librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-08-20 06:28:26	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 515 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-20 06:27:49	card stringlengths 11 1.01M
dodo1234/mistral-classification-model-mergeeed	dodo1234	2025-08-18T22:58:44Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-08-18T22:58:14Z	--- base_model: mistral-classification-model-mergeed tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: dodo1234 - License: apache-2.0 - Finetuned from model : mistral-classification-model-mergeed This mistral 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)
crystalline7/1363028	crystalline7	2025-08-18T22:58:33Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:58:29Z	[View on Civ Archive](https://civarchive.com/models/1295323?modelVersionId=1461911)
crystalline7/1180705	crystalline7	2025-08-18T22:58:24Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:58:21Z	[View on Civ Archive](https://civarchive.com/models/1134818?modelVersionId=1275898)
seraphimzzzz/1200252	seraphimzzzz	2025-08-18T22:58:17Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:58:14Z	[View on Civ Archive](https://civarchive.com/models/1152263?modelVersionId=1296011)
razor534/blockassist-bc-lazy_extinct_termite_1755557840	razor534	2025-08-18T22:58:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lazy extinct termite", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:58:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lazy extinct termite --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ultratopaz/1623777	ultratopaz	2025-08-18T22:58:07Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:58:00Z	[View on Civ Archive](https://civarchive.com/models/693933?modelVersionId=1723260)
ultratopaz/1359879	ultratopaz	2025-08-18T22:57:55Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:57:52Z	[View on Civ Archive](https://civarchive.com/models/1292588?modelVersionId=1458722)
crystalline7/1454395	crystalline7	2025-08-18T22:57:48Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:57:45Z	[View on Civ Archive](https://civarchive.com/models/1375798?modelVersionId=1554509)
ultratopaz/1488700	ultratopaz	2025-08-18T22:57:31Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:57:23Z	[View on Civ Archive](https://civarchive.com/models/1285488?modelVersionId=1588898)
ultratopaz/1299082	ultratopaz	2025-08-18T22:56:12Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:56:10Z	[View on Civ Archive](https://civarchive.com/models/1239292?modelVersionId=1396676)
databoyface/python-tf-ome-keras-v4.1	databoyface	2025-08-18T22:56:09Z	0	1	null	[ "en", "dataset:databoyface/python-tf-ome-src-v4.1", "license:mit", "region:us" ]	null	2025-08-18T15:00:10Z	--- license: mit datasets: - databoyface/python-tf-ome-src-v4.1 language: - en --- # Orthogonal Model of Emotions A Text Classifier created using TensorFlow and Keras ## Author C.J. Pitchford ## Published 18 August 2025 ## Model and Weights Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding (Embedding) (None, 1000, 64) 6400000 bidirectional (Bidirection (None, 1000, 128) 66048 al) global_max_pooling1d (Glob (None, 128) 0 alMaxPooling1D) dense (Dense) (None, 64) 8256 dropout (Dropout) (None, 64) 0 dense_1 (Dense) (None, 47) 3055 ================================================================= Total params: 6477359 (24.71 MB) Trainable params: 6477359 (24.71 MB) Non-trainable params: 0 (0.00 Byte) ## Usage import numpy as np import tensorflow as tf import tensorflow.keras.preprocessing.text as text import pickle from tensorflow.keras.preprocessing.sequence import pad_sequences # 1. Load pre-trained model model = tf.keras.models.load_model('OME4tf/ome-4a-model.h5') # 2. Load tokenizer and label encoder with open('OME4tf/ome-4a-tokenizer.pkl', 'rb') as f: tokenizer = pickle.load(f) with open('OME4tf/ome-4a-label_encoder.pkl', 'rb') as f: label_encoder = pickle.load(f) # 3. Test model with prediction on text "I failed to hide my distress." text = "I failed to hide my distress." text_seq = tokenizer.texts_to_sequences([text]) max_len = 1000 text_seq = pad_sequences(text_seq, maxlen=max_len, padding='post') pred_probs = model.predict(text_seq) pred_label = np.argmax(pred_probs, axis=1) print(f"Statement: {text}\nPrediction: {label_encoder.classes_[pred_label][0]}") ## Additional Tokenizer and label encoder included as JSON to avoid using `pickle` files.
seraphimzzzz/1316758	seraphimzzzz	2025-08-18T22:55:58Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:55:54Z	[View on Civ Archive](https://civarchive.com/models/1254992?modelVersionId=1414887)
seraphimzzzz/1263759	seraphimzzzz	2025-08-18T22:54:58Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:54:56Z	[View on Civ Archive](https://civarchive.com/models/1208027?modelVersionId=1360518)
crystalline7/1444117	crystalline7	2025-08-18T22:53:33Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:53:30Z	[View on Civ Archive](https://civarchive.com/models/1366927?modelVersionId=1544263)
crystalline7/1439734	crystalline7	2025-08-18T22:52:44Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:52:41Z	[View on Civ Archive](https://civarchive.com/models/361569?modelVersionId=1539769)
Dejiat/blockassist-bc-savage_unseen_bobcat_1755557483	Dejiat	2025-08-18T22:52:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:52:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
g-assismoraes/Qwen3-4B-Base-fpi-alpha0.12-var-hatebr-ep30-g5	g-assismoraes	2025-08-18T22:51:51Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T22:48:07Z	--- 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]
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755557298	IvanJAjebu	2025-08-18T22:50:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:49:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dejiat/blockassist-bc-savage_unseen_bobcat_1755557262	Dejiat	2025-08-18T22:48:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:48:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
donoway/ARC-Challenge_Llama-3.2-1B-hifbxhfd	donoway	2025-08-18T22:48:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T22:36:19Z	--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: ARC-Challenge_Llama-3.2-1B-hifbxhfd 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. --> # ARC-Challenge_Llama-3.2-1B-hifbxhfd This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.9794 - Model Preparation Time: 0.0063 - Mdl: 2147.9346 - Accumulated Loss: 1488.8348 - Correct Preds: 120.0 - Total Preds: 299.0 - Accuracy: 0.4013 - Correct Gen Preds: 116.0 - Gen Accuracy: 0.3880 - Correct Gen Preds 32: 15.0 - Correct Preds 32: 15.0 - Total Labels 32: 64.0 - Accuracy 32: 0.2344 - Gen Accuracy 32: 0.2344 - Correct Gen Preds 33: 41.0 - Correct Preds 33: 43.0 - Total Labels 33: 73.0 - Accuracy 33: 0.5890 - Gen Accuracy 33: 0.5616 - Correct Gen Preds 34: 31.0 - Correct Preds 34: 31.0 - Total Labels 34: 78.0 - Accuracy 34: 0.3974 - Gen Accuracy 34: 0.3974 - Correct Gen Preds 35: 29.0 - Correct Preds 35: 31.0 - Total Labels 35: 83.0 - Accuracy 35: 0.3735 - Gen Accuracy 35: 0.3494 - Correct Gen Preds 36: 0.0 - Correct Preds 36: 0.0 - Total Labels 36: 1.0 - Accuracy 36: 0.0 - Gen Accuracy 36: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 112 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Mdl \| Accumulated Loss \| Correct Preds \| Total Preds \| Accuracy \| Correct Gen Preds \| Gen Accuracy \| Correct Gen Preds 32 \| Correct Preds 32 \| Total Labels 32 \| Accuracy 32 \| Gen Accuracy 32 \| Correct Gen Preds 33 \| Correct Preds 33 \| Total Labels 33 \| Accuracy 33 \| Gen Accuracy 33 \| Correct Gen Preds 34 \| Correct Preds 34 \| Total Labels 34 \| Accuracy 34 \| Gen Accuracy 34 \| Correct Gen Preds 35 \| Correct Preds 35 \| Total Labels 35 \| Accuracy 35 \| Gen Accuracy 35 \| Correct Gen Preds 36 \| Correct Preds 36 \| Total Labels 36 \| Accuracy 36 \| Gen Accuracy 36 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------------:\|:---------:\|:----------------:\|:-------------:\|:-----------:\|:--------:\|:-----------------:\|:------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\| \| No log \| 0 \| 0 \| 1.6389 \| 0.0063 \| 706.9523 \| 490.0220 \| 66.0 \| 299.0 \| 0.2207 \| 66.0 \| 0.2207 \| 62.0 \| 62.0 \| 64.0 \| 0.9688 \| 0.9688 \| 0.0 \| 0.0 \| 73.0 \| 0.0 \| 0.0 \| 4.0 \| 4.0 \| 78.0 \| 0.0513 \| 0.0513 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 1.4701 \| 1.0 \| 3 \| 1.5924 \| 0.0063 \| 686.8892 \| 476.1153 \| 76.0 \| 299.0 \| 0.2542 \| 75.0 \| 0.2508 \| 7.0 \| 7.0 \| 64.0 \| 0.1094 \| 0.1094 \| 68.0 \| 69.0 \| 73.0 \| 0.9452 \| 0.9315 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 1.4771 \| 2.0 \| 6 \| 1.4077 \| 0.0063 \| 607.2453 \| 420.9104 \| 77.0 \| 299.0 \| 0.2575 \| 74.0 \| 0.2475 \| 36.0 \| 37.0 \| 64.0 \| 0.5781 \| 0.5625 \| 7.0 \| 7.0 \| 73.0 \| 0.0959 \| 0.0959 \| 0.0 \| 0.0 \| 78.0 \| 0.0 \| 0.0 \| 31.0 \| 33.0 \| 83.0 \| 0.3976 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 1.0843 \| 3.0 \| 9 \| 1.4049 \| 0.0063 \| 606.0456 \| 420.0788 \| 99.0 \| 299.0 \| 0.3311 \| 98.0 \| 0.3278 \| 24.0 \| 25.0 \| 64.0 \| 0.3906 \| 0.375 \| 40.0 \| 40.0 \| 73.0 \| 0.5479 \| 0.5479 \| 21.0 \| 21.0 \| 78.0 \| 0.2692 \| 0.2692 \| 13.0 \| 13.0 \| 83.0 \| 0.1566 \| 0.1566 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.4069 \| 4.0 \| 12 \| 2.1331 \| 0.0063 \| 920.1642 \| 637.8092 \| 114.0 \| 299.0 \| 0.3813 \| 105.0 \| 0.3512 \| 24.0 \| 26.0 \| 64.0 \| 0.4062 \| 0.375 \| 24.0 \| 26.0 \| 73.0 \| 0.3562 \| 0.3288 \| 22.0 \| 25.0 \| 78.0 \| 0.3205 \| 0.2821 \| 35.0 \| 37.0 \| 83.0 \| 0.4458 \| 0.4217 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0958 \| 5.0 \| 15 \| 3.1166 \| 0.0063 \| 1344.3795 \| 931.8528 \| 106.0 \| 299.0 \| 0.3545 \| 91.0 \| 0.3043 \| 11.0 \| 15.0 \| 64.0 \| 0.2344 \| 0.1719 \| 28.0 \| 30.0 \| 73.0 \| 0.4110 \| 0.3836 \| 18.0 \| 22.0 \| 78.0 \| 0.2821 \| 0.2308 \| 34.0 \| 39.0 \| 83.0 \| 0.4699 \| 0.4096 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0596 \| 6.0 \| 18 \| 4.9794 \| 0.0063 \| 2147.9346 \| 1488.8348 \| 120.0 \| 299.0 \| 0.4013 \| 116.0 \| 0.3880 \| 15.0 \| 15.0 \| 64.0 \| 0.2344 \| 0.2344 \| 41.0 \| 43.0 \| 73.0 \| 0.5890 \| 0.5616 \| 31.0 \| 31.0 \| 78.0 \| 0.3974 \| 0.3974 \| 29.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3494 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0006 \| 7.0 \| 21 \| 5.7281 \| 0.0063 \| 2470.9158 \| 1712.7083 \| 118.0 \| 299.0 \| 0.3946 \| 116.0 \| 0.3880 \| 20.0 \| 20.0 \| 64.0 \| 0.3125 \| 0.3125 \| 35.0 \| 36.0 \| 73.0 \| 0.4932 \| 0.4795 \| 31.0 \| 31.0 \| 78.0 \| 0.3974 \| 0.3974 \| 30.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3614 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0001 \| 8.0 \| 24 \| 6.5268 \| 0.0063 \| 2815.4597 \| 1951.5280 \| 116.0 \| 299.0 \| 0.3880 \| 115.0 \| 0.3846 \| 25.0 \| 25.0 \| 64.0 \| 0.3906 \| 0.3906 \| 31.0 \| 31.0 \| 73.0 \| 0.4247 \| 0.4247 \| 28.0 \| 28.0 \| 78.0 \| 0.3590 \| 0.3590 \| 31.0 \| 32.0 \| 83.0 \| 0.3855 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 9.0 \| 27 \| 6.9695 \| 0.0063 \| 3006.4105 \| 2083.8849 \| 113.0 \| 299.0 \| 0.3779 \| 112.0 \| 0.3746 \| 27.0 \| 27.0 \| 64.0 \| 0.4219 \| 0.4219 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 26.0 \| 26.0 \| 78.0 \| 0.3333 \| 0.3333 \| 31.0 \| 32.0 \| 83.0 \| 0.3855 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 10.0 \| 30 \| 7.2697 \| 0.0063 \| 3135.8946 \| 2173.6365 \| 113.0 \| 299.0 \| 0.3779 \| 112.0 \| 0.3746 \| 28.0 \| 28.0 \| 64.0 \| 0.4375 \| 0.4375 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 25.0 \| 25.0 \| 78.0 \| 0.3205 \| 0.3205 \| 31.0 \| 32.0 \| 83.0 \| 0.3855 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 11.0 \| 33 \| 7.4778 \| 0.0063 \| 3225.6685 \| 2235.8630 \| 113.0 \| 299.0 \| 0.3779 \| 112.0 \| 0.3746 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 27.0 \| 27.0 \| 73.0 \| 0.3699 \| 0.3699 \| 25.0 \| 25.0 \| 78.0 \| 0.3205 \| 0.3205 \| 31.0 \| 32.0 \| 83.0 \| 0.3855 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 12.0 \| 36 \| 7.5700 \| 0.0063 \| 3265.4444 \| 2263.4336 \| 113.0 \| 299.0 \| 0.3779 \| 113.0 \| 0.3779 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 25.0 \| 25.0 \| 78.0 \| 0.3205 \| 0.3205 \| 31.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 13.0 \| 39 \| 7.6337 \| 0.0063 \| 3292.8992 \| 2282.4638 \| 115.0 \| 299.0 \| 0.3846 \| 115.0 \| 0.3846 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 27.0 \| 27.0 \| 78.0 \| 0.3462 \| 0.3462 \| 31.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 14.0 \| 42 \| 7.7177 \| 0.0063 \| 3329.1652 \| 2307.6014 \| 115.0 \| 299.0 \| 0.3846 \| 114.0 \| 0.3813 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 26.0 \| 26.0 \| 78.0 \| 0.3333 \| 0.3333 \| 31.0 \| 32.0 \| 83.0 \| 0.3855 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 15.0 \| 45 \| 7.7144 \| 0.0063 \| 3327.7168 \| 2306.5975 \| 113.0 \| 299.0 \| 0.3779 \| 112.0 \| 0.3746 \| 28.0 \| 28.0 \| 64.0 \| 0.4375 \| 0.4375 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 25.0 \| 25.0 \| 78.0 \| 0.3205 \| 0.3205 \| 31.0 \| 32.0 \| 83.0 \| 0.3855 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 16.0 \| 48 \| 7.7456 \| 0.0063 \| 3341.2047 \| 2315.9466 \| 114.0 \| 299.0 \| 0.3813 \| 114.0 \| 0.3813 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 26.0 \| 26.0 \| 78.0 \| 0.3333 \| 0.3333 \| 31.