_vl_prompt(question) prompt[-1]['content'].append({"type":"image_url","image_url": {"url":f"data:image/png;base64,{pil_to_base64(image)}"}}) return prompt def build_html_table_merge_query(text_1,text_2) -> dict: image = Image.new('RGB', (28, 28), color='black') question = build_html_table_merge_prompt(text_1,text_2) prompt = build_qwen2_5_vl_prompt(question) prompt[-1]['content'].append({"type":"image_url","image_url": {"url":f"data:image/png;base64,{pil_to_base64(image)}"}}) return prompt def bulid_document_text(page_to_markdown_result, element_merge_detect_result, html_table_merge_result): page_to_markdown_keys = list(page_to_markdown_result.keys()) element_merge_detect_keys = list(element_merge_detect_result.keys()) html_table_merge_keys = list(html_table_merge_result.keys()) for page_1,page_2,elem_idx_1,elem_idx_2 in sorted(html_table_merge_keys,key=lambda x: -x[0]): page_to_markdown_result[page_1][elem_idx_1] = html_table_merge_result[(page_1,page_2,elem_idx_1,elem_idx_2)] page_to_markdown_result[page_2][elem_idx_2] = '' for page_1,page_2 in sorted(element_merge_detect_keys,key=lambda x: -x[0]): for elem_idx_1,elem_idx_2 in element_merge_detect_result[(page_1,page_2)]: if len(page_to_markdown_result[page_1][elem_idx_1]) == 0 or page_to_markdown_result[page_1][elem_idx_1][-1] == '-' or ('\u4e00' <= page_to_markdown_result[page_1][elem_idx_1][-1] <= '\u9fff'): page_to_markdown_result[page_1][elem_idx_1] = page_to_markdown_result[page_1][elem_idx_1] + '' + page_to_markdown_result[page_2][elem_idx_2] else: page_to_markdown_result[page_1][elem_idx_1] = page_to_markdown_result[page_1][elem_idx_1] + ' ' + page_to_markdown_result[page_2][elem_idx_2] page_to_markdown_result[page_2][elem_idx_2] = '' document_text_list = [] for page in page_to_markdown_keys: page_text_list = [s for s in page_to_markdown_result[page] if s] document_text_list += page_text_list return "\n\n".join(document_text_list) async def parse(file_path,skip_cross_page_merge=False,max_page_retries=0): sampling_params = SamplingParams(temperature=0.0,max_tokens=8192) if file_path.lower().endswith(".pdf"): try: reader = PdfReader(file_path) num_pages = reader.get_num_pages() except: return None else: num_pages = 1 # try: # Stage 1: Page to Markdown page_to_markdown_query_list = [build_page_to_markdown_query(file_path,page_num) for page_num in range(1, num_pages + 1)] # responses = [get_response(page_to_markdown_query, 0.0) for page_to_markdown_query in page_to_markdown_query_list] tasks = [ get_response(query, 0.0) for query in page_to_markdown_query_list ] responses = await asyncio.gather(*tasks) results = [response for response in responses] page_to_markdown_result = {} retry_list = [] for i,result in enumerate(results): try: json_data = json.loads(result) page_response = PageResponse(**json_data) natural_text = page_response.natural_text markdown_element_list = [] for text in natural_text.split('\n\n'): if text.startswith("<Image>") and text.endswith("</Image>"): pass elif text.startswith("<table>") and text.endswith("</table>"): try: new_text = table_matrix2html(text) except: new_text = text.replace("<t>","").replace("<l>","").replace("<lt>","") markdown_element_list.append(new_text) else: markdown_element_list.append(text) page_to_markdown_result[i+1] = markdown_element_list except: retry_list.append(i) attempt = 0 while len(retry_list) > 0 and attempt < max_page_retries: retry_page_to_markdown_query_list = [build_page_to_markdown_query(file_path,page_num) for page_num in retry_list] # retry_sampling_params = SamplingParams(temperature=0.1*attempt, max_tokens=8192) # responses = [get_response(retry_page_to_markdown_query, 0.1*attempt) for retry_page_to_markdown_query in retry_page_to_markdown_query_list] # responses = llm.generate(retry_page_to_markdown_query_list, sampling_params=retry_sampling_params) tasks = [ get_response(query, 0.1*attempt) for query in retry_page_to_markdown_query_list ] responses = await asyncio.gather(*tasks) results = [response for response in responses] next_retry_list = [] for i,result in zip(retry_list,results): try: json_data = json.loads(result) page_response = PageResponse(**json_data) natural_text = page_response.natural_text markdown_element_list = [] for text in natural_text.split('\n\n'): if text.startswith("<Image>") and text.endswith("</Image>"): pass elif text.startswith("<table>") and text.endswith("</table>"): try: new_text = table_matrix2html(text) except: new_text = text.replace("<t>","").replace("<l>","").replace("<lt>","") markdown_element_list.append(new_text) else: markdown_element_list.append(text) page_to_markdown_result[i+1] = markdown_element_list except: next_retry_list.append(i) retry_list = next_retry_list attempt += 1 page_texts = {} fallback_pages = [] for page_number in range(1, num_pages+1): if page_number not in page_to_markdown_result.keys(): fallback_pages.append(page_number-1) else: page_texts[str(page_number-1)] = "\n\n".join(page_to_markdown_result[page_number]) if skip_cross_page_merge: document_text_list = [] for i in range(num_pages): if i not in fallback_pages: document_text_list.append(page_texts[str(i)]) document_text = "\n\n".join(document_text_list) return { "orig_path": file_path, "num_pages": num_pages, "document_text": document_text, "page_texts": page_texts, "fallback_pages": fallback_pages, } # Stage 2: Element Merge Detect element_merge_detect_keys = [] element_merge_detect_query_list = [] for page_num in range(1,num_pages): if page_num in page_to_markdown_result.keys() and page_num+1 in page_to_markdown_result.keys(): element_merge_detect_query_list.append(build_element_merge_detect_query(page_to_markdown_result[page_num],page_to_markdown_result[page_num+1])) element_merge_detect_keys.append((page_num,page_num+1)) # responses = [get_response(element_merge_detect_query, 0.0) for element_merge_detect_query in element_merge_detect_query_list] # responses = llm.generate(element_merge_detect_query_list, sampling_params=sampling_params) tasks = [ get_response(query, 0.0) for query in element_merge_detect_query_list ] responses = await asyncio.gather(*tasks) results = [response for response in responses] element_merge_detect_result = {} for key,result in zip(element_merge_detect_keys,results): try: element_merge_detect_result[key] = eval(result) except: pass # Stage 3: HTML Table Merge html_table_merge_keys = [] for key,result in element_merge_detect_result.items(): page_1,page_2 = key for elem_idx_1,elem_idx_2 in result: text_1 = page_to_markdown_result[page_1][elem_idx_1] text_2 = page_to_markdown_result[page_2][elem_idx_2] if text_1.startswith("<table>") and text_1.endswith("</table>") and text_2.startswith("<table>") and text_2.endswith("</table>"): html_table_merge_keys.append((page_1,page_2,elem_idx_1,elem_idx_2)) html_table_merge_keys = sorted(html_table_merge_keys,key=lambda x: -x[0]) html_table_merge_result = {} page_to_markdown_result_tmp = copy.deepcopy(page_to_markdown_result) i = 0 while i < len(html_table_merge_keys): tmp = set() keys = [] while i < len(html_table_merge_keys): page_1,page_2,elem_idx_1,elem_idx_2 = html_table_merge_keys[i] if (page_2,elem_idx_2) in tmp: break tmp.add((page_1,elem_idx_1)) keys.append((page_1,page_2,elem_idx_1,elem_idx_2)) i += 1 html_table_merge_query_list = [build_html_table_merge_query(page_to_markdown_result_tmp[page_1][elem_idx_1],page_to_markdown_result_tmp[page_2][elem_idx_2]) for page_1,page_2,elem_idx_1,elem_idx_2 in keys] # responses = [get_response(html_table_merge_query, 0.0) for html_table_merge_query in html_table_merge_query_list] # responses = llm.generate(html_table_merge_query_list, sampling_params=sampling_params) tasks = [ get_response(query, 0.0) for query in html_table_merge_query_list ] responses = await asyncio.gather(*tasks) results = [response for response in responses] for key,result in zip(keys,results): if result.startswith("<table>") and result.endswith("</table>"): html_table_merge_result[key] = result page_to_markdown_result_tmp[page_1][elem_idx_1] = result document_text = bulid_document_text(page_to_markdown_result, element_merge_detect_result, html_table_merge_result) return { "orig_path": file_path, "num_pages": num_pages, "document_text": document_text, "page_texts": page_texts, "fallback_pages": fallback_pages, } file_path = '/content/test.pdf' result = await parse(file_path) if result != None: document_markdown = result['document_text'] print(document_markdown) with open('test.md','w') as f: f.write(document_markdown) else: print("Parse failed.") ```
crystalline7/1663013
crystalline7
2025-09-02T00:38:52Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:38:51Z
[View on Civ Archive](https://civarchive.com/models/1557354?modelVersionId=1762284)
amethyst9/1892098
amethyst9
2025-09-02T00:38:34Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:38:34Z
[View on Civ Archive](https://civarchive.com/models/1762769?modelVersionId=1994923)
ultratopaz/358142
ultratopaz
2025-09-02T00:38:18Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:38:18Z
[View on Civ Archive](https://civarchive.com/models/392352?modelVersionId=437670)
ayuzawa/gemma-product-description
ayuzawa
2025-09-02T00:38:13Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:google/gemma-3-4b-it", "base_model:finetune:google/gemma-3-4b-it", "endpoints_compatible", "region:us" ]
null
2025-04-10T03:57:28Z
--- base_model: google/gemma-3-4b-it library_name: transformers model_name: gemma-product-description tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for gemma-product-description This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it). 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="ayuzawa/gemma-product-description", 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: 0.22.1 - Transformers: 4.56.0 - Pytorch: 2.8.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.22.0 ## 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}} } ```
amethyst9/152230
amethyst9
2025-09-02T00:37:10Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:37:10Z
[View on Civ Archive](https://civarchive.com/models/177148?modelVersionId=198873)
seraphimzzzz/886481
seraphimzzzz
2025-09-02T00:37:02Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:37:02Z
[View on Civ Archive](https://civarchive.com/models/875794?modelVersionId=980427)
ultratopaz/1658749
ultratopaz
2025-09-02T00:36:37Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:36:36Z
[View on Civ Archive](https://civarchive.com/models/1553548?modelVersionId=1757920)
yujiangw/Qwen3-1.7B-GRPO
yujiangw
2025-09-02T00:35:46Z
13
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-07-01T22:05:46Z