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 17.0 \| 51 \| 7.7246 \| 0.0063 \| 3332.1346 \| 2309.6597 \| 113.0 \| 299.0 \| 0.3779 \| 112.0 \| 0.3746 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 27.0 \| 27.0 \| 73.0 \| 0.3699 \| 0.3699 \| 25.0 \| 25.0 \| 78.0 \| 0.3205 \| 0.3205 \| 31.0 \| 32.0 \| 83.0 \| 0.3855 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 18.0 \| 54 \| 7.7910 \| 0.0063 \| 3360.7844 \| 2329.5182 \| 113.0 \| 299.0 \| 0.3779 \| 112.0 \| 0.3746 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 24.0 \| 24.0 \| 78.0 \| 0.3077 \| 0.3077 \| 31.0 \| 32.0 \| 83.0 \| 0.3855 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 19.0 \| 57 \| 7.8124 \| 0.0063 \| 3369.9946 \| 2335.9022 \| 114.0 \| 299.0 \| 0.3813 \| 114.0 \| 0.3813 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 26.0 \| 26.0 \| 78.0 \| 0.3333 \| 0.3333 \| 31.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 20.0 \| 60 \| 7.7234 \| 0.0063 \| 3331.5996 \| 2309.2889 \| 115.0 \| 299.0 \| 0.3846 \| 115.0 \| 0.3846 \| 30.0 \| 30.0 \| 64.0 \| 0.4688 \| 0.4688 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 26.0 \| 26.0 \| 78.0 \| 0.3333 \| 0.3333 \| 31.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 21.0 \| 63 \| 7.7679 \| 0.0063 \| 3350.8072 \| 2322.6025 \| 115.0 \| 299.0 \| 0.3846 \| 115.0 \| 0.3846 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 27.0 \| 27.0 \| 78.0 \| 0.3462 \| 0.3462 \| 31.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 22.0 \| 66 \| 7.7526 \| 0.0063 \| 3344.1986 \| 2318.0219 \| 115.0 \| 299.0 \| 0.3846 \| 115.0 \| 0.3846 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 27.0 \| 27.0 \| 78.0 \| 0.3462 \| 0.3462 \| 31.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 23.0 \| 69 \| 7.7910 \| 0.0063 \| 3360.7764 \| 2329.5127 \| 112.0 \| 299.0 \| 0.3746 \| 112.0 \| 0.3746 \| 28.0 \| 28.0 \| 64.0 \| 0.4375 \| 0.4375 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 25.0 \| 25.0 \| 78.0 \| 0.3205 \| 0.3205 \| 31.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 24.0 \| 72 \| 7.7183 \| 0.0063 \| 3329.3986 \| 2307.7632 \| 115.0 \| 299.0 \| 0.3846 \| 115.0 \| 0.3846 \| 30.0 \| 30.0 \| 64.0 \| 0.4688 \| 0.4688 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 26.0 \| 26.0 \| 78.0 \| 0.3333 \| 0.3333 \| 31.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 25.0 \| 75 \| 7.7304 \| 0.0063 \| 3334.6225 \| 2311.3842 \| 114.0 \| 299.0 \| 0.3813 \| 114.0 \| 0.3813 \| 30.0 \| 30.0 \| 64.0 \| 0.4688 \| 0.4688 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 25.0 \| 25.0 \| 78.0 \| 0.3205 \| 0.3205 \| 31.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 26.0 \| 78 \| 7.7551 \| 0.0063 \| 3345.2652 \| 2318.7612 \| 114.0 \| 299.0 \| 0.3813 \| 113.0 \| 0.3779 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 25.0 \| 25.0 \| 78.0 \| 0.3205 \| 0.3205 \| 31.0 \| 32.0 \| 83.0 \| 0.3855 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 27.0 \| 81 \| 7.7737 \| 0.0063 \| 3353.3180 \| 2324.3429 \| 116.0 \| 299.0 \| 0.3880 \| 115.0 \| 0.3846 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 27.0 \| 27.0 \| 78.0 \| 0.3462 \| 0.3462 \| 31.0 \| 32.0 \| 83.0 \| 0.3855 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 28.0 \| 84 \| 7.7507 \| 0.0063 \| 3343.3752 \| 2317.4511 \| 116.0 \| 299.0 \| 0.3880 \| 115.0 \| 0.3846 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 27.0 \| 27.0 \| 78.0 \| 0.3462 \| 0.3462 \| 31.0 \| 32.0 \| 83.0 \| 0.3855 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 29.0 \| 87 \| 7.7632 \| 0.0063 \| 3348.7890 \| 2321.2037 \| 116.0 \| 299.0 \| 0.3880 \| 115.0 \| 0.3846 \| 30.0 \| 30.0 \| 64.0 \| 0.4688 \| 0.4688 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 26.0 \| 26.0 \| 78.0 \| 0.3333 \| 0.3333 \| 31.0 \| 32.0 \| 83.0 \| 0.3855 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 30.0 \| 90 \| 7.7401 \| 0.0063 \| 3338.8227 \| 2314.2955 \| 116.0 \| 299.0 \| 0.3880 \| 115.0 \| 0.3846 \| 30.0 \| 30.0 \| 64.0 \| 0.4688 \| 0.4688 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 26.0 \| 26.0 \| 78.0 \| 0.3333 \| 0.3333 \| 31.0 \| 32.0 \| 83.0 \| 0.3855 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 31.0 \| 93 \| 7.7578 \| 0.0063 \| 3346.4502 \| 2319.5825 \| 113.0 \| 299.0 \| 0.3779 \| 113.0 \| 0.3779 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 25.0 \| 25.0 \| 78.0 \| 0.3205 \| 0.3205 \| 31.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 32.0 \| 96 \| 7.7958 \| 0.0063 \| 3362.8384 \| 2330.9420 \| 112.0 \| 299.0 \| 0.3746 \| 112.0 \| 0.3746 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 27.0 \| 27.0 \| 73.0 \| 0.3699 \| 0.3699 \| 25.0 \| 25.0 \| 78.0 \| 0.3205 \| 0.3205 \| 31.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 33.0 \| 99 \| 7.7635 \| 0.0063 \| 3348.8919 \| 2321.2750 \| 114.0 \| 299.0 \| 0.3813 \| 114.0 \| 0.3813 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 26.0 \| 26.0 \| 78.0 \| 0.3333 \| 0.3333 \| 31.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 34.0 \| 102 \| 7.7796 \| 0.0063 \| 3355.8332 \| 2326.0863 \| 114.0 \| 299.0 \| 0.3813 \| 113.0 \| 0.3779 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 25.0 \| 25.0 \| 78.0 \| 0.3205 \| 0.3205 \| 31.0 \| 32.0 \| 83.0 \| 0.3855 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 35.0 \| 105 \| 7.7913 \| 0.0063 \| 3360.8969 \| 2329.5962 \| 113.0 \| 299.0 \| 0.3779 \| 113.0 \| 0.3779 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 25.0 \| 25.0 \| 78.0 \| 0.3205 \| 0.3205 \| 31.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 36.0 \| 108 \| 7.7978 \| 0.0063 \| 3363.7162 \| 2331.5504 \| 112.0 \| 299.0 \| 0.3746 \| 112.0 \| 0.3746 \| 29.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4531 \| 28.0 \| 28.0 \| 73.0 \| 0.3836 \| 0.3836 \| 24.0 \| 24.0 \| 78.0 \| 0.3077 \| 0.3077 \| 31.0 \| 31.0 \| 83.0 \| 0.3735 \| 0.3735 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755557207	AminuPeril	2025-08-18T22:47:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:47:12Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755555569	vwzyrraz7l	2025-08-18T22:46:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:45:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dejiat/blockassist-bc-savage_unseen_bobcat_1755557050	Dejiat	2025-08-18T22:44:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:44:49Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
emre0005/blockassist-bc-humming_winged_okapi_1755557030	emre0005	2025-08-18T22:44:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "humming winged okapi", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:44:27Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - humming winged okapi --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
seraphimzzzz/1410215	seraphimzzzz	2025-08-18T22:42:54Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:42:47Z	[View on Civ Archive](https://civarchive.com/models/1252497?modelVersionId=1510150)
crystalline7/1474712	crystalline7	2025-08-18T22:42:36Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:42:32Z	[View on Civ Archive](https://civarchive.com/models/1333275?modelVersionId=1574869)
ultratopaz/1488010	ultratopaz	2025-08-18T22:42:25Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:42:18Z	[View on Civ Archive](https://civarchive.com/models/1266645?modelVersionId=1588242)
johngreendr1/b3b0ee9e-8d46-4e86-96b5-3dfd60d79bf1	johngreendr1	2025-08-18T22:42:15Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:jhflow/mistral7b-lora-multi-turn-v2", "base_model:adapter:jhflow/mistral7b-lora-multi-turn-v2", "region:us" ]	null	2025-08-18T19:21:57Z	--- base_model: jhflow/mistral7b-lora-multi-turn-v2 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
Azurastar2903/Llama-3.2-1B-rk3588-1.2.1	Azurastar2903	2025-08-18T22:41:28Z	0	0	transformers	[ "transformers", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "arxiv:2405.16406", "license:llama3.2", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T22:03:15Z	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers license: llama3.2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version\ \ Release Date: September 25, 2024\n\n“Agreement” means the terms and conditions\ \ for use, reproduction, distribution and modification of the Llama Materials set\ \ forth herein.\n\n“Documentation” means the specifications, manuals and documentation\ \ accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview.\n\ \n“Licensee” or “you” means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf),\ \ of the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\n“Llama 3.2”\ \ means the foundational large language models and software and algorithms, including\ \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\ \ code, fine-tuning enabling code and other elements of the foregoing distributed\ \ by Meta at https://www.llama.com/llama-downloads.\n\n“Llama Materials” means,\ \ collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion\ \ thereof) made available under this Agreement.\n\n“Meta” or “we” means Meta Platforms\ \ Ireland Limited (if you are located in or, if you are an entity, your principal\ \ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if\ \ you are located outside of the EEA or Switzerland). \n\nBy clicking “I Accept”\ \ below or by using or distributing any portion or element of the Llama Materials,\ \ you agree to be bound by this Agreement.\n\n1. License Rights and Redistribution.\n\ a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable\ \ and royalty-free limited license under Meta’s intellectual property or other rights\ \ owned by Meta embodied in the Llama Materials to use, reproduce, distribute,\ \ copy, create derivative works of, and make modifications to the Llama Materials.\ \ \nb. Redistribution and Use. \ni. If you distribute or make available the Llama\ \ Materials (or any derivative works thereof), or a product or service (including\ \ another AI model) that contains any of them, you shall (A) provide a copy of this\ \ Agreement with any such Llama Materials; and (B) prominently display “Built with\ \ Llama” on a related website, user interface, blogpost, about page, or product\ \ documentation. If you use the Llama Materials or any outputs or results of the\ \ Llama Materials to create, train, fine tune, or otherwise improve an AI model,\ \ which is distributed or made available, you shall also include “Llama” at the\ \ beginning of any such AI model name.\nii. If you receive Llama Materials, or any\ \ derivative works thereof, from a Licensee as part of an integrated end user product,\ \ then Section 2 of this Agreement will not apply to you. \niii. You must retain\ \ in all copies of the Llama Materials that you distribute the following attribution\ \ notice within a “Notice” text file distributed as a part of such copies: “Llama\ \ 3.2 is licensed under the Llama 3.2 Community License, Copyright © Meta Platforms,\ \ Inc. All Rights Reserved.”\niv. Your use of the Llama Materials must comply with\ \ applicable laws and regulations (including trade compliance laws and regulations)\ \ and adhere to the Acceptable Use Policy for the Llama Materials (available at\ \ https://www.llama.com/llama3_2/use-policy), which is hereby incorporated by reference\ \ into this Agreement.\n \n2. Additional Commercial Terms. If, on the Llama 3.2\ \ version release date, the monthly active users of the products or services made\ \ available by or for Licensee, or Licensee’s affiliates, is greater than 700 million\ \ monthly active users in the preceding calendar month, you must request a license\ \ from Meta, which Meta may grant to you in its sole discretion, and you are not\ \ authorized to exercise any of the rights under this Agreement unless or until\ \ Meta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS\ \ REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM\ \ ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS\ \ ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION,\ \ ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR\ \ PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING\ \ OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR\ \ USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability.\ \ IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY,\ \ WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING\ \ OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL,\ \ INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE\ \ BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\n\ a. No trademark licenses are granted under this Agreement, and in connection with\ \ the Llama Materials, neither Meta nor Licensee may use any name or mark owned\ \ by or associated with the other or any of its affiliates, except as required\ \ for reasonable and customary use in describing and redistributing the Llama Materials\ \ or as set forth in this Section 5(a). Meta hereby grants you a license to use\ \ “Llama” (the “Mark”) solely as required to comply with the last sentence of Section\ \ 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at\ \ https://about.meta.com/brand/resources/meta/company-brand/). All goodwill arising\ \ out of your use of the Mark will inure to the benefit of Meta.\nb. Subject to\ \ Meta’s ownership of Llama Materials and derivatives made by or for Meta, with\ \ respect to any derivative works and modifications of the Llama Materials that\ \ are made by you, as between you and Meta, you are and will be the owner of such\ \ derivative works and modifications.