--- library_name: transformers model_name: Qwen3-1.7B-GRPO tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen3-1.7B-GRPO This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="yujiangw/Qwen3-1.7B-GRPO", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/yujiangw-carnegie-mellon-university/huggingface/runs/i00lzoxd) 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 - Transformers: 4.52.3 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## 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}} } ```
ik/speechless-twi-stage1-rvq-whisper-medium
ik
2025-09-02T00:35:21Z
0
0
pytorch
[ "pytorch", "speechless", "rvq", "whisper", "twi", "akan", "vector-quantization", "semantic-tokens", "tw", "ak", "license:apache-2.0", "region:us" ]
null
2025-09-02T00:35:13Z
--- license: apache-2.0 language: - tw - ak library_name: pytorch tags: - speechless - rvq - whisper - twi - akan - vector-quantization - semantic-tokens --- # Speechless TWI — Stage 1 (RVQ for Whisper Encoder) Trained RVQ that discretizes Whisper encoder features into semantic tokens for **Twi/Akan**. ## Files - `rvq_final.pt` — state dict - `config_stage1.json` — training/config params - `rvq_wrapper.py` — tiny module defining `RVQWrapper` ## Usage (example) ```python import torch, json from huggingface_hub import hf_hub_download from rvq_wrapper import RVQWrapper cfg = json.load(open(hf_hub_download("ik/speechless-twi-stage1-rvq-whisper-medium", "config_stage1.json"), "r")) ckpt = torch.load(hf_hub_download("ik/speechless-twi-stage1-rvq-whisper-medium", "rvq_final.pt"), map_location="cpu") rvq = RVQWrapper(cfg["rvq_dim"], cfg["rvq_num_quantizers"], cfg["rvq_codebook_size"]) rvq.load_state_dict(ckpt["rvq"]) rvq.eval() ```
liukevin666/blockassist-bc-yawning_striped_cassowary_1756773246
liukevin666
2025-09-02T00:35:16Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]
null
2025-09-02T00:35:10Z
--- 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).
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.1-FisherMaskSentence-1e-4-v2_6674
luckeciano
2025-09-02T00:34:12Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-09-01T20:31:25Z
--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.1-FisherMaskSentence-1e-4-v2_6674 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.1-FisherMaskSentence-1e-4-v2_6674 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) 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="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.1-FisherMaskSentence-1e-4-v2_6674", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/hwklsahq) 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: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - Tokenizers: 0.21.2 ## 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ultratopaz/127120
ultratopaz
2025-09-02T00:33:51Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:33:51Z
[View on Civ Archive](https://civarchive.com/models/149959?modelVersionId=167563)
GlobalWheat/GWFSS_Segformer_b4
GlobalWheat
2025-09-02T00:33:46Z
0
0
transformers
[ "transformers", "safetensors", "segformer", "scientific", "research", "agricultural research", "wheat", "segmentation", "crop phenotyping", "global wheat", "crop", "plant", "canopy", "field", "image-segmentation", "dataset:GlobalWheat/GWFSS_v1.0", "base_model:nvidia/segformer-b1-finetuned-ade-512-512", "base_model:finetune:nvidia/segformer-b1-finetuned-ade-512-512", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]
image-segmentation
2025-09-02T00:29:35Z
--- license: cc-by-4.0 pipeline_tag: image-segmentation library_name: transformers datasets: - GlobalWheat/GWFSS_v1.0 metrics: - mean_iou base_model: - nvidia/segformer-b1-finetuned-ade-512-512 tags: - scientific - research - agricultural research - wheat - segmentation - crop phenotyping - global wheat - crop - plant - canopy - field source: https://doi.org/10.1016/j.plaphe.2025.100084 --- ## Usage ```python from transformers import AutoImageProcessor, SegformerForSemanticSegmentation import torch, torch.nn.functional as F from PIL import Image import numpy as np repo = "GlobalWheat/GWFSS_model_v1.1" processor = AutoImageProcessor.from_pretrained(repo) model = SegformerForSemanticSegmentation.from_pretrained(repo).eval() img = Image.open("example.jpg").convert("RGB") inputs = processor(images=img, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits up = F.interpolate(logits, size=(img.height, img.width), mode="bilinear", align_corners=False) pred = up.argmax(1)[0].cpu().numpy() # (H, W) class IDs ``` This version is based on huggingface Segformer which could be slightly different from the one we used for our paper. The paper version was implemented based on the mmsegmentation. You can find the model weight for mmsegmentation library in this repo as well. ## Related Paper This dataset is associated with the following paper: The Global Wheat Full Semantic Organ Segmentation (GWFSS) Dataset https://doi.org/10.1016/j.plaphe.2025.100084
ultratopaz/132506
ultratopaz
2025-09-02T00:33:43Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:33:43Z
[View on Civ Archive](https://civarchive.com/models/155011?modelVersionId=173815)
crystalline7/328188
crystalline7
2025-09-02T00:33:35Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:33:35Z
[View on Civ Archive](https://civarchive.com/models/363116?modelVersionId=405748)
ultratopaz/1658758
ultratopaz
2025-09-02T00:33:27Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:33:26Z
[View on Civ Archive](https://civarchive.com/models/1553557?modelVersionId=1757929)
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756773128
ggozzy
2025-09-02T00:33:23Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]
null
2025-09-02T00:33:17Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
amethyst9/132523
amethyst9
2025-09-02T00:33:19Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:33:18Z
[View on Civ Archive](https://civarchive.com/models/155030?modelVersionId=173839)
omerbektass/blockassist-bc-insectivorous_bold_lion_1756773168
omerbektass
2025-09-02T00:33:12Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous bold lion", "arxiv:2504.07091", "region:us" ]
null
2025-09-02T00:33:08Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous bold lion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
amethyst9/1603511
amethyst9
2025-09-02T00:33:02Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:33:02Z
[View on Civ Archive](https://civarchive.com/models/1505471?modelVersionId=1702925)
crystalline7/328190
crystalline7
2025-09-02T00:32:54Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:32:54Z
[View on Civ Archive](https://civarchive.com/models/363119?modelVersionId=405750)
ORIGINAL-VIDEO-DE-ARCHITA-PHUKAN/FULL.ORIGINAL.VIDEO.DE.ARCHITA.PHUKAN.ABUSADA
ORIGINAL-VIDEO-DE-ARCHITA-PHUKAN
2025-09-02T00:32:46Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:32:33Z
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" 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>
crystalline7/896178
crystalline7
2025-09-02T00:32:46Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:32:46Z
[View on Civ Archive](https://civarchive.com/models/884563?modelVersionId=990164)
ultratopaz/220372
ultratopaz
2025-09-02T00:32:36Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:32:36Z
[View on Civ Archive](https://civarchive.com/models/249195?modelVersionId=281198)
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1756771464
coelacanthxyz
2025-09-02T00:32:29Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]
null
2025-09-02T00:32:22Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ultratopaz/520266
ultratopaz
2025-09-02T00:32:11Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:32:10Z
[View on Civ Archive](https://civarchive.com/models/544250?modelVersionId=605235)
crystalline7/1885234
crystalline7
2025-09-02T00:32:02Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:32:02Z
[View on Civ Archive](https://civarchive.com/models/1756729?modelVersionId=1988142)
crystalline7/186795
crystalline7
2025-09-02T00:31:22Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:31:22Z
[View on Civ Archive](https://civarchive.com/models/214312?modelVersionId=241418)
seraphimzzzz/1603288
seraphimzzzz
2025-09-02T00:31:14Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:31:14Z
[View on Civ Archive](https://civarchive.com/models/1505244?modelVersionId=1702667)
amethyst9/220380
amethyst9
2025-09-02T00:31:06Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:31:06Z
[View on Civ Archive](https://civarchive.com/models/249199?modelVersionId=281203)
amethyst9/132511
amethyst9
2025-09-02T00:30:51Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:30:51Z
[View on Civ Archive](https://civarchive.com/models/155015?modelVersionId=173819)
crystalline7/1667042
crystalline7
2025-09-02T00:30:33Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:30:33Z
[View on Civ Archive](https://civarchive.com/models/1560826?modelVersionId=1766221)
ultratopaz/1675871
ultratopaz
2025-09-02T00:30:17Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:30:16Z
[View on Civ Archive](https://civarchive.com/models/1568645?modelVersionId=1775098)
sekirr/blockassist-bc-masked_tenacious_whale_1756772938
sekirr
2025-09-02T00:29:37Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "masked tenacious whale", "arxiv:2504.07091", "region:us" ]
null
2025-09-02T00:29:34Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - masked tenacious whale --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
seraphimzzzz/423772
seraphimzzzz
2025-09-02T00:29:35Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:29:34Z
[View on Civ Archive](https://civarchive.com/models/454742?modelVersionId=506255)
akirafudo/blockassist-bc-insectivorous_bold_lion_1756772942
akirafudo
2025-09-02T00:29:26Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous bold lion", "arxiv:2504.07091", "region:us" ]
null
2025-09-02T00:29:22Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous bold lion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
amethyst9/130254
amethyst9
2025-09-02T00:29:26Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:29:26Z
[View on Civ Archive](https://civarchive.com/models/152972?modelVersionId=171248)
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756772873
ggozzy
2025-09-02T00:29:09Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]
null
2025-09-02T00:29:02Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
amethyst9/1570632
amethyst9
2025-09-02T00:29:03Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:29:02Z
[View on Civ Archive](https://civarchive.com/models/1476354?modelVersionId=1669897)
seraphimzzzz/321366
seraphimzzzz
2025-09-02T00:28:39Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:28:38Z
[View on Civ Archive](https://civarchive.com/models/356483?modelVersionId=398516)