\nc. If you institute litigation or other proceedings\ \ against Meta or any entity (including a cross-claim or counterclaim in a lawsuit)\ \ alleging that the Llama Materials or Llama 3.2 outputs or results, or any portion\ \ of any of the foregoing, constitutes infringement of intellectual property or\ \ other rights owned or licensable by you, then any licenses granted to you under\ \ this Agreement shall terminate as of the date such litigation or claim is filed\ \ or instituted. You will indemnify and hold harmless Meta from and against any\ \ claim by any third party arising out of or related to your use or distribution\ \ of the Llama Materials.\n6. Term and Termination. The term of this Agreement will\ \ commence upon your acceptance of this Agreement or access to the Llama Materials\ \ and will continue in full force and effect until terminated in accordance with\ \ the terms and conditions herein. Meta may terminate this Agreement if you are\ \ in breach of any term or condition of this Agreement. Upon termination of this\ \ Agreement, you shall delete and cease use of the Llama Materials. Sections 3,\ \ 4 and 7 shall survive the termination of this Agreement. \n7. Governing Law and\ \ Jurisdiction. This Agreement will be governed and construed under the laws of\ \ the State of California without regard to choice of law principles, and the UN\ \ Convention on Contracts for the International Sale of Goods does not apply to\ \ this Agreement. The courts of California shall have exclusive jurisdiction of\ \ any dispute arising out of this Agreement. \n### Llama 3.2 Acceptable Use Policy\n\ Meta is committed to promoting safe and fair use of its tools and features, including\ \ Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy\ \ (“Policy”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\n\ #### Prohibited Uses\nWe want everyone to use Llama 3.2 safely and responsibly.\ \ You agree you will not use, or allow others to use, Llama 3.2 to:\n1. Violate\ \ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\ \ contribute to, encourage, plan, incite, or further illegal or unlawful activity\ \ or content, such as:\n 1. Violence or terrorism\n 2. Exploitation\ \ or harm to children, including the solicitation, creation, acquisition, or dissemination\ \ of child exploitative content or failure to report Child Sexual Abuse Material\n\ \ 3. Human trafficking, exploitation, and sexual violence\n 4. The\ \ illegal distribution of information or materials to minors, including obscene\ \ materials, or failure to employ legally required age-gating in connection with\ \ such information or materials.\n 5. Sexual solicitation\n 6. Any\ \ other criminal activity\n 1. Engage in, promote, incite, or facilitate the\ \ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\ \ 2. Engage in, promote, incite, or facilitate discrimination or other unlawful\ \ or harmful conduct in the provision of employment, employment benefits, credit,\ \ housing, other economic benefits, or other essential goods and services\n 3.\ \ Engage in the unauthorized or unlicensed practice of any profession including,\ \ but not limited to, financial, legal, medical/health, or related professional\ \ practices\n 4. Collect, process, disclose, generate, or infer private or sensitive\ \ information about individuals, including information about individuals’ identity,\ \ health, or demographic information, unless you have obtained the right to do so\ \ in accordance with applicable law\n 5. Engage in or facilitate any action or\ \ generate any content that infringes, misappropriates, or otherwise violates any\ \ third-party rights, including the outputs or results of any products or services\ \ using the Llama Materials\n 6. Create, generate, or facilitate the creation\ \ of malicious code, malware, computer viruses or do anything else that could disable,\ \ overburden, interfere with or impair the proper working, integrity, operation\ \ or appearance of a website or computer system\n 7. Engage in any action, or\ \ facilitate any action, to intentionally circumvent or remove usage restrictions\ \ or other safety measures, or to enable functionality disabled by Meta \n2. Engage\ \ in, promote, incite, facilitate, or assist in the planning or development of activities\ \ that present a risk of death or bodily harm to individuals, including use of Llama\ \ 3.2 related to the following:\n 8. Military, warfare, nuclear industries or\ \ applications, espionage, use for materials or activities that are subject to the\ \ International Traffic Arms Regulations (ITAR) maintained by the United States\ \ Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989\ \ or the Chemical Weapons Convention Implementation Act of 1997\n 9. Guns and\ \ illegal weapons (including weapon development)\n 10. Illegal drugs and regulated/controlled\ \ substances\n 11. Operation of critical infrastructure, transportation technologies,\ \ or heavy machinery\n 12. Self-harm or harm to others, including suicide, cutting,\ \ and eating disorders\n 13. Any content intended to incite or promote violence,\ \ abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive\ \ or mislead others, including use of Llama 3.2 related to the following:\n 14.\ \ Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n\ \ 15. Generating, promoting, or furthering defamatory content, including the\ \ creation of defamatory statements, images, or other content\n 16. Generating,\ \ promoting, or further distributing spam\n 17. Impersonating another individual\ \ without consent, authorization, or legal right\n 18. Representing that the\ \ use of Llama 3.2 or outputs are human-generated\n 19. Generating or facilitating\ \ false online engagement, including fake reviews and other means of fake online\ \ engagement \n4. Fail to appropriately disclose to end users any known dangers\ \ of your AI system 5. Interact with third party tools, models, or software designed\ \ to generate unlawful content or engage in unlawful or harmful conduct and/or represent\ \ that the outputs of such tools, models, or software are associated with Meta or\ \ Llama 3.2\n\nWith respect to any multimodal models included in Llama 3.2, the\ \ rights granted under Section 1(a) of the Llama 3.2 Community License Agreement\ \ are not being granted to you if you are an individual domiciled in, or a company\ \ with a principal place of business in, the European Union. This restriction does\ \ not apply to end users of a product or service that incorporates any such multimodal\ \ models.\n\nPlease report any violation of this Policy, software “bug,” or other\ \ problems that could lead to a violation of this Policy through one of the following\ \ means:\n\n* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\n\ * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\n\ * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\n\ * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama\ \ 3.2: LlamaUseReport@meta.com" extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location ? By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- # Llama-3.2-1B-RK3588-1.2.1 This version of Llama-3.2-1B has been converted to run on the RK3588 NPU using w8a8_g256 quantization. This model has been optimized with the following LoRA: Compatible with RKLLM version: 1.2.1 ## Useful links: [Official RKLLM GitHub](https://github.com/airockchip/rknn-llm) [RockhipNPU Reddit](https://reddit.com/r/RockchipNPU) [EZRKNN-LLM](https://github.com/Pelochus/ezrknn-llm/) Pretty much anything by these folks: [marty1885](https://github.com/marty1885) and [happyme531](https://huggingface.co/happyme531) Converted using https://github.com/c0zaut/ez-er-rkllm-toolkit # Original Model Card for base model, Llama-3.2-1B, below: ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. \| \| Training Data \| Params \| Input modalities \| Output modalities \| Context Length \| GQA \| Shared Embeddings \| Token count \| Knowledge cutoff \| \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| \| Llama 3.2 (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 128k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| \| Llama 3.2 Quantized (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 8k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: Sept 25, 2024 Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. License: Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Feedback: Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases: Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. Out of Scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-1B, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function. Make sure to update your transformers installation via pip install --upgrade transformers. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-1B" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto" ) pipe("The key to life is") ``` ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-1B --include "original/" --local-dir Llama-3.2-1B ``` ## Hardware and Software Training Factors:* We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Training utilized a cumulative of 916k GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. Training Greenhouse Gas Emissions: Estimated total location-based greenhouse gas emissions were 240 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. \| \| Training Time (GPU hours) \| Logit Generation Time (GPU Hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| ----- \| :---: \| :---: \| :---: \| \| Llama 3.2 1B \| 370k \| \- \| 700 \| 107 \| 0 \| \| Llama 3.2 3B \| 460k \| \- \| 700 \| 133 \| 0 \| \| Llama 3.2 1B SpinQuant \| 1.7 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 3B SpinQuant \| 2.4 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 1B QLora \| 1.3k \| 0 \| 700 \| 0.381 \| 0 \| \| Llama 3.2 3B QLora \| 1.6k \| 0 \| 700 \| 0.461 \| 0 \| \| Total \| 833k \| 86k \| \| 240 \| 0 \| \\ The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). Data Freshness: The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B \| Llama 3.2 3B \| Llama 3.1 8B \| \| ----- \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU \| 5 \| macro\_avg/acc\_char \| 32.2 \| 58 \| 66.7 \| \| \| AGIEval English \| 3-5 \| average/acc\_char \| 23.3 \| 39.2 \| 47.8 \| \| \| ARC-Challenge \| 25 \| acc\_char \| 32.8 \| 69.1 \| 79.7 \| \| Reading comprehension \| SQuAD \| 1 \| em \| 49.2 \| 67.7 \| 77 \| \| \| QuAC (F1) \| 1 \| f1 \| 37.9 \| 42.9 \| 44.9 \| \| \| DROP (F1) \| 3 \| f1 \| 28.0 \| 45.2 \| 59.5 \| \| Long Context \| Needle in Haystack \| 0 \| em \| 96.8 \| 1 \| 1 \| ### Instruction Tuned Models \| Capability \| \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B bf16 \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B bf16 \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| \| MMLU \| 5 \| macro\_avg/acc \| 49.3 \| 43.3 \| 47.3 \| 49.0 \| 63.4 \| 60.5 \| 62 \| 62.4 \| 69.4 \| \| Re-writing \| \| Open-rewrite eval \| 0 \| micro\_avg/rougeL \| 41.6 \| 39.2 \| 40.9 \| 41.2 \| 40.1 \| 40.3 \| 40.8 \| 40.7 \| 40.9 \| \| Summarization \| \| TLDR9+ (test) \| 1 \| rougeL \| 16.8 \| 14.9 \| 16.7 \| 16.8 \| 19.0 \| 19.1 \| 19.2 \| 19.1 \| 17.2 \| \| Instruction following \| \| IFEval \| 0 \| Avg(Prompt/Instruction acc Loose/Strict) \| 59.5 \| 51.5 \| 58.4 \| 55.6 \| 77.4 \| 73.9 \| 73.5 \| 75.9 \| 80.4 \| \| Math \| \| GSM8K (CoT) \| 8 \| em\_maj1@1 \| 44.4 \| 33.1 \| 40.6 \| 46.5 \| 77.7 \| 72.9 \| 75.7 \| 77.9 \| 84.5 \| \| \| \| MATH (CoT) \| 0 \| final\_em \| 30.6 \| 20.5 \| 25.3 \| 31.0 \| 48.0 \| 44.2 \| 45.3 \| 49.2 \| 51.9 \| \| Reasoning \| \| ARC-C \| 0 \| acc \| 59.4 \| 54.3 \| 57 \| 60.7 \| 78.6 \| 75.6 \| 77.6 \| 77.6 \| 83.4 \| \| \| \| GPQA \| 0 \| acc \| 27.2 \| 25.9 \| 26.3 \| 25.9 \| 32.8 \| 32.8 \| 31.7 \| 33.9 \| 32.8 \| \| \| \| Hellaswag \| 0 \| acc \| 41.2 \| 38.1 \| 41.3 \| 41.5 \| 69.8 \| 66.3 \| 68 \| 66.3 \| 78.7 \| \| Tool Use \| \| BFCL V2 \| 0 \| acc \| 25.7 \| 14.3 \| 15.9 \| 23.7 \| 67.0 \| 53.4 \| 60.1 \| 63.5 \| 67.1 \| \| \| \| Nexus \| 0 \| macro\_avg/acc \| 13.5 \| 5.2 \| 9.6 \| 12.5 \| 34.3 \| 32.4 \| 31.5 \| 30.1 \| 38.5 \| \| Long Context \| \| InfiniteBench/En.QA \| 0 \| longbook\_qa/f1 \| 20.3 \| N/A \| N/A \| N/A \| 19.8 \| N/A \| N/A \| N/A \| 27.3 \| \| \| \| InfiniteBench/En.MC \| 0 \| longbook\_choice/acc \| 38.0 \| N/A \| N/A \| N/A \| 63.3 \| N/A \| N/A \| N/A \| 72.2 \| \| \| \| NIH/Multi-needle \| 0 \| recall \| 75.0 \| N/A \| N/A \| N/A \| 84.7 \| N/A \| N/A \| N/A \| 98.8 \| \| Multilingual \| \| MGSM (CoT) \| 0 \| em \| 24.5 \| 13.7 \| 18.2 \| 24.4 \| 58.2 \| 48.9 \| 54.3 \| 56.8 \| 68.9 \| \\for comparison purposes only. Model not released. ### Multilingual Benchmarks \| Category \| Benchmark \| Language \| Llama 3.2 1B \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU (5-shot, macro_avg/acc) \| Portuguese \| 39.8 \| 34.9 \| 38.9 \| 40.2 \| 54.5 \| 50.9 \| 53.3 \| 53.4 \| 62.1 \| \| \| \| Spanish \| 41.5 \| 36.0 \| 39.8 \| 41.8 \| 55.1 \| 51.9 \| 53.6 \| 53.6 \| 62.5 \| \| \| \| Italian \| 39.8 \| 34.9 \| 38.1 \| 40.6 \| 53.8 \| 49.9 \| 52.1 \| 51.7 \| 61.6 \| \| \| \| German \| 39.2 \| 34.9 \| 37.5 \| 39.6 \| 53.3 \| 50.0 \| 52.2 \| 51.3 \| 60.6 \| \| \| \| French \| 40.5 \| 34.8 \| 39.2 \| 40.8 \| 54.6 \| 51.2 \| 53.3 \| 53.3 \| 62.3 \| \| \| \| Hindi \| 33.5 \| 30.0 \| 32.1 \| 34.0 \| 43.3 \| 40.4 \| 42.0 \| 42.1 \| 50.9 \| \| \| \| Thai \| 34.7 \| 31.2 \| 32.4 \| 34.9 \| 44.5 \| 41.3 \| 44.0 \| 42.2 \| 50.3 \| \\for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. \| Category \| Decode (tokens/sec) \| Time-to-first-token (sec) \| Prefill (tokens/sec) \| Model size (PTE file size in MB) \| Memory size (RSS in MB) \| \| :---- \| ----- \| ----- \| ----- \| ----- \| ----- \| \| 1B BF16 (baseline) \| 19.2 \| 1.0 \| 60.3 \| 2358 \| 3,185 \| \| 1B SpinQuant \| 50.2 (2.6x) \| 0.3 (-76.9%) \| 260.5 (4.3x) \| 1083 (-54.1%) \| 1,921 (-39.7%) \| \| 1B QLoRA \| 45.8 (2.4x) \| 0.3 (-76.0%) \| 252.0 (4.2x) \| 1127 (-52.2%) \| 2,255 (-29.2%) \| \| 3B BF16 (baseline) \| 7.6 \| 3.0 \| 21.2 \| 6129 \| 7,419 \| \| 3B SpinQuant \| 19.7 (2.6x) \| 0.7 (-76.4%) \| 89.7 (4.2x) \| 2435 (-60.3%) \| 3,726 (-49.8%) \| \| 3B QLoRA \| 18.5 (2.4x) \| 0.7 (-76.1%) \| 88.8 (4.2x) \| 2529 (-58.7%) \| 4,060 (-45.3%) \| (\) The performance measurement is done using an adb binary-based approach. (\\) It is measured on an Android OnePlus 12 device. (\\\) Time-to-first-token (TTFT) is measured with prompt length=64 Footnote: - Decode (tokens/second) is for how quickly it keeps generating. Higher is better. - Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better. - Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better - Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch - RSS size \- Memory usage in resident set size (RSS) ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment Approach: Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct Objective: Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). Fine-Tuning Data: We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals and Tone: Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems Safety as a System: Large language models, including Llama 3.2, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases Technological Advancement: Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. Constrained Environments: Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations Scaled Evaluations: We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Red Teaming: We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: 1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons): Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. 