YuhanQiu/iPSC_GPT_model_for_STATE
YuhanQiu
2025-09-02T00:28:37Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:09:35Z
# iPSC_GPT_model_for_STATE This repository hosts a small **STATE/ST model** trained on **iPSC reprogramming data (GSE213069)** for virtual gene knockout and perturbation prediction tasks. --- ## 📊 Model Summary - **Framework**: [ArcInstitute/STATE](https://github.com/ArcInstitute/state) - **Dataset**: [GSE213069](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE213069) - **Preprocessing**: - HVG = 3000 - Forced inclusion of key pluripotency/ROS-related genes: `PARK7, PINK1, OCT4, KLF4, SOX2, NANOG, ESRRB, REX1` - **Training setup**: - Steps: 5000 - Batch size: 32 - Learning rate: 1e-4 - Optimizer: AdamW - Model size: ~67M parameters (~648MB checkpoint) - **Output checkpoint**: - `ST-iPSC_GPT.ckpt` → Selected based on lowest validation loss --- ## 🚀 Usage ### 1. Install dependencies ```bash pip install arc-state cell-load anndata scanpy ``` ### 2. Download checkpoint ```python from huggingface_hub import hf_hub_download ckpt_path = hf_hub_download( repo_id="YuhanQiu/iPSC_GPT_model_for_STATE", filename="ST-iPSC_GPT.ckpt", repo_type="model" ) print("Checkpoint downloaded to:", ckpt_path) ``` ### 3. Run inference / virtual KO Example: simulate a **PARK7** or **PINK1** knockout. ```bash state tx infer data.kwargs.toml_config_path=./config.toml data.kwargs.embed_key=X_hvg data.kwargs.pert_col=condition data.kwargs.cell_type_key=cell_type data.kwargs.control_pert=ctrl infer.genes=PARK7,PINK1 infer.ckpt_path=ST-iPSC_GPT.ckpt output_dir=./inference_results ``` --- ## 📂 Files in this repo - `ST-iPSC_GPT.ckpt` → best validation-loss checkpoint - `README.md` (this file) --- ## 📖 Citation If you use this model, please cite: - **STATE framework**: Arc Institute (https://github.com/ArcInstitute/state) - **Dataset**: GSE213069 (NCBI GEO) --- ## ⚠️ Notes - This is a **small-scale demo model** trained on iPSC reprogramming data. - Checkpoint size: ~648 MB - Intended for research use (virtual gene perturbation, proof-of-concept).
kwspringkles/whisper_15epoch_specaug
kwspringkles
2025-09-02T00:28:35Z
0
0
transformers
[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2025-09-01T14:15:26Z
--- library_name: transformers license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer model-index: - name: whisper_15epoch_specaug 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. --> # whisper_15epoch_specaug This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3102 - Cer: 10.6155 ## 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: 3e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5709 | 1.0 | 160 | 0.4689 | 12.4801 | | 0.3323 | 2.0 | 320 | 0.3196 | 11.2728 | | 0.299 | 3.0 | 480 | 0.3059 | 10.9308 | | 0.2705 | 4.0 | 640 | 0.3029 | 10.7597 | | 0.2507 | 5.0 | 800 | 0.3024 | 10.6625 | | 0.2383 | 6.0 | 960 | 0.3028 | 10.5703 | | 0.23 | 7.0 | 1120 | 0.3058 | 10.6524 | | 0.2248 | 8.0 | 1280 | 0.3066 | 10.5988 | | 0.2173 | 9.0 | 1440 | 0.3085 | 10.6457 | | 0.2128 | 10.0 | 1600 | 0.3102 | 10.6155 | ### Framework versions - Transformers 4.56.0 - Pytorch 2.8.0+cu129 - Datasets 4.0.0 - Tokenizers 0.22.0
giovannidemuri/llama8b-er-v532-seed2-hx_lora
giovannidemuri
2025-09-02T00:28:31Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-09-01T21:39:18Z
--- 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]
vera6/sn105_denoising_7
vera6
2025-09-02T00:28:26Z
0
0
null
[ "region:us" ]
null
2025-09-01T20:01:58Z
DENOISING speech enhancement model
ultratopaz/1579425
ultratopaz
2025-09-02T00:28:22Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:28:21Z
[View on Civ Archive](https://civarchive.com/models/1483782?modelVersionId=1678408)
amethyst9/445964
amethyst9
2025-09-02T00:27:48Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:27:47Z
[View on Civ Archive](https://civarchive.com/models/475662?modelVersionId=529060)
alexandroputra/medsiglip-448-ft-tb-screening
alexandroputra
2025-09-02T00:27:17Z
0
0
transformers
[ "transformers", "safetensors", "siglip", "zero-shot-image-classification", "generated_from_trainer", "base_model:google/medsiglip-448", "base_model:finetune:google/medsiglip-448", "license:other", "endpoints_compatible", "region:us" ]
zero-shot-image-classification
2025-09-01T10:59:31Z
--- library_name: transformers license: other base_model: google/medsiglip-448 tags: - generated_from_trainer model-index: - name: medsiglip-448-ft-tb-screening 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. --> # medsiglip-448-ft-tb-screening This model is a fine-tuned version of [google/medsiglip-448](https://huggingface.co/google/medsiglip-448) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5291 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 3.5828 | 0.4762 | 25 | 2.4233 | | 1.947 | 0.9524 | 50 | 2.6277 | | 1.9315 | 1.4190 | 75 | 2.4902 | | 1.9614 | 1.8952 | 100 | 2.5603 | | 1.9357 | 2.3619 | 125 | 2.5067 | | 1.9396 | 2.8381 | 150 | 2.6298 | | 1.9313 | 3.3048 | 175 | 2.5459 | | 1.8956 | 3.7810 | 200 | 2.5050 | | 1.9271 | 4.2476 | 225 | 2.5318 | | 1.9317 | 4.7238 | 250 | 2.5298 | | 1.9365 | 5.1905 | 275 | 2.5108 | | 1.9255 | 5.6667 | 300 | 2.5120 | | 1.9284 | 6.1333 | 325 | 2.5134 | | 1.907 | 6.6095 | 350 | 2.5199 | | 1.8996 | 7.0762 | 375 | 2.5279 | | 1.9321 | 7.5524 | 400 | 2.5274 | | 1.9101 | 8.0190 | 425 | 2.5273 | | 1.9131 | 8.4952 | 450 | 2.5293 | | 1.91 | 8.9714 | 475 | 2.5300 | | 1.919 | 9.4381 | 500 | 2.5290 | | 1.9189 | 9.9143 | 525 | 2.5291 | ### Framework versions - Transformers 4.56.0 - Pytorch 2.8.0+cu128 - Tokenizers 0.22.0