2\. Child Safety: Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 3\. Cyber Attacks: For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community Industry Partnerships: Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Grants: We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Reporting: Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations Values: The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Testing: Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
KS190/diffusion_pick_0816	KS190	2025-08-18T22:40:31Z	0	0	lerobot	[ "lerobot", "safetensors", "diffusion", "robotics", "dataset:KS190/pick_0816", "arxiv:2303.04137", "license:apache-2.0", "region:us" ]	robotics	2025-08-18T22:39:30Z	--- datasets: KS190/pick_0816 library_name: lerobot license: apache-2.0 model_name: diffusion pipeline_tag: robotics tags: - diffusion - robotics - lerobot --- # Model Card for diffusion <!-- Provide a quick summary of what the model is/does. --> [Diffusion Policy](https://huggingface.co/papers/2303.04137) treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash lerobot-record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755555300	lisaozill03	2025-08-18T22:39:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:39:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
emre0005/blockassist-bc-humming_winged_okapi_1755556696	emre0005	2025-08-18T22:39:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "humming winged okapi", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:39:02Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - humming winged okapi --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755556578	IvanJAjebu	2025-08-18T22:38:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:37:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
crystalline7/1445316	crystalline7	2025-08-18T22:37:39Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:37:32Z	[View on Civ Archive](https://civarchive.com/models/1146289?modelVersionId=1545549)
crystalline7/357893	crystalline7	2025-08-18T22:37:13Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:37:10Z	[View on Civ Archive](https://civarchive.com/models/388958?modelVersionId=437409)
AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755556574	AminuPeril	2025-08-18T22:36:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:36:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
crystalline7/1274312	crystalline7	2025-08-18T22:36:07Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:36:04Z	[View on Civ Archive](https://civarchive.com/models/1217433?modelVersionId=1371374)
crystalline7/1520088	crystalline7	2025-08-18T22:35:53Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:35:50Z	[View on Civ Archive](https://civarchive.com/models/1433264?modelVersionId=1620124)
ultratopaz/1374056	ultratopaz	2025-08-18T22:35:25Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:35:21Z	[View on Civ Archive](https://civarchive.com/models/1305038?modelVersionId=1472649)
ultratopaz/1204501	ultratopaz	2025-08-18T22:35:10Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:35:07Z	[View on Civ Archive](https://civarchive.com/models/1156124?modelVersionId=1300266)
ultratopaz/1301225	ultratopaz	2025-08-18T22:34:28Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:34:25Z	[View on Civ Archive](https://civarchive.com/models/1241196?modelVersionId=1398885)
crystalline7/1480092	crystalline7	2025-08-18T22:34:00Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:33:57Z	[View on Civ Archive](https://civarchive.com/models/1398112?modelVersionId=1580334)
smirki/UIGEN-X-4B-SFT-QLoRA-epoch-2.0	smirki	2025-08-18T22:33:58Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-18T22:33:54Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
crystalline7/1399367	crystalline7	2025-08-18T22:33:53Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:33:50Z	[View on Civ Archive](https://civarchive.com/models/1328064?modelVersionId=1499540)
crystalline7/1488590	crystalline7	2025-08-18T22:33:29Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:33:21Z	[View on Civ Archive](https://civarchive.com/models/1279990?modelVersionId=1588804)
AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755556372	AminuPeril	2025-08-18T22:33:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:33:18Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Yukiyoke-Lab/TAR-model	Yukiyoke-Lab	2025-08-18T22:33:22Z	0	0	null	[ "onnx", "text-to-speech", "ja", "dataset:Yukiyoke-Lab/Tsukuyomi-chan_datasets", "base_model:Yukiyoke-Lab/Tsukuyomi-chan", "base_model:quantized:Yukiyoke-Lab/Tsukuyomi-chan", "license:other", "region:us" ]	text-to-speech	2025-08-16T08:49:44Z	--- language: - ja pipeline_tag: text-to-speech datasets: - Yukiyoke-Lab/Tsukuyomi-chan_datasets license: other base_model: - Yukiyoke-Lab/Tsukuyomi-chan --- インストール方法は下に記載しています。ライセンスに同意し、よくREADMEを読んだ上でダウンロード・インストールを行ってください。 # モデルについて本モデルは、「つくよみちゃん」、その他の音源を学習・マージして作成されています。<br> 「つくよみちゃん」公式が提供している合成音声モデルではありません。 <br> つくよみちゃん以外の合成音声モデルは公開しておりません。<br> ハイパーパラメータ、マージ時のパラメータ等やエポック数等、一部情報を削除しています。<br> つくよみちゃんモデルに関しては、[別リポジトリ](https://huggingface.co/Yukiyoke-Lab/Tsukuyomi-chan)を参照してください。<br> ONNX形式ファイルは、Safetensors形式ファイルを変換して作成しています。<br> 変換には、[litagin02/Style-Bert-VITS2/convert_bert_onnx.py](https://github.com/litagin02/Style-Bert-VITS2/blob/dev/convert_bert_onnx.py)を利用しています。<br> <details> <summary>デモ音声</summary> ## デモ音声 Aivisspeechにて生成した無調整音声です。<br> 音声の調整はパラメータ調整を含め、行っておりません。<br> <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/668a2a06aafa84bf3c3f1797/gttoi1AYXDwC4TGc4h7ZI.wav"></audio> ``` やったー！テストで満点取れた！私とっても嬉しいな！どうして私の意見を無視するの？許せない！ムカつく！あんたなんか死ねばいいのに。あはははっ！この漫画めっちゃ笑える、見てよこれ、ふふふ、あはは。あなたがいなくなって、私は一人になっちゃって、泣いちゃいそうなほど悲しい。 Aivisspeechバージョン：Aivisspeech 1.1.0-preview.2 モデルバージョン：1.0.0 ``` <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/668a2a06aafa84bf3c3f1797/yEDIC7ppt16svu9tCkTV5.wav"></audio> ``` 音声合成は、機械学習を活用して、テキストから人の声を再現する技術です。この技術は、言語の構造を解析し、それに基づいて音声を生成します。この分野の最新の研究成果を使うと、より自然で表現豊かな音声の生成が可能である。深層学習の応用により、感情やアクセントを含む声質の微妙な変化も再現することが出来る。 Aivisspeechバージョン：Aivisspeech 1.1.0-preview.2 モデルバージョン：1.0.0 ``` </details> ## Aivisspeech対応ファイル Aivisspeech形式ファイルは、拡張子が```.aivm``` ```.aivmx```のものとします。<br> Aivisspeech形式ファイルは、以下の通りにファイル名を命名する。<br> ```TAR-v1.0.0.aivmx``` ```TAR-v1.0.0.aivm```<br> ```モデル名```-v```バージョン``` ```拡張子``` --- ## バージョニングセマンティックバージョニングに基づいてバージョニングをし、音声モデルの形式にかかわらず、すべてでバージョンを統一します。 > Aivisspeech・Style-Birt-Vits2対応ファイルその他に関わらず、アップデートした場合に、共通して同一バージョンを割り振ります。バージョンアップの基準は、マージ前モデルの追加学習等で音声の音質が変化した場合、マイナーバージョンをアップします。メジャーバージョンは、メタデータ編集等により互換性が無くなった場合にアップします。 > Aivisspeechでは、ハイパーパラメータ・UUIDを変更すると同一モデル扱いではなくなるため。 --- # ライセンス・クレジット表記本リポジトリ・モデルは```Aivis Common Model License (ACML) - Non Commercial 1.0```でライセンスされています。 ## 共通ライセンスすべての他のライセンスよりも共通ライセンスが優先されます。<br> ACMLライセンスよりも、共通ライセンスが優先されます。本モデルの音声合成には、フリー素材キャラクター「つくよみちゃん」（© Rei Yumesaki）が無料公開している音声データを一部使用し、そこから作成されたマージモデルです。<br> 「つくよみちゃん」公式が提供している合成音声モデルではありません。 ■つくよみちゃんコーパス（CV.夢前黎）<br> https://tyc.rei-yumesaki.net/material/corpus/ ■つくよみちゃんキャラクターライセンス<br> https://tyc.rei-yumesaki.net/about/terms/ ■つくよみちゃん公式サイト<br> https://tyc.rei-yumesaki.net/ - つくよみちゃんコーパスに由来する部分の取り扱いについては「つくよみちゃんコーパス」、「つくよみちゃんキャラクターライセンス」の利用規約に従うこととします。 ### 独自ライセンス独自ライセンスは、共通ライセンスと同一として解釈されます。<br> - このライセンスの全ては修正・変更、それに限らずを告知せずとも行うことができます。<br> - ライセンスは記載が無い限り改定の翌日から効力を持ちます。 - ライセンスの改変がされても改変以前に一般に公開されたコンテンツに関しては、以前のライセンスがそのまま適用されます。<br> - このライセンスはすべて日本語で提供され、日本語のみにより解釈されるものとします。 - クレジット表記は原則行う必要があります。 - つくよみちゃんキャラクターライセンスと異なり、ニコニコ動画、Youtubeそれに限らずクレジット表記を原則行う必要があります。 - クレジット表記の表記方法は自由ですが、つくよみちゃんのクレジット表記、マージモデルの作成者・配布先のクレジット表記を目立つ箇所に記載してください。 - クレジット表記が困難な場合、行わないことも可能です。 > 詳しくは、こちらを参考にしてください。ただし、共通・独自ライセンスも遵守する必要があります。https://tyc.rei-yumesaki.net/about/terms/credit/ ``` 音声合成には、フリー素材キャラクター「つくよみちゃん」（© Rei Yumesaki）が無料公開している音声データを一部使用し、akikukeo氏によって利用規約に基づき作成されたマージモデルを使用しています。「つくよみちゃん」公式が提供している合成音声モデルではありません。利用規約に基づき制作されています。つくよみちゃんの言動は、投稿者の言動が反映されたものであり、公式のつくよみちゃんとは一切関係ありません。 ■つくよみちゃんコーパス（CV.夢前黎） https://tyc.rei-yumesaki.net/material/corpus/ ■つくよみちゃんキャラクターライセンス https://tyc.rei-yumesaki.net/about/terms/ ■つくよみちゃん公式サイト https://tyc.rei-yumesaki.net/ 「つくよみちゃんコーパスライセンス」「つくよみちゃんキャラクターライセンス」以外にも規約が適用されます。ご注意ください。詳しくは以下マージモデル配布先をご覧ください。 ■モデル配布先（マージモデル） https://huggingface.co/Yukiyoke-Lab/TAR-model © Rei Yumesaki, YukiyokeLab, Akikukeo ``` --- Aivis Common Model License (ACML) - Non Commercial 1.0<br> https://github.com/Aivis-Project/ACML/blob/master/ACML-NC-1.0.md より引用 <details> <summary>Aivis Common Model License (ACML) - Non Commercial 1.0</summary> # Aivis Common Model License (ACML) - Non Commercial 1.0 このライセンスは、AI 音声合成モデルの利用条件と制限を定めるものです。音声合成技術の発展により、誰もが簡単に高品質な合成音声を作れるようになりました。この技術は、創作活動はもちろん、AI との対話や新しいサービスの開発など、さまざまな可能性を広げる革新的な手段として注目されています。現在、音声合成モデルのライセンスは制作者ごとにまちまちで、利用条件も曖昧なことが多く、使いづらい状況が続いています。 Aivis Project が策定する ACML は、音声合成モデルの自由な利用を促進しながら、制作者と利用者の双方にとって安心して活用できる環境を実現することを目指しています。また、多くの制作者に共通のライセンスとして採用していただくことで、モデルごとの規約に気を遣うことなく、誰もが安心して音声合成モデルを利用できる環境づくりを目指しています。このライセンスは、あなたに以下の権利を許諾します。 - ✅ この音声合成モデルの利用・複製・改変・派生物の作成 - 音声合成モデルを実行して音声を生成することはもちろん、モデルの複製や改変、派生モデルの作成も自由に行えます。 - ✅ この音声合成モデルやその派生物の配布 - このライセンスの条件に従う限り、改変の有無を問わず、自由に再配布することができます。ただし、これらの権利は「できないこと（禁止事項）」に定める制限に従うものとします。 ## 用語の定義 - 話者: 話し手 (Speaker) のことをいいます。話者には「実在人物」の声と「キャラクター」の声の両方を含みます。 - 音声合成モデル: 話者の声を AI に学習させることで制作された、テキストからその話者に近い合成音声を生成できる、重み (Weight) やパラメータのことを指します。 - あなた: このライセンスによって許可された行為を行う個人・法人のことをいいます。 - このライセンス: このライセンス自身 (Aivis Common Model License: ACML) のことをいいます。 - この音声合成モデル: このライセンスにより利用が許諾される音声合成モデルのことをいいます。 - 利用: この音声合成モデルの実行、複製、改変、配布、組み込み、その他あらゆる利用形態を含みます。 - 派生物: この音声合成モデルを基に作成された二次的著作物、改変物、または派生物を指します。 ## できないこと（禁止事項） - ❌ 音声合成モデルの元となった話者や無関係な他者の「本人」「原作者」「公式関係者」であるとの誤解を招く/騙すような利用 - 生成した音声を、ディープフェイクや公式なコンテンツだと誤解されるような形で公開しないでください。 - 「※非公式です」「本人とは一切関係ありません」と明記するなど、なるべく公式なものだと誤解されないよう注意を払ってください。 - ❌ 話者のイメージ・尊厳・品位・社会的評価を「傷つける」「価値を下げる」「貶める」ような利用 - 第三者が『この声がこんな用途に使われるのは嫌だ』と感じるような使い方をしないでください。 - 話者がキャラクターのときは、そのキャラクターが登場する作品に対しても同様に適用されます。 - 具体的には、下記の禁止事項が該当します。 - ❌ 実在する人物・団体・商品などを「批判」「攻撃」「嫌がらせ」「誹謗中傷」「差別」する活動への利用 - 「攻撃」とは、自他を傷つけるあらゆる行為（犯罪、戦争、暴行、自傷・自殺、薬物乱用、ヘイトスピーチ、誹謗中傷、侮辱、揶揄、アンチ活動、詐欺、クラッキング、その他あらゆる身体的・精神的・社会的加害を含むがこれらに限定されない）を、実行または扇動することをいいます。 - あなたの主張が正当であるかどうかにかかわらず、批判や攻撃を目的とした活動にはお使いいただけません。 - ❌ 人々を騙す目的で虚偽の情報やコンテンツを公開・流布する活動への利用 - フェイクニュースの作成や誤情報を広める目的ではお使いいただけません。 - ❌ 虚偽または誇大な表現によるマーケティングや倫理的に問題のあるビジネスへの利用 - 消費者の誤解を狙った悪質なマーケティング活動・倫理的に問題のある事業活動（虚偽広告、誇大広告、高額情報商材、マルチ商法、ねずみ講を含むがこれらに限定されない）やその宣伝にはお使いいただけません。 - ❌ 特定の政治的立場・政治団体・政治家・宗教団体・宗教家・排他的思想・社会的勢力・陰謀論への賛同・支援または反対・批判・非難を呼びかける活動（権利運動・署名運動・デモ・プロパガンダを含むがこれらに限定されない）への利用 - 「陰謀論」とは、科学的根拠や事実に基づかない主張（ワクチン、通信技術、医療、健康、社会問題などに関する誤った情報を含むがこれらに限定されない）のことをいいます。 - あなたの主張が正当であるかどうかにかかわらず、特定の政治・宗教・排他的思想・社会的勢力・陰謀論に関する主張を広める活動にはお使いいただけません。 - ❌ 反社会的・犯罪目的での利用 - 話者へのなりすましによるオレオレ詐欺や、反社会的勢力による利用などを含みます。 - ❌ 営利目的での利用 - 以下のような、営利を目的としない使い方でのみお使いいただけます。 - 個人による私的な創作活動 - 学校や大学など教育機関における教育・研究目的での利用 - その他、営利を目的としない活動での利用 ## できること - ✅ 上記「できないこと（禁止事項）」に該当しない、すべての非営利利用 - 禁止事項に該当しない使い方であれば、どのような用途にも自由にお使いいただけます。 - その際、下記の「なるべく守ってほしいこと」を尊重した利用をお願いします。 - クレジット表記は任意です。音声合成モデルの制作者や話者のクレジット表記を行うかどうかは、あなたの判断にお任せします。 - この音声合成モデルを他の人に配布・共有する場合は、必ずこのライセンス文書も一緒に添付してください。 - ℹ️ この音声合成モデルを組み込んだアプリ・Web サービスを不特定多数に公開する場合: - 不特定多数のユーザー or AI (LLM) が任意のテキストを入力して音声合成できる状況において、このライセンスをユーザーや LLM に完璧に遵守させることは、技術的・現実的に極めて困難であると考えられます。 - このため特例として、（技術的に禁止事項に該当する利用を防げる状態かに関わらず）「アプリ・Web サービスの開発元自身がこのライセンスを遵守し、現実的な範囲でなるべく禁止事項に該当する利用が起きないよう努めていれば」お使いいただけます。 ## なるべく守ってほしいこと - ℹ️ 話者をリスペクト・尊重した利用をしてほしい - 具体的にどのような使い方が「リスペクト」となるかは、あなたの良識にお任せします。 - 話者がキャラクターのときは、そのキャラクターが登場する作品も十分にリスペクトしてください。 - キャラクター愛・作品愛のある使い方をお願いします。 - ℹ️ 刺激の強い/万人向けでない表現を公開するときは、「見たくない人・見るべきでない人の目に入らない」よう十分配慮してほしい - 表現に合わせた適切なゾーニングをお願いします。 - ゾーニングの例:「年齢制限を掛ける」「SNS のセンシティブ設定をオンにする」「コンテンツの前に注意書きを設置する」 - ℹ️ 常識の範囲内で、良識ある利用をしてほしい - 上記はあくまで任意であり、遵守されていなくてもライセンス違反にはなりません。 ## 免責事項 - この音声合成モデルは「現状のまま」提供されており、商品性や特定の目的への適合性、権利の非侵害などについて、明示的または黙示的を問わず、いかなる保証もありません。 - 音声合成モデルの制作者は、この音声合成モデルの利用や取り扱いまたはその結果に関連して、契約や不法行為など、いかなる形の請求や損害賠償、その他の責任についても、一切の責任を負いません。あなた自身の責任においてお使いください。 - このライセンスのいずれかの条項が無効または執行不能と判断された場合でも、残りの条項は完全に有効に存続するものとします。 - このライセンスのいずれかの条項の権利行使を行わなかったとしても、それは当該条項または他の条項の権利放棄とはみなされません。 - このライセンスはすべて日本語で提供され、日本語のみにより解釈されるものとします。上記の免責事項は、適用される法令の下で許容される最大限の範囲で適用されるものとします。 </details> --- ## インストール方法インストール方法についてのサポートは行っておりません。<br> Aivisspeech版を推奨しています。 ### Aivisspeech ```Aivisspeech 1.1.0-preview.2```にて動作確認しています。手動でダウンロードしてAivisspeechにインストール頂くか、以下URLを「設定」「音声合成モデルの管理」「URLからインストール」に入力してください。<br> ```https://huggingface.co/Yukiyoke-Lab/TAR-model/resolve/main/TAR-v1.0.2.aivmx?download=true``` ### SBV2 必要ファイルをダウンロード頂き、指定ディレクトリへと配置してください。 --- ## ライセンス改定ライセンスは記載が無い限り改定の翌日から効力を持ちます。<br> 日本時間 2025年8月17日第2号<br> 日本時間 2025年8月17日第3号<br> 日本時間 2025年8月17日第4号<br> 日本時間 2025年8月17日第5号<br>
crystalline7/1622588	crystalline7	2025-08-18T22:32:50Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:32:43Z	[View on Civ Archive](https://civarchive.com/models/1131118?modelVersionId=1722074)
crystalline7/1560430	crystalline7	2025-08-18T22:32:26Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:32:26Z	[View on Civ Archive](https://civarchive.com/models/1467698?modelVersionId=1659988)
crystalline7/1425654	crystalline7	2025-08-18T22:32:15Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:32:13Z	[View on Civ Archive](https://civarchive.com/models/1350620?modelVersionId=1525575)
seraphimzzzz/1179513	seraphimzzzz	2025-08-18T22:32:08Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:32:05Z	[View on Civ Archive](https://civarchive.com/models/1132879?modelVersionId=1274669)
ultratopaz/294585	ultratopaz	2025-08-18T22:31:56Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:31:53Z	[View on Civ Archive](https://civarchive.com/models/328548?modelVersionId=368183)
fatmhd1995/phi35_ft_llm_4_annotation_rnd3_without_hpt	fatmhd1995	2025-08-18T22:31:51Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T22:28:58Z	--- base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: fatmhd1995 - License: apache-2.0 - Finetuned from model : unsloth/phi-3.5-mini-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
aliangdw/rfm_v3.1	aliangdw	2025-08-18T22:31:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "reward-model", "rfm", "vision-language", "multimodal", "base_model:Qwen/Qwen2.5-VL-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-08-18T22:24:28Z	--- license: apache-2.0 base_model: Qwen/Qwen2.5-VL-3B-Instruct tags: - reward-model - rfm - vision-language - multimodal library_name: transformers --- # aliangdw/rfm_v3.1 This is a Reward Function Model (RFM) for vision-language preference learning and similarity assessment. ## Model Details - Base Model: Qwen/Qwen2.5-VL-3B-Instruct - Model Type: qwen2_5_vl - Architecture: RFMModel - Task: Vision-Language Reward Modeling - Training Method: FSDP (Fully Sharded Data Parallel) ## Usage ```python from transformers import AutoProcessor, AutoModel import torch # Load model and processor processor = AutoProcessor.from_pretrained("aliangdw/rfm_v3.1", trust_remote_code=True) model = AutoModel.from_pretrained("aliangdw/rfm_v3.1", trust_remote_code=True) # Example usage for preference scoring # inputs = processor(images=images, text=text, return_tensors="pt") # outputs = model(inputs, sample_type="preference") ``` ## Model Capabilities This RFM model can perform: 1. Preference Prediction: Given two trajectories A and B, predict which one is preferred 2. Similarity Assessment: Evaluate how similar a trajectory is to a reference 3. Progress Estimation**: Estimate task completion progress ## Training The model was trained using: - FSDP for distributed training - Mixed precision (bfloat16) - Custom loss functions for preference and similarity learning ## Files This repository contains: - Model weights in SafeTensors format - Configuration files - Tokenizer/Processor files ## Citation If you use this model, please cite:
ultratopaz/1348382	ultratopaz	2025-08-18T22:31:35Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:31:33Z	[View on Civ Archive](https://civarchive.com/models/1282728?modelVersionId=1447202)
seraphimzzzz/1497204	seraphimzzzz	2025-08-18T22:31:28Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:31:25Z	[View on Civ Archive](https://civarchive.com/models/1412810?modelVersionId=1597175)
ultratopaz/1245990	ultratopaz	2025-08-18T22:31:12Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:31:10Z	[View on Civ Archive](https://civarchive.com/models/1192275?modelVersionId=1342384)
seraphimzzzz/1474526	seraphimzzzz	2025-08-18T22:31:05Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:31:02Z	[View on Civ Archive](https://civarchive.com/models/1393239?modelVersionId=1574735)
seraphimzzzz/1209032	seraphimzzzz	2025-08-18T22:30:35Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:30:33Z	[View on Civ Archive](https://civarchive.com/models/1159581?modelVersionId=1304854)
AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755556199	AminuPeril	2025-08-18T22:30:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:30:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
seraphimzzzz/1435841	seraphimzzzz	2025-08-18T22:30:29Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:30:26Z	[View on Civ Archive](https://civarchive.com/models/1359635?modelVersionId=1535908)
seraphimzzzz/1332514	seraphimzzzz	2025-08-18T22:30:22Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:30:20Z	[View on Civ Archive](https://civarchive.com/models/1268989?modelVersionId=1431376)
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755556140	IvanJAjebu	2025-08-18T22:30:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:30:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kimono998/Wordle-curr-neg-3_lora_adapter_iter_15	kimono998	2025-08-18T22:30:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-18T22:30:11Z	--- 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]
Guilherme34/Maya-lora	Guilherme34	2025-08-18T22:30:02Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:unsloth/orpheus-3b-0.1-ft", "lora", "transformers", "unsloth", "text-generation", "arxiv:1910.09700", "base_model:unsloth/orpheus-3b-0.1-ft", "region:us" ]	text-generation	2025-08-18T22:29:25Z	--- base_model: unsloth/orpheus-3b-0.1-ft library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/orpheus-3b-0.1-ft - lora - transformers - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0
seraphimzzzz/1232441	seraphimzzzz	2025-08-18T22:29:42Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:29:39Z	[View on Civ Archive](https://civarchive.com/models/1180572?modelVersionId=1328557)
seraphimzzzz/1391154	seraphimzzzz	2025-08-18T22:29:35Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:29:32Z	[View on Civ Archive](https://civarchive.com/models/1320686?modelVersionId=1491055)
crystalline7/128713	crystalline7	2025-08-18T22:29:29Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:29:27Z	[View on Civ Archive](https://civarchive.com/models/151523?modelVersionId=169426)
crystalline7/1465942	crystalline7	2025-08-18T22:29:23Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:29:20Z	[View on Civ Archive](https://civarchive.com/models/1385916?modelVersionId=1566155)
ultratopaz/1488216	ultratopaz	2025-08-18T22:29:11Z	0	0	null	[ "region:us" ]	null	2025-08-18T22:29:03Z	[View on Civ Archive](https://civarchive.com/models/1295945?modelVersionId=1588426)
mradermacher/Cydonia-24B-v4.1-GGUF	mradermacher	2025-08-18T22:28:48Z	0	1	transformers	[ "transformers", "gguf", "en", "base_model:TheDrummer/Cydonia-24B-v4.1", "base_model:quantized:TheDrummer/Cydonia-24B-v4.1", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-18T17:45:22Z	--- base_model: TheDrummer/Cydonia-24B-v4.1 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/TheDrummer/Cydonia-24B-v4.1 <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Cydonia-24B-v4.1-GGUF).* weighted/imatrix quants are available at https://huggingface.co/mradermacher/Cydonia-24B-v4.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q2_K.gguf) \| Q2_K \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q3_K_S.gguf) \| Q3_K_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q3_K_M.gguf) \| Q3_K_M \| 11.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q3_K_L.gguf) \| Q3_K_L \| 12.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.IQ4_XS.gguf) \| IQ4_XS \| 13.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q4_K_S.gguf) \| Q4_K_S \| 13.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q4_K_M.gguf) \| Q4_K_M \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q5_K_S.gguf) \| Q5_K_S \| 16.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q5_K_M.gguf) \| Q5_K_M \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q6_K.gguf) \| Q6_K \| 19.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q8_0.gguf) \| Q8_0 \| 25.2 \| fast, best quality \| 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 -->
lemonhat/Qwen2.5-Coder-7B-Instruct-swe_5k_v1_tag5mini	lemonhat	2025-08-18T22:28:00Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-Coder-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-7B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T22:26:34Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-Coder-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: swe_5k_v1_tag5mini 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. --> # swe_5k_v1_tag5mini This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the swe_5k_v1_tag5mini dataset. It achieves the following results on the evaluation set: - Loss: 0.4714 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 4 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.6295 \| 0.3650 \| 100 \| 0.4949 \| \| 0.6747 \| 0.7299 \| 200 \| 0.4726 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
hohotok/blockassist-bc-dormant_rapid_salmon_1755555414	hohotok	2025-08-18T22:27:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant rapid salmon", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:27:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant rapid salmon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dejiat/blockassist-bc-savage_unseen_bobcat_1755555921	Dejiat	2025-08-18T22:26:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:25:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
JinghuiLuAstronaut/smolvla_spatial	JinghuiLuAstronaut	2025-08-18T22:25:22Z	2	0	lerobot	[ "lerobot", "safetensors", "robotics", "smolvla", "dataset:IPEC-COMMUNITY/libero_spatial_no_noops_1.0.0_lerobot", "arxiv:2506.01844", "base_model:lerobot/smolvla_base", "base_model:finetune:lerobot/smolvla_base", "license:apache-2.0", "region:us" ]	robotics	2025-08-09T23:17:25Z	--- base_model: lerobot/smolvla_base datasets: IPEC-COMMUNITY/libero_spatial_no_noops_1.0.0_lerobot library_name: lerobot license: apache-2.0 model_name: smolvla pipeline_tag: robotics tags: - lerobot - robotics - smolvla --- # Model Card for smolvla <!-- Provide a quick summary of what the model is/does. --> [SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755555793	AminuPeril	2025-08-18T22:23:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:23:39Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF	mradermacher	2025-08-18T22:23:34Z	16	0	transformers	[ "transformers", "gguf", "en", "base_model:hlwei/qwen-2.5-0.5b-r1-Sudoku", "base_model:quantized:hlwei/qwen-2.5-0.5b-r1-Sudoku", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-09T15:24:38Z	--- base_model: hlwei/qwen-2.5-0.5b-r1-Sudoku 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: --> static quants of https://huggingface.co/hlwei/qwen-2.5-0.5b-r1-Sudoku <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#qwen-2.5-0.5b-r1-Sudoku-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/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q3_K_S.gguf) \| Q3_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q2_K.gguf) \| Q2_K \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.IQ4_XS.gguf) \| IQ4_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q3_K_M.gguf) \| Q3_K_M \| 0.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q3_K_L.gguf) \| Q3_K_L \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q4_K_S.gguf) \| Q4_K_S \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q4_K_M.gguf) \| Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q5_K_S.gguf) \| Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q5_K_M.gguf) \| Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q6_K.gguf) \| Q6_K \| 0.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q8_0.gguf) \| Q8_0 \| 0.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.f16.gguf) \| f16 \| 1.1 \| 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 -->
efficient-nlp/stt-1b-en_fr-quantized	efficient-nlp	2025-08-18T22:23:05Z	0	0	moshi	[ "moshi", "gguf", "stt", "audio", "automatic-speech-recognition", "en", "fr", "license:cc-by-4.0", "region:us" ]	automatic-speech-recognition	2025-08-18T22:09:11Z	--- license: cc-by-4.0 language: - en - fr library_name: moshi tags: - audio - automatic-speech-recognition --- # Moshi Streaming Speech-to-Text (Quantized) This is a quantized version of Kyutai’s [stt-1b-en_fr](https://huggingface.co/kyutai/stt-1b-en_fr) model. The original model is a 1B parameter streaming speech-to-text model for English and French. This fork contains the same model, quantized to Q8_0 and Q4_K GGUF formats for reduced memory usage and faster inference.