crystalline7/321471
crystalline7
2025-09-02T00:27:15Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:27:14Z
[View on Civ Archive](https://civarchive.com/models/356581?modelVersionId=398624)
amethyst9/1603620
amethyst9
2025-09-02T00:26:56Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:26:55Z
[View on Civ Archive](https://civarchive.com/models/1505561?modelVersionId=1703034)
crystalline7/365135
crystalline7
2025-09-02T00:26:46Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:26:45Z
[View on Civ Archive](https://civarchive.com/models/399131?modelVersionId=445146)
ElRompeAnosFullAnal/ElRompeAnosFullAnal
ElRompeAnosFullAnal
2025-09-02T00:25:57Z
0
0
null
[ "license:cc-by-nc-4.0", "region:us" ]
null
2025-03-31T22:45:18Z
--- license: cc-by-nc-4.0 ---
ultratopaz/321567
ultratopaz
2025-09-02T00:25:46Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:25:45Z
[View on Civ Archive](https://civarchive.com/models/356693?modelVersionId=398739)
seraphimzzzz/1624722
seraphimzzzz
2025-09-02T00:25:20Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:25:19Z
[View on Civ Archive](https://civarchive.com/models/1523865?modelVersionId=1724179)
crystalline7/1570492
crystalline7
2025-09-02T00:25:11Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:25:11Z
[View on Civ Archive](https://civarchive.com/models/1476231?modelVersionId=1669763)
giovannidemuri/llama8b-er-v531-seed2-hx_lora
giovannidemuri
2025-09-02T00:25:09Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-09-01T21:48:58Z
--- 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]
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756772619
ggozzy
2025-09-02T00:24:56Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]
null
2025-09-02T00:24:48Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
arka7/medreason-finetuned-qwen
arka7
2025-09-02T00:24:42Z
0
0
transformers
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-09-02T00:24:34Z
--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** arka7 - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit 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)
mradermacher/Qwen2.5-VL-3B-Retrieval-React-GGUF
mradermacher
2025-09-02T00:22:28Z
0
0
transformers
[ "transformers", "gguf", "en", "base_model:Agent-One/Qwen2.5-VL-3B-Retrieval-React", "base_model:quantized:Agent-One/Qwen2.5-VL-3B-Retrieval-React", "endpoints_compatible", "region:us", "conversational" ]
null
2025-09-02T00:14:57Z
--- base_model: Agent-One/Qwen2.5-VL-3B-Retrieval-React language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/Agent-One/Qwen2.5-VL-3B-Retrieval-React <!-- provided-files --> ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen2.5-VL-3B-Retrieval-React-GGUF).*** weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Qwen2.5-VL-3B-Retrieval-React-GGUF/resolve/main/Qwen2.5-VL-3B-Retrieval-React.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.9 | multi-modal supplement | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-3B-Retrieval-React-GGUF/resolve/main/Qwen2.5-VL-3B-Retrieval-React.Q2_K.gguf) | Q2_K | 1.4 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-3B-Retrieval-React-GGUF/resolve/main/Qwen2.5-VL-3B-Retrieval-React.mmproj-f16.gguf) | mmproj-f16 | 1.4 | multi-modal supplement | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-3B-Retrieval-React-GGUF/resolve/main/Qwen2.5-VL-3B-Retrieval-React.Q3_K_S.gguf) | Q3_K_S | 1.6 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-3B-Retrieval-React-GGUF/resolve/main/Qwen2.5-VL-3B-Retrieval-React.Q3_K_M.gguf) | Q3_K_M | 1.7 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-3B-Retrieval-React-GGUF/resolve/main/Qwen2.5-VL-3B-Retrieval-React.Q3_K_L.gguf) | Q3_K_L | 1.8 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-3B-Retrieval-React-GGUF/resolve/main/Qwen2.5-VL-3B-Retrieval-React.IQ4_XS.gguf) | IQ4_XS | 1.9 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-3B-Retrieval-React-GGUF/resolve/main/Qwen2.5-VL-3B-Retrieval-React.Q4_K_S.gguf) | Q4_K_S | 1.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-3B-Retrieval-React-GGUF/resolve/main/Qwen2.5-VL-3B-Retrieval-React.Q4_K_M.gguf) | Q4_K_M | 2.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-3B-Retrieval-React-GGUF/resolve/main/Qwen2.5-VL-3B-Retrieval-React.Q5_K_S.gguf) | Q5_K_S | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-3B-Retrieval-React-GGUF/resolve/main/Qwen2.5-VL-3B-Retrieval-React.Q5_K_M.gguf) | Q5_K_M | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-3B-Retrieval-React-GGUF/resolve/main/Qwen2.5-VL-3B-Retrieval-React.Q6_K.gguf) | Q6_K | 2.6 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-3B-Retrieval-React-GGUF/resolve/main/Qwen2.5-VL-3B-Retrieval-React.Q8_0.gguf) | Q8_0 | 3.4 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-3B-Retrieval-React-GGUF/resolve/main/Qwen2.5-VL-3B-Retrieval-React.f16.gguf) | f16 | 6.3 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
vendi11/blockassist-bc-placid_placid_llama_1756772473
vendi11
2025-09-02T00:21:55Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "placid placid llama", "arxiv:2504.07091", "region:us" ]
null