kohya-ss/misc-models	kohya-ss	2025-08-18T22:22:09Z	0	31	null	[ "region:us" ]	null	2023-05-11T23:38:36Z	## qwenimage-blob_emoji-4-s020-6.safetensors Blob emoji LoRA. The training captions are like `Yellow blob emoji with smiling face with smiling eyes. The background is gray.`, so `blob emoji` or `blob emoji with face ...` etc. act as trigger words. - Blob emoji with face holds a sign says "Blob Emoji" in front of Japanese Shrine. --w 1024 --h 1024 --s 50 --d 1001 ![sample1](yellow_blob_1.png) - Blob emoji face drives a red sport car along a curved road on a cliff overlooking the sea. The sea is dotted with whitecaps. The sky is blue, and cumulonimbus clouds float on the horizon. --w 1664 --h 928 --s 50 --d 12345678 ![sample2](yellow_blob_2.png) ### Dataset Creation Procedure The dataset was created following these steps: - The SVG files from [C1710/blobmoji](https://github.com/C1710/blobmoji) (licensed under ASL 2.0) were used. Specifically, 118 different yellow blob emojis were selected from the SVG files. - `cairosvg` was used to convert these SVGs into 512x512 pixel transparent PNGs. - A script was then used to pad the images to 640x640 pixels and generate four versions of each image with different background colors: white, light gray, gray, and black. This resulted in a total of 472 images. - The captions were generated based on the official Unicode names of the emojis. The prefix `Yellow blob emoji with ` and the suffix `. The background is <color>.` were added to each name. - For example: `Yellow blob emoji with smiling face with smiling eyes. The background is gray.` - Note: For some emojis (e.g., devil, zombie), the word `Yellow` was omitted from the prefix. ### Dataset Definition ``` # general configurations [general] resolution = [640, 640] batch_size = 16 enable_bucket = true bucket_no_upscale = false caption_extension = ".txt" [[datasets]] image_directory = "path/to/images_and_captions_dir" cache_directory = "path/to/cache_dir" ``` ### Training Command ``` accelerate launch --num_cpu_threads_per_process 1 --mixed_precision bf16 --rdzv_backend=c10d \ src/musubi_tuner/qwen_image_train_network.py \ --dit path/to/dit.safetensors --vae path/to/vae.safetensors \ --text_encoder path/to/vlm.safetensors \ --dataset_config path/to/blob_emoji_v1_640_bs16.toml \ --output_dir path/to/output_dir \ --learning_rate 2e-4 \ --timestep_sampling shift --weighting_scheme none --discrete_flow_shift 2.0 \ --max_train_epochs 16 --mixed_precision bf16 --seed 42 --gradient_checkpointing \ --network_module=networks.lora_qwen_image \ --network_dim=4 --network_args loraplus_lr_ratio=4 \ --save_every_n_epochs=1 --max_data_loader_n_workers 2 \ --persistent_data_loader_workers \ --logging_dir ./logs --log_prefix qwenimage-blob4-2e4- \ --output_name qwenimage-blob4-2e4 \ --optimizer_type adamw8bit --flash_attn --split_attn \ --log_with tensorboard \ --sample_every_n_epochs 1 --sample_prompts path/to/prompts_qwen_blob_emoji.txt \ --fp8_base --fp8_scaled ``` ### Training Details - Training was conducted on a Windows machine with a multi-GPU setup (2x RTX A6000). - If you are not using a Windows environment or not performing multi-GPU training, please remove the `--rdzv_backend=c10d` argument. - Please note that due to the 2-GPU setup, the effective batch size is 32. To achieve the same results with limited VRAM, increase the gradient accumulation steps. However, you should be able to train successfully with a lower batch size by adjusting the learning rate. - The model was trained for 6 epochs (90 steps), which took approximately 1 hour with the Power Limit set to 60%. - Finally, the weights from all 6 epochs were merged using the LoRA Post-Hoc EMA script from Musubi Tuner with `sigma_rel=0.2`. ## fp-1f-kisekae-1024-v4-2-PfPHEMA.safetensors Post-Hoc EMA (with Power function sigma_rel=0.2) version of the following LoRA. The usage is the same. ## fp-1f-kisekae-1024-v4-2.safetensors Experimental LoRA for FramePack One Frame kisekaeichi. The target index is 5. The prompt is as follows: ``` The girl stays in the same pose, but her outfit changes into a <costume description>, then she changes into another girl wearing the same outfit. ``` `costume description` is something like `school uniform` etc. A detailed description may improve the results. For example: "T-shirt with writing on it" or "Girl with long hair" This model is trained with 1024x1024 resolution. Please use at roughly the same resolution. ## fp-1f-chibi-1024.safetensors Experimental LoRA for FramePack One Frame Inference. The target index is 9. The prompt is as follows: ``` An anime character transforms: her head grows larger, her body becomes shorter and smaller, eyes become bigger and cuter. She turns into a chibi (super-deformed) version, with cartoonishly cute proportions. The transformation is quick and playful. ``` This model is trained with 1024x1024 resolution. Please use at roughly the same resolution. If the effect is too strong, lower the multiplier (strength) to 0.8 or less. ## FramePack-dance-lora-d8.safetensors Experimental LoRA for FramePack. This is for testing purposes and the effect is weak. Please set the prompt to something like `A woman is spinning on her tiptoes` . `. ## flux-hasui-lora-d4-sigmoid-raw-gs1.0.safetensors Experimental LoRA for FLUX.1 dev. Trained with `sd-scripts` (Aug. 11) `sd3` branch. __NOTE:__ This settings requires > 26GB VRAM. Please add `--fp8_base` to enable fp8 training to reduce VRAM usage. ``` accelerate launch --mixed_precision bf16 --num_cpu_threads_per_process 1 flux_train_network.py --pretrained_model_name_or_path flux1/flux1-dev.sft --clip_l sd3/clip_l.safetensors --t5xxl sd3/t5xxl_fp16.safetensors --ae flux1/ae_dev.sft --cache_latents_to_disk --save_model_as safetensors --sdpa --persistent_data_loader_workers --max_data_loader_n_workers 2 --seed 42 --gradient_checkpointing --mixed_precision bf16 --save_precision bf16 --network_module networks.lora_flux --network_dim 4 --optimizer_type adamw8bit --learning_rate 1e-3 --network_train_unet_only --cache_text_encoder_outputs --cache_text_encoder_outputs_to_disk --highvram --max_train_epochs 4 --save_every_n_epochs 1 --dataset_config hasui_1024_bs1.toml --output_dir flux/lora --output_name lora-name --timestep_sampling sigmoid --model_prediction_type raw --guidance_scale 1.0 ``` .toml is below. ```.toml [general] flip_aug = true color_aug = false [[datasets]] enable_bucket = true resolution = [1024,1024] bucket_reso_steps = 64 max_bucket_reso = 2048 min_bucket_reso = 128 bucket_no_upscale = false batch_size = 1 random_crop = false shuffle_caption = false [[datasets.subsets]] image_dir = "path/to/train/images" num_repeats = 1 caption_extension = ".txt" ``` ## sdxl-negprompt8-v1m.safetensors Negative embeddings for sdxl. Num vectors per token = 8 ## stable-cascade-c-lora-hasui-v02.safetensors Sample of LoRA for Stable Cascade Stage C. Feb 22, 2024 Update: Fixed a bug that LoRA is not applied to some modules (to_q/k/v and to_out) in Attention. __This is an experimental model, so the format of the weights may change in the future.__ - a painting of an anthropomorphic penguin sitting in a cafe reading a book and having a coffee --w 1024 --h 1024 --d 1 ![sample1](penguin.png) - a painting of japanese shrine in winter with snowfall --w 832 --h 1152 --d 1234 ![sample2](shrine.png) This model is trained with 169 images with captions. U-Net only, dim=4, conv_dim=4, alpha=1, lr=1e-3, 4 epochs, mixed precision bf16, 8bit AdamW, batch size 8, resolution 1024x1024 with aspect ratio bucketing. VRAM usage is approximately 22 GB.
AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755555588	AminuPeril	2025-08-18T22:20:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:20:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vjkhambe/dqn-SpaceInvadersNoFrameskip-v4	vjkhambe	2025-08-18T22:20:12Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-08-18T21:15:46Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 250.50 +/- 147.45 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga vjkhambe -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga vjkhambe -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga vjkhambe ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 1e-05), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755555192	AminuPeril	2025-08-18T22:13:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:13:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755553635	vwzyrraz7l	2025-08-18T22:13:42Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:13:38Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
haihp02/aa465003-5d10-46d8-a8e0-d20b7380fe9b	haihp02	2025-08-18T22:12:58Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-18T22:12:47Z	--- 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]
NexVeridian/OpenReasoning-Nemotron-14B-6bit	NexVeridian	2025-08-18T22:12:40Z	0	0	mlx	[ "mlx", "safetensors", "qwen2", "nvidia", "code", "text-generation", "conversational", "en", "base_model:nvidia/OpenReasoning-Nemotron-14B", "base_model:quantized:nvidia/OpenReasoning-Nemotron-14B", "license:cc-by-4.0", "6-bit", "region:us" ]	text-generation	2025-08-18T22:06:54Z	--- license: cc-by-4.0 language: - en base_model: nvidia/OpenReasoning-Nemotron-14B pipeline_tag: text-generation library_name: mlx tags: - nvidia - code - mlx --- # NexVeridian/OpenReasoning-Nemotron-14B-6bit This model [NexVeridian/OpenReasoning-Nemotron-14B-6bit](https://huggingface.co/NexVeridian/OpenReasoning-Nemotron-14B-6bit) was converted to MLX format from [nvidia/OpenReasoning-Nemotron-14B](https://huggingface.co/nvidia/OpenReasoning-Nemotron-14B) using mlx-lm version 0.26.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("NexVeridian/OpenReasoning-Nemotron-14B-6bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755554998	IvanJAjebu	2025-08-18T22:11:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:11:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755553607	lisaozill03	2025-08-18T22:11:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:11:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
SharpAI/yolo11n-onnx-cpu-ort	SharpAI	2025-08-18T22:10:49Z	0	0	ultralytics	[ "ultralytics", "onnx", "yolo", "object-detection", "computer-vision", "unknown", "aegis-ai", "license:agpl-3.0", "region:us" ]	object-detection	2025-08-18T21:42:17Z	--- title: YOLO Model tags: - yolo - object-detection - computer-vision - unknown - aegis-ai library_name: ultralytics license: agpl-3.0 --- # YOLO Model This model has been converted and optimized using the Aegis AI Model Conversion Tool. ## Model Details - Original Model: Unknown - Format: UNKNOWN - Task: Object Detection - Framework: Ultralytics YOLO - License: AGPL-3.0 ## Performance Metrics \| Metric \| Value \| \|--------\|--------\| \| Average FPS \| N/A \| \| Inference Time \| N/A ms \| \| Memory Usage \| N/A MB \| \| Target Hardware \| cpu \| ## Hardware Information - Platform: Unknown - Device: cpu - Optimization: Hardware-specific optimizations applied ## Usage ### Loading the Model ```python # For ONNX models import onnxruntime as ort session = ort.InferenceSession("model.onnx") # For PyTorch models from ultralytics import YOLO model = YOLO("model.pt") # For TensorRT models (NVIDIA GPU) # Requires TensorRT runtime model = YOLO("model.engine") ``` ### Inference ```python import numpy as np from PIL import Image # Load your image image = Image.open("path/to/image.jpg") # Run inference results = model(image) # Process results for result in results: boxes = result.boxes # Bounding boxes classes = result.names # Class names ``` ## Conversion Details This model was converted using the Aegis AI Model Conversion Tool with the following configuration: - Precision: fp32 - Optimization Level: standard - Hardware Target: cpu - Conversion Date: 2025-08-18 15:10:46 ## Model Architecture Based on the YOLO (You Only Look Once) architecture, this model provides real-time object detection capabilities with optimized performance for the target hardware. ### Input - Shape: 640x640 - Format: RGB images - Normalization: [0-1] range ### Output - Bounding Boxes: Object locations - Confidence Scores: Detection confidence - Class Predictions: Object categories ## Benchmarking The model has been benchmarked on the target hardware with the following results: ```json {} ``` ## Hardware Compatibility This model has been optimized for: - Primary: cpu - Platform: Unknown For other hardware configurations, consider using the Aegis AI Model Conversion Tool to create optimized versions. ## Citation If you use this model in your research or project, please cite: ```bibtex @misc{aegis-ai-converted-model, title={Aegis AI Converted YOLO Model}, author={Aegis AI Team}, year={2025}, howpublished={\url{https://github.com/aegis-ai/model-conversion-tool}} } ``` ## Related Models - [Original YOLO Models](https://github.com/ultralytics/ultralytics) - [Aegis AI Model Zoo](https://huggingface.co/aegis-ai) ## Support For issues with this converted model or the conversion tool: - [GitHub Issues](https://github.com/aegis-ai/model-conversion-tool/issues) - [Aegis AI Documentation](https://docs.aegis-ai.com) --- This model was automatically converted and uploaded by the Aegis AI Model Conversion Tool.