2025-09-02T00:21:52Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - placid placid llama --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
seraphimzzzz/1570616
seraphimzzzz
2025-09-02T00:20:51Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:20:51Z
[View on Civ Archive](https://civarchive.com/models/1476348?modelVersionId=1669889)
ultratopaz/517694
ultratopaz
2025-09-02T00:20:22Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:20:22Z
[View on Civ Archive](https://civarchive.com/models/542047?modelVersionId=602673)
amethyst9/517711
amethyst9
2025-09-02T00:20:14Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:20:13Z
[View on Civ Archive](https://civarchive.com/models/541960?modelVersionId=602578)
amethyst9/522732
amethyst9
2025-09-02T00:19:56Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:19:55Z
[View on Civ Archive](https://civarchive.com/models/546312?modelVersionId=607619)
crystalline7/292573
crystalline7
2025-09-02T00:19:47Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:19:46Z
[View on Civ Archive](https://civarchive.com/models/326476?modelVersionId=365939)
mradermacher/InternVL3_5-38B-i1-GGUF
mradermacher
2025-09-02T00:19:41Z
0
0
transformers
[ "transformers", "gguf", "internvl", "custom_code", "multilingual", "dataset:OpenGVLab/MMPR-v1.2", "dataset:OpenGVLab/MMPR-Tiny", "base_model:OpenGVLab/InternVL3_5-38B", "base_model:quantized:OpenGVLab/InternVL3_5-38B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]
null
2025-09-01T22:16:15Z
--- base_model: OpenGVLab/InternVL3_5-38B datasets: - OpenGVLab/MMPR-v1.2 - OpenGVLab/MMPR-Tiny language: - multilingual library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - internvl - custom_code --- ## 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/OpenGVLab/InternVL3_5-38B <!-- provided-files --> ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#InternVL3_5-38B-i1-GGUF).*** static quants are available at https://huggingface.co/mradermacher/InternVL3_5-38B-GGUF **This is a vision model - mmproj files (if any) will be in the [static repository](https://huggingface.co/mradermacher/InternVL3_5-38B-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/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-IQ1_M.gguf) | i1-IQ1_M | 8.1 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-IQ2_S.gguf) | i1-IQ2_S | 10.6 | | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-IQ2_M.gguf) | i1-IQ2_M | 11.5 | | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.6 | very low quality | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 | | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-IQ3_M.gguf) | i1-IQ3_M | 15.0 | | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 | | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-Q4_1.gguf) | i1-Q4_1 | 20.7 | | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 | | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.3 | | | [GGUF](https://huggingface.co/mradermacher/InternVL3_5-38B-i1-GGUF/resolve/main/InternVL3_5-38B.i1-Q6_K.gguf) | i1-Q6_K | 27.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) 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 -->
ultratopaz/1570661
ultratopaz
2025-09-02T00:19:12Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:19:12Z
[View on Civ Archive](https://civarchive.com/models/1476380?modelVersionId=1669931)
amethyst9/1663040
amethyst9
2025-09-02T00:19:04Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:19:03Z
[View on Civ Archive](https://civarchive.com/models/1557375?modelVersionId=1762309)
amethyst9/1666983
amethyst9
2025-09-02T00:18:55Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:18:54Z
[View on Civ Archive](https://civarchive.com/models/1560767?modelVersionId=1766152)
omerbkts/blockassist-bc-insectivorous_bold_lion_1756772304
omerbkts
2025-09-02T00:18:47Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous bold lion", "arxiv:2504.07091", "region:us" ]
null
2025-09-02T00:18:43Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous bold lion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF
mradermacher
2025-09-02T00:18:10Z
0
0
transformers
[ "transformers", "gguf", "nsfw", "explicit", "roleplay", "unaligned", "dangerous", "ERP", "en", "base_model:ReadyArt/Safeword-Casual-v1-R1-4B", "base_model:quantized:ReadyArt/Safeword-Casual-v1-R1-4B", "license:gemma", "endpoints_compatible", "region:us", "imatrix", "conversational" ]
null
2025-09-01T23:46:59Z
--- base_model: ReadyArt/Safeword-Casual-v1-R1-4B language: - en library_name: transformers license: gemma mradermacher: readme_rev: 1 quantized_by: mradermacher tags: - nsfw - explicit - roleplay - unaligned - dangerous - ERP --- ## 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/ReadyArt/Safeword-Casual-v1-R1-4B <!-- provided-files --> ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Safeword-Casual-v1-R1-4B-i1-GGUF).*** static quants are available at https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-GGUF **This is a vision model - mmproj files (if any) will be in the [static repository](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-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/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.2 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.4 | | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.5 | | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-IQ2_S.gguf) | i1-IQ2_S | 1.5 | | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-IQ2_M.gguf) | i1-IQ2_M | 1.6 | | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.7 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-Q2_K.gguf) | i1-Q2_K | 1.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 2.0 | | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-IQ3_S.gguf) | i1-IQ3_S | 2.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 2.0 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-IQ3_M.gguf) | i1-IQ3_M | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 2.2 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 2.3 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 2.5 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-Q4_0.gguf) | i1-Q4_0 | 2.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.5 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-Q4_1.gguf) | i1-Q4_1 | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Safeword-Casual-v1-R1-4B-i1-GGUF/resolve/main/Safeword-Casual-v1-R1-4B.i1-Q6_K.gguf) | i1-Q6_K | 3.3 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) 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 -->
ultratopaz/137740
ultratopaz
2025-09-02T00:18:06Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:18:06Z
[View on Civ Archive](https://civarchive.com/models/159921?modelVersionId=179873)
crystalline7/321461
crystalline7
2025-09-02T00:17:58Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:17:57Z
[View on Civ Archive](https://civarchive.com/models/356552?modelVersionId=398590)
sugoitoolkit/Sugoi-32B-Ultra-GGUF
sugoitoolkit
2025-09-02T00:17:23Z
1,009
3
null
[ "gguf", "translation", "ja", "en", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:quantized:Qwen/Qwen2.5-32B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
translation
2025-08-23T13:16:07Z
--- license: apache-2.0 language: - ja - en base_model: - Qwen/Qwen2.5-32B-Instruct tags: - gguf - translation --- # Sugoi LLM 32B Ultra (GGUF version) Unleashing the full potential of the previous sugoi 32B model, Sugoi 32B Ultra. Benchmark soon.
crystalline7/484435
crystalline7
2025-09-02T00:17:23Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:17:23Z
[View on Civ Archive](https://civarchive.com/models/511641?modelVersionId=568639)
seraphimzzzz/321456
seraphimzzzz
2025-09-02T00:16:51Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:16:51Z
[View on Civ Archive](https://civarchive.com/models/356553?modelVersionId=398591)
crystalline7/1562450
crystalline7
2025-09-02T00:16:34Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:16:33Z
[View on Civ Archive](https://civarchive.com/models/1469373?modelVersionId=1661981)
amethyst9/484440
amethyst9
2025-09-02T00:16:25Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:16:24Z
[View on Civ Archive](https://civarchive.com/models/511642?modelVersionId=568640)
seraphimzzzz/344584
seraphimzzzz
2025-09-02T00:15:58Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:15:58Z
[View on Civ Archive](https://civarchive.com/models/378913?modelVersionId=423060)
ultratopaz/365140
ultratopaz
2025-09-02T00:15:49Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:15:49Z
[View on Civ Archive](https://civarchive.com/models/399137?modelVersionId=445151)
seraphimzzzz/152259
seraphimzzzz
2025-09-02T00:15:31Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:15:30Z
[View on Civ Archive](https://civarchive.com/models/177185?modelVersionId=198915)
gwenweng/gemma
gwenweng
2025-09-02T00:15:26Z
0
0
null
[ "safetensors", "region:us" ]
null
2025-09-02T00:10:13Z
--- license: mit datasets: - allenai/real-toxicity-prompts language: - en pipeline_tag: text-generation tags: - controllable-generation - toxicity-reduction - hmm - language-model --- # TRACE HMM Model (Gemma-2b) This is the pre-trained Hidden Markov Model used in the TRACE (Tractable Reasoning for Adaptable Controllable gEneration) paper for controllable text generation with toxicity reduction. ## Model Details - **Base Model**: Gemma-2b - **Training Data**: RealToxicityPrompts dataset - **Sequence Length**: 32 tokens - **Hidden Size**: 4096 - **Training Samples**: 10M - **Purpose**: Guidance for reducing toxicity in language generation ## Usage ```python from huggingface_hub import snapshot_download # Download the model model_path = snapshot_download( repo_id="gwenweng/gemma", local_dir="models/gemma" ) # Use with TRACE # See: https://github.com/yidouweng/trace Citation @inproceedings{weng2025trace, title={TRACE Back from the Future: A Probabilistic Reasoning Approach to Controllable Language Generation}, author={Weng, Yidou and Wang, Benjie and Van den Broeck, Guy}, booktitle={Proceedings of the 42nd International Conference on Machine Learning (ICML)}, year={2025} } License MIT License - see https://github.com/yidouweng/trace for details.
omerbektass/blockassist-bc-insectivorous_bold_lion_1756772073
omerbektass
2025-09-02T00:14:57Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "insectivorous bold lion", "arxiv:2504.07091", "region:us" ]
null
2025-09-02T00:14:53Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - insectivorous bold lion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
crystalline7/130240
crystalline7
2025-09-02T00:14:56Z
0
0
null
[ "region:us" ]
null
2025-09-02T00:14:56Z
[View on Civ Archive](https://civarchive.com/models/152957?modelVersionId=171231)