donoway/ARC-Challenge_Llama-3.2-1B-zfgomj43	donoway	2025-08-18T22:09:49Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T21:58:26Z	--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: ARC-Challenge_Llama-3.2-1B-zfgomj43 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. --> # ARC-Challenge_Llama-3.2-1B-zfgomj43 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5531 - Model Preparation Time: 0.0057 - Mdl: 669.9700 - Accumulated Loss: 464.3878 - Correct Preds: 132.0 - Total Preds: 299.0 - Accuracy: 0.4415 - Correct Gen Preds: 114.0 - Gen Accuracy: 0.3813 - Correct Gen Preds 32: 12.0 - Correct Preds 32: 20.0 - Total Labels 32: 64.0 - Accuracy 32: 0.3125 - Gen Accuracy 32: 0.1875 - Correct Gen Preds 33: 23.0 - Correct Preds 33: 24.0 - Total Labels 33: 73.0 - Accuracy 33: 0.3288 - Gen Accuracy 33: 0.3151 - Correct Gen Preds 34: 42.0 - Correct Preds 34: 47.0 - Total Labels 34: 78.0 - Accuracy 34: 0.6026 - Gen Accuracy 34: 0.5385 - Correct Gen Preds 35: 37.0 - Correct Preds 35: 41.0 - Total Labels 35: 83.0 - Accuracy 35: 0.4940 - Gen Accuracy 35: 0.4458 - Correct Gen Preds 36: 0.0 - Correct Preds 36: 0.0 - Total Labels 36: 1.0 - Accuracy 36: 0.0 - Gen Accuracy 36: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 112 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Mdl \| Accumulated Loss \| Correct Preds \| Total Preds \| Accuracy \| Correct Gen Preds \| Gen Accuracy \| Correct Gen Preds 32 \| Correct Preds 32 \| Total Labels 32 \| Accuracy 32 \| Gen Accuracy 32 \| Correct Gen Preds 33 \| Correct Preds 33 \| Total Labels 33 \| Accuracy 33 \| Gen Accuracy 33 \| Correct Gen Preds 34 \| Correct Preds 34 \| Total Labels 34 \| Accuracy 34 \| Gen Accuracy 34 \| Correct Gen Preds 35 \| Correct Preds 35 \| Total Labels 35 \| Accuracy 35 \| Gen Accuracy 35 \| Correct Gen Preds 36 \| Correct Preds 36 \| Total Labels 36 \| Accuracy 36 \| Gen Accuracy 36 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------------:\|:---------:\|:----------------:\|:-------------:\|:-----------:\|:--------:\|:-----------------:\|:------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------------:\|:----------------:\|:---------------:\|:-----------:\|:---------------:\| \| No log \| 0 \| 0 \| 1.6389 \| 0.0057 \| 706.9523 \| 490.0220 \| 66.0 \| 299.0 \| 0.2207 \| 66.0 \| 0.2207 \| 62.0 \| 62.0 \| 64.0 \| 0.9688 \| 0.9688 \| 0.0 \| 0.0 \| 73.0 \| 0.0 \| 0.0 \| 4.0 \| 4.0 \| 78.0 \| 0.0513 \| 0.0513 \| 0.0 \| 0.0 \| 83.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 1.4865 \| 1.0 \| 6 \| 1.4608 \| 0.0057 \| 630.1492 \| 436.7861 \| 79.0 \| 299.0 \| 0.2642 \| 77.0 \| 0.2575 \| 56.0 \| 57.0 \| 64.0 \| 0.8906 \| 0.875 \| 11.0 \| 12.0 \| 73.0 \| 0.1644 \| 0.1507 \| 1.0 \| 1.0 \| 78.0 \| 0.0128 \| 0.0128 \| 9.0 \| 9.0 \| 83.0 \| 0.1084 \| 0.1084 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 1.0972 \| 2.0 \| 12 \| 1.4324 \| 0.0057 \| 617.8780 \| 428.2804 \| 120.0 \| 299.0 \| 0.4013 \| 119.0 \| 0.3980 \| 36.0 \| 36.0 \| 64.0 \| 0.5625 \| 0.5625 \| 24.0 \| 24.0 \| 73.0 \| 0.3288 \| 0.3288 \| 25.0 \| 25.0 \| 78.0 \| 0.3205 \| 0.3205 \| 34.0 \| 35.0 \| 83.0 \| 0.4217 \| 0.4096 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.4836 \| 3.0 \| 18 \| 1.5531 \| 0.0057 \| 669.9700 \| 464.3878 \| 132.0 \| 299.0 \| 0.4415 \| 114.0 \| 0.3813 \| 12.0 \| 20.0 \| 64.0 \| 0.3125 \| 0.1875 \| 23.0 \| 24.0 \| 73.0 \| 0.3288 \| 0.3151 \| 42.0 \| 47.0 \| 78.0 \| 0.6026 \| 0.5385 \| 37.0 \| 41.0 \| 83.0 \| 0.4940 \| 0.4458 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.1277 \| 4.0 \| 24 \| 2.8384 \| 0.0057 \| 1224.4029 \| 848.6914 \| 125.0 \| 299.0 \| 0.4181 \| 116.0 \| 0.3880 \| 18.0 \| 23.0 \| 64.0 \| 0.3594 \| 0.2812 \| 28.0 \| 30.0 \| 73.0 \| 0.4110 \| 0.3836 \| 35.0 \| 35.0 \| 78.0 \| 0.4487 \| 0.4487 \| 35.0 \| 37.0 \| 83.0 \| 0.4458 \| 0.4217 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0009 \| 5.0 \| 30 \| 4.5527 \| 0.0057 \| 1963.8919 \| 1361.2661 \| 121.0 \| 299.0 \| 0.4047 \| 112.0 \| 0.3746 \| 19.0 \| 24.0 \| 64.0 \| 0.375 \| 0.2969 \| 32.0 \| 34.0 \| 73.0 \| 0.4658 \| 0.4384 \| 35.0 \| 35.0 \| 78.0 \| 0.4487 \| 0.4487 \| 26.0 \| 28.0 \| 83.0 \| 0.3373 \| 0.3133 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0005 \| 6.0 \| 36 \| 7.0651 \| 0.0057 \| 3047.6390 \| 2112.4624 \| 120.0 \| 299.0 \| 0.4013 \| 115.0 \| 0.3846 \| 25.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.3906 \| 33.0 \| 33.0 \| 73.0 \| 0.4521 \| 0.4521 \| 32.0 \| 32.0 \| 78.0 \| 0.4103 \| 0.4103 \| 25.0 \| 26.0 \| 83.0 \| 0.3133 \| 0.3012 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 7.0 \| 42 \| 7.6423 \| 0.0057 \| 3296.6319 \| 2285.0511 \| 127.0 \| 299.0 \| 0.4247 \| 118.0 \| 0.3946 \| 26.0 \| 29.0 \| 64.0 \| 0.4531 \| 0.4062 \| 34.0 \| 34.0 \| 73.0 \| 0.4658 \| 0.4658 \| 28.0 \| 31.0 \| 78.0 \| 0.3974 \| 0.3590 \| 30.0 \| 33.0 \| 83.0 \| 0.3976 \| 0.3614 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 8.0 \| 48 \| 7.8574 \| 0.0057 \| 3389.4254 \| 2349.3707 \| 127.0 \| 299.0 \| 0.4247 \| 117.0 \| 0.3913 \| 19.0 \| 23.0 \| 64.0 \| 0.3594 \| 0.2969 \| 34.0 \| 36.0 \| 73.0 \| 0.4932 \| 0.4658 \| 30.0 \| 31.0 \| 78.0 \| 0.3974 \| 0.3846 \| 34.0 \| 36.0 \| 83.0 \| 0.4337 \| 0.4096 \| 0.0 \| 1.0 \| 1.0 \| 1.0 \| 0.0 \| \| 0.0 \| 9.0 \| 54 \| 7.5145 \| 0.0057 \| 3241.4943 \| 2246.8326 \| 126.0 \| 299.0 \| 0.4214 \| 111.0 \| 0.3712 \| 18.0 \| 25.0 \| 64.0 \| 0.3906 \| 0.2812 \| 36.0 \| 38.0 \| 73.0 \| 0.5205 \| 0.4932 \| 28.0 \| 30.0 \| 78.0 \| 0.3846 \| 0.3590 \| 29.0 \| 32.0 \| 83.0 \| 0.3855 \| 0.3494 \| 0.0 \| 1.0 \| 1.0 \| 1.0 \| 0.0 \| \| 0.0 \| 10.0 \| 60 \| 7.0254 \| 0.0057 \| 3030.5166 \| 2100.5941 \| 127.0 \| 299.0 \| 0.4247 \| 98.0 \| 0.3278 \| 14.0 \| 30.0 \| 64.0 \| 0.4688 \| 0.2188 \| 32.0 \| 37.0 \| 73.0 \| 0.5068 \| 0.4384 \| 28.0 \| 33.0 \| 78.0 \| 0.4231 \| 0.3590 \| 24.0 \| 27.0 \| 83.0 \| 0.3253 \| 0.2892 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 11.0 \| 66 \| 7.2467 \| 0.0057 \| 3125.9883 \| 2166.7700 \| 127.0 \| 299.0 \| 0.4247 \| 113.0 \| 0.3779 \| 17.0 \| 26.0 \| 64.0 \| 0.4062 \| 0.2656 \| 37.0 \| 39.0 \| 73.0 \| 0.5342 \| 0.5068 \| 34.0 \| 36.0 \| 78.0 \| 0.4615 \| 0.4359 \| 25.0 \| 26.0 \| 83.0 \| 0.3133 \| 0.3012 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 12.0 \| 72 \| 7.4089 \| 0.0057 \| 3195.9474 \| 2215.2619 \| 123.0 \| 299.0 \| 0.4114 \| 116.0 \| 0.3880 \| 20.0 \| 24.0 \| 64.0 \| 0.375 \| 0.3125 \| 35.0 \| 37.0 \| 73.0 \| 0.5068 \| 0.4795 \| 33.0 \| 34.0 \| 78.0 \| 0.4359 \| 0.4231 \| 28.0 \| 28.0 \| 83.0 \| 0.3373 \| 0.3373 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 13.0 \| 78 \| 7.2529 \| 0.0057 \| 3128.6606 \| 2168.6223 \| 126.0 \| 299.0 \| 0.4214 \| 121.0 \| 0.4047 \| 21.0 \| 24.0 \| 64.0 \| 0.375 \| 0.3281 \| 37.0 \| 38.0 \| 73.0 \| 0.5205 \| 0.5068 \| 37.0 \| 38.0 \| 78.0 \| 0.4872 \| 0.4744 \| 26.0 \| 26.0 \| 83.0 \| 0.3133 \| 0.3133 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 14.0 \| 84 \| 7.2595 \| 0.0057 \| 3131.5101 \| 2170.5974 \| 127.0 \| 299.0 \| 0.4247 \| 120.0 \| 0.4013 \| 21.0 \| 24.0 \| 64.0 \| 0.375 \| 0.3281 \| 38.0 \| 40.0 \| 73.0 \| 0.5479 \| 0.5205 \| 35.0 \| 37.0 \| 78.0 \| 0.4744 \| 0.4487 \| 26.0 \| 26.0 \| 83.0 \| 0.3133 \| 0.3133 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 15.0 \| 90 \| 7.2704 \| 0.0057 \| 3136.2190 \| 2173.8614 \| 125.0 \| 299.0 \| 0.4181 \| 118.0 \| 0.3946 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 37.0 \| 39.0 \| 73.0 \| 0.5342 \| 0.5068 \| 35.0 \| 37.0 \| 78.0 \| 0.4744 \| 0.4487 \| 27.0 \| 27.0 \| 83.0 \| 0.3253 \| 0.3253 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 16.0 \| 96 \| 7.2516 \| 0.0057 \| 3128.0812 \| 2168.2206 \| 124.0 \| 299.0 \| 0.4147 \| 118.0 \| 0.3946 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 37.0 \| 38.0 \| 73.0 \| 0.5205 \| 0.5068 \| 35.0 \| 36.0 \| 78.0 \| 0.4615 \| 0.4487 \| 27.0 \| 28.0 \| 83.0 \| 0.3373 \| 0.3253 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 17.0 \| 102 \| 7.2158 \| 0.0057 \| 3112.6486 \| 2157.5236 \| 123.0 \| 299.0 \| 0.4114 \| 118.0 \| 0.3946 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 37.0 \| 37.0 \| 73.0 \| 0.5068 \| 0.5068 \| 35.0 \| 37.0 \| 78.0 \| 0.4744 \| 0.4487 \| 27.0 \| 27.0 \| 83.0 \| 0.3253 \| 0.3253 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 18.0 \| 108 \| 7.2719 \| 0.0057 \| 3136.8490 \| 2174.2980 \| 124.0 \| 299.0 \| 0.4147 \| 117.0 \| 0.3913 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 36.0 \| 37.0 \| 73.0 \| 0.5068 \| 0.4932 \| 35.0 \| 37.0 \| 78.0 \| 0.4744 \| 0.4487 \| 27.0 \| 28.0 \| 83.0 \| 0.3373 \| 0.3253 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 19.0 \| 114 \| 7.3003 \| 0.0057 \| 3149.1089 \| 2182.7960 \| 123.0 \| 299.0 \| 0.4114 \| 118.0 \| 0.3946 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 37.0 \| 37.0 \| 73.0 \| 0.5068 \| 0.5068 \| 35.0 \| 37.0 \| 78.0 \| 0.4744 \| 0.4487 \| 27.0 \| 27.0 \| 83.0 \| 0.3253 \| 0.3253 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 20.0 \| 120 \| 7.2762 \| 0.0057 \| 3138.7201 \| 2175.5950 \| 125.0 \| 299.0 \| 0.4181 \| 118.0 \| 0.3946 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 37.0 \| 38.0 \| 73.0 \| 0.5205 \| 0.5068 \| 35.0 \| 37.0 \| 78.0 \| 0.4744 \| 0.4487 \| 27.0 \| 28.0 \| 83.0 \| 0.3373 \| 0.3253 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 21.0 \| 126 \| 7.2258 \| 0.0057 \| 3116.9751 \| 2160.5225 \| 123.0 \| 299.0 \| 0.4114 \| 119.0 \| 0.3980 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 37.0 \| 38.0 \| 73.0 \| 0.5205 \| 0.5068 \| 35.0 \| 35.0 \| 78.0 \| 0.4487 \| 0.4487 \| 28.0 \| 28.0 \| 83.0 \| 0.3373 \| 0.3373 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 22.0 \| 132 \| 7.2761 \| 0.0057 \| 3138.6438 \| 2175.5421 \| 123.0 \| 299.0 \| 0.4114 \| 118.0 \| 0.3946 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 37.0 \| 38.0 \| 73.0 \| 0.5205 \| 0.5068 \| 35.0 \| 36.0 \| 78.0 \| 0.4615 \| 0.4487 \| 27.0 \| 27.0 \| 83.0 \| 0.3253 \| 0.3253 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 23.0 \| 138 \| 7.2421 \| 0.0057 \| 3123.9891 \| 2165.3842 \| 125.0 \| 299.0 \| 0.4181 \| 119.0 \| 0.3980 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 38.0 \| 39.0 \| 73.0 \| 0.5342 \| 0.5205 \| 35.0 \| 36.0 \| 78.0 \| 0.4615 \| 0.4487 \| 27.0 \| 28.0 \| 83.0 \| 0.3373 \| 0.3253 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 24.0 \| 144 \| 7.2574 \| 0.0057 \| 3130.5871 \| 2169.9576 \| 125.0 \| 299.0 \| 0.4181 \| 118.0 \| 0.3946 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 36.0 \| 38.0 \| 73.0 \| 0.5205 \| 0.4932 \| 35.0 \| 37.0 \| 78.0 \| 0.4744 \| 0.4487 \| 28.0 \| 28.0 \| 83.0 \| 0.3373 \| 0.3373 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 25.0 \| 150 \| 7.2338 \| 0.0057 \| 3120.4105 \| 2162.9037 \| 123.0 \| 299.0 \| 0.4114 \| 117.0 \| 0.3913 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 36.0 \| 38.0 \| 73.0 \| 0.5205 \| 0.4932 \| 34.0 \| 35.0 \| 78.0 \| 0.4487 \| 0.4359 \| 28.0 \| 28.0 \| 83.0 \| 0.3373 \| 0.3373 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 26.0 \| 156 \| 7.2551 \| 0.0057 \| 3129.6043 \| 2169.2764 \| 123.0 \| 299.0 \| 0.4114 \| 118.0 \| 0.3946 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 37.0 \| 38.0 \| 73.0 \| 0.5205 \| 0.5068 \| 35.0 \| 36.0 \| 78.0 \| 0.4615 \| 0.4487 \| 27.0 \| 27.0 \| 83.0 \| 0.3253 \| 0.3253 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 27.0 \| 162 \| 7.2376 \| 0.0057 \| 3122.0548 \| 2164.0435 \| 125.0 \| 299.0 \| 0.4181 \| 117.0 \| 0.3913 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 36.0 \| 38.0 \| 73.0 \| 0.5205 \| 0.4932 \| 35.0 \| 37.0 \| 78.0 \| 0.4744 \| 0.4487 \| 27.0 \| 28.0 \| 83.0 \| 0.3373 \| 0.3253 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 28.0 \| 168 \| 7.2457 \| 0.0057 \| 3125.5609 \| 2166.4737 \| 125.0 \| 299.0 \| 0.4181 \| 118.0 \| 0.3946 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 36.0 \| 38.0 \| 73.0 \| 0.5205 \| 0.4932 \| 35.0 \| 37.0 \| 78.0 \| 0.4744 \| 0.4487 \| 28.0 \| 28.0 \| 83.0 \| 0.3373 \| 0.3373 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 29.0 \| 174 \| 7.2451 \| 0.0057 \| 3125.2939 \| 2166.2886 \| 125.0 \| 299.0 \| 0.4181 \| 119.0 \| 0.3980 \| 20.0 \| 24.0 \| 64.0 \| 0.375 \| 0.3125 \| 37.0 \| 37.0 \| 73.0 \| 0.5068 \| 0.5068 \| 35.0 \| 37.0 \| 78.0 \| 0.4744 \| 0.4487 \| 27.0 \| 27.0 \| 83.0 \| 0.3253 \| 0.3253 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 30.0 \| 180 \| 7.2520 \| 0.0057 \| 3128.2546 \| 2168.3409 \| 124.0 \| 299.0 \| 0.4147 \| 118.0 \| 0.3946 \| 20.0 \| 23.0 \| 64.0 \| 0.3594 \| 0.3125 \| 36.0 \| 37.0 \| 73.0 \| 0.5068 \| 0.4932 \| 35.0 \| 36.0 \| 78.0 \| 0.4615 \| 0.4487 \| 27.0 \| 28.0 \| 83.0 \| 0.3373 \| 0.3253 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 31.0 \| 186 \| 7.2720 \| 0.0057 \| 3136.8961 \| 2174.3307 \| 124.0 \| 299.0 \| 0.4147 \| 117.0 \| 0.3913 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 37.0 \| 38.0 \| 73.0 \| 0.5205 \| 0.5068 \| 34.0 \| 36.0 \| 78.0 \| 0.4615 \| 0.4359 \| 27.0 \| 28.0 \| 83.0 \| 0.3373 \| 0.3253 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 32.0 \| 192 \| 7.2555 \| 0.0057 \| 3129.7943 \| 2169.4081 \| 123.0 \| 299.0 \| 0.4114 \| 117.0 \| 0.3913 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 36.0 \| 37.0 \| 73.0 \| 0.5068 \| 0.4932 \| 35.0 \| 36.0 \| 78.0 \| 0.4615 \| 0.4487 \| 27.0 \| 28.0 \| 83.0 \| 0.3373 \| 0.3253 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| \| 0.0 \| 33.0 \| 198 \| 7.2496 \| 0.0057 \| 3127.2402 \| 2167.6377 \| 124.0 \| 299.0 \| 0.4147 \| 119.0 \| 0.3980 \| 19.0 \| 22.0 \| 64.0 \| 0.3438 \| 0.2969 \| 37.0 \| 38.0 \| 73.0 \| 0.5205 \| 0.5068 \| 35.0 \| 36.0 \| 78.0 \| 0.4615 \| 0.4487 \| 28.0 \| 28.0 \| 83.0 \| 0.3373 \| 0.3373 \| 0.0 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
lmsys/gpt-oss-20b-bf16	lmsys	2025-08-18T22:09:15Z	15,646	5	null	[ "safetensors", "gpt_oss", "region:us" ]	null	2025-08-05T21:58:13Z	# gpt-oss-20b-bf16 ## Model Introduction This model is the bf16 version converted from [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b). ## Usage You can use this model in [SGLang](https://github.com/sgl-project/sglang) with the following instructions. ### Installation ``` # build from source git clone https://github.com/sgl-project/sglang cd sglang pip3 install pip --upgrade pip3 install -e "python[all]" # ROCm 6.3 pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/rocm6.3 git clone https://github.com/triton-lang/triton cd python/triton_kernels pip3 install . # hopper pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu126 pip3 install sgl-kernel==0.3.2 # blackwell cu128 pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu128 pip3 install https://github.com/sgl-project/whl/releases/download/v0.3.2/sgl_kernel-0.3.2+cu128-cp39-abi3-manylinux2014_x86_64.whl # blackwell cu129 pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu129 pip3 install https://github.com/sgl-project/whl/releases/download/v0.3.2/sgl_kernel-0.3.2-cp39-abi3-manylinux2014_x86_64.whl ``` ### Launch command ``` python3 -m sglang.launch_server --model lmsys/gpt-oss-20b-bf16 ``` ### For more details https://github.com/sgl-project/sglang/issues/8833
g-assismoraes/Qwen3-4B-Base-fpi-alpha1.6-var-hatebr-ep30-g5	g-assismoraes	2025-08-18T22:08:06Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T22:04:06Z	--- 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]
lmsys/gpt-oss-120b-bf16	lmsys	2025-08-18T22:07:44Z	10,337	2	null	[ "safetensors", "gpt_oss", "region:us" ]	null	2025-08-05T18:54:32Z	# gpt-oss-120b-bf16 ## Model Introduction This model is the bf16 version converted from [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b). ## Usage You can use this model in [SGLang](https://github.com/sgl-project/sglang) with the following instructions. ### Installation ``` # build from source git clone https://github.com/sgl-project/sglang cd sglang pip3 install pip --upgrade pip3 install -e "python[all]" # ROCm 6.3 pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/rocm6.3 git clone https://github.com/triton-lang/triton cd python/triton_kernels pip3 install . # hopper pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu126 pip3 install sgl-kernel==0.3.2 # blackwell cu128 pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu128 pip3 install https://github.com/sgl-project/whl/releases/download/v0.3.2/sgl_kernel-0.3.2+cu128-cp39-abi3-manylinux2014_x86_64.whl # blackwell cu129 pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu129 pip3 install https://github.com/sgl-project/whl/releases/download/v0.3.2/sgl_kernel-0.3.2-cp39-abi3-manylinux2014_x86_64.whl ``` ### Launch command ``` python3 -m sglang.launch_server --model lmsys/gpt-oss-120b-bf16 --tp 4 ``` ### For more details https://github.com/sgl-project/sglang/issues/8833
koloni/blockassist-bc-deadly_graceful_stingray_1755553249	koloni	2025-08-18T22:07:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:07:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
donoway/BoolQ_Llama-3.2-1B-n5s6b4x8	donoway	2025-08-18T22:05:41Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T20:27:42Z	--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: BoolQ_Llama-3.2-1B-n5s6b4x8 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. --> # BoolQ_Llama-3.2-1B-n5s6b4x8 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4568 - Model Preparation Time: 0.0058 - Mdl: 6872.6248 - Accumulated Loss: 4763.7405 - Correct Preds: 2760.0 - Total Preds: 3270.0 - Accuracy: 0.8440 - Correct Gen Preds: 2757.0 - Gen Accuracy: 0.8431 - Correct Gen Preds 9642: 1814.0 - Correct Preds 9642: 1823.0 - Total Labels 9642: 2026.0 - Accuracy 9642: 0.8998 - Gen Accuracy 9642: 0.8954 - Correct Gen Preds 2822: 934.0 - Correct Preds 2822: 937.0 - Total Labels 2822: 1231.0 - Accuracy 2822: 0.7612 - Gen Accuracy 2822: 0.7587 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 120 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Mdl \| Accumulated Loss \| Correct Preds \| Total Preds \| Accuracy \| Correct Gen Preds \| Gen Accuracy \| Correct Gen Preds 9642 \| Correct Preds 9642 \| Total Labels 9642 \| Accuracy 9642 \| Gen Accuracy 9642 \| Correct Gen Preds 2822 \| Correct Preds 2822 \| Total Labels 2822 \| Accuracy 2822 \| Gen Accuracy 2822 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------------:\|:---------:\|:----------------:\|:-------------:\|:-----------:\|:--------:\|:-----------------:\|:------------:\|:----------------------:\|:------------------:\|:-----------------:\|:-------------:\|:-----------------:\|:----------------------:\|:------------------:\|:-----------------:\|:-------------:\|:-----------------:\| \| No log \| 0 \| 0 \| 0.7080 \| 0.0058 \| 3339.8933 \| 2315.0376 \| 2032.0 \| 3270.0 \| 0.6214 \| 2040.0 \| 0.6239 \| 2007.0 \| 2008.0 \| 2026.0 \| 0.9911 \| 0.9906 \| 24.0 \| 24.0 \| 1231.0 \| 0.0195 \| 0.0195 \| \| 0.3324 \| 1.0 \| 232 \| 0.4426 \| 0.0058 \| 2088.1525 \| 1447.3970 \| 2676.0 \| 3270.0 \| 0.8183 \| 2679.0 \| 0.8193 \| 1808.0 \| 1809.0 \| 2026.0 \| 0.8929 \| 0.8924 \| 862.0 \| 867.0 \| 1231.0 \| 0.7043 \| 0.7002 \| \| 0.4917 \| 2.0 \| 464 \| 0.4887 \| 0.0058 \| 2305.5456 \| 1598.0824 \| 2697.0 \| 3270.0 \| 0.8248 \| 2690.0 \| 0.8226 \| 1887.0 \| 1893.0 \| 2026.0 \| 0.9344 \| 0.9314 \| 797.0 \| 804.0 \| 1231.0 \| 0.6531 \| 0.6474 \| \| 0.0028 \| 3.0 \| 696 \| 0.7594 \| 0.0058 \| 3582.3567 \| 2483.1004 \| 2676.0 \| 3270.0 \| 0.8183 \| 2659.0 \| 0.8131 \| 1641.0 \| 1663.0 \| 2026.0 \| 0.8208 \| 0.8100 \| 1009.0 \| 1013.0 \| 1231.0 \| 0.8229 \| 0.8197 \| \| 0.0011 \| 4.0 \| 928 \| 0.9795 \| 0.0058 \| 4620.9576 \| 3203.0038 \| 2731.0 \| 3270.0 \| 0.8352 \| 2723.0 \| 0.8327 \| 1788.0 \| 1799.0 \| 2026.0 \| 0.8880 \| 0.8825 \| 927.0 \| 932.0 \| 1231.0 \| 0.7571 \| 0.7530 \| \| 0.1389 \| 5.0 \| 1160 \| 1.0611 \| 0.0058 \| 5005.9836 \| 3469.8834 \| 2739.0 \| 3270.0 \| 0.8376 \| 2737.0 \| 0.8370 \| 1847.0 \| 1853.0 \| 2026.0 \| 0.9146 \| 0.9116 \| 882.0 \| 886.0 \| 1231.0 \| 0.7197 \| 0.7165 \| \| 0.0002 \| 6.0 \| 1392 \| 1.1056 \| 0.0058 \| 5215.9426 \| 3615.4159 \| 2749.0 \| 3270.0 \| 0.8407 \| 2751.0 \| 0.8413 \| 1881.0 \| 1885.0 \| 2026.0 \| 0.9304 \| 0.9284 \| 862.0 \| 864.0 \| 1231.0 \| 0.7019 \| 0.7002 \| \| 0.0001 \| 7.0 \| 1624 \| 1.2332 \| 0.0058 \| 5817.6850 \| 4032.5120 \| 2754.0 \| 3270.0 \| 0.8422 \| 2738.0 \| 0.8373 \| 1806.0 \| 1824.0 \| 2026.0 \| 0.9003 \| 0.8914 \| 923.0 \| 930.0 \| 1231.0 \| 0.7555 \| 0.7498 \| \| 0.0 \| 8.0 \| 1856 \| 1.2209 \| 0.0058 \| 5759.8281 \| 3992.4086 \| 2754.0 \| 3270.0 \| 0.8422 \| 2753.0 \| 0.8419 \| 1788.0 \| 1798.0 \| 2026.0 \| 0.8875 \| 0.8825 \| 956.0 \| 956.0 \| 1231.0 \| 0.7766 \| 0.7766 \| \| 0.0 \| 9.0 \| 2088 \| 1.4452 \| 0.0058 \| 6817.7274 \| 4725.6885 \| 2750.0 \| 3270.0 \| 0.8410 \| 2746.0 \| 0.8398 \| 1815.0 \| 1825.0 \| 2026.0 \| 0.9008 \| 0.8959 \| 922.0 \| 925.0 \| 1231.0 \| 0.7514 \| 0.7490 \| \| 0.0 \| 10.0 \| 2320 \| 1.4119 \| 0.0058 \| 6660.5648 \| 4616.7517 \| 2752.0 \| 3270.0 \| 0.8416 \| 2749.0 \| 0.8407 \| 1797.0 \| 1807.0 \| 2026.0 \| 0.8919 \| 0.8870 \| 943.0 \| 945.0 \| 1231.0 \| 0.7677 \| 0.7660 \| \| 0.0 \| 11.0 \| 2552 \| 1.4389 \| 0.0058 \| 6788.4022 \| 4705.3618 \| 2753.0 \| 3270.0 \| 0.8419 \| 2751.0 \| 0.8413 \| 1813.0 \| 1822.0 \| 2026.0 \| 0.8993 \| 0.8949 \| 929.0 \| 931.0 \| 1231.0 \| 0.7563 \| 0.7547 \| \| 0.0 \| 12.0 \| 2784 \| 1.4300 \| 0.0058 \| 6746.3247 \| 4676.1959 \| 2755.0 \| 3270.0 \| 0.8425 \| 2752.0 \| 0.8416 \| 1812.0 \| 1821.0 \| 2026.0 \| 0.8988 \| 0.8944 \| 931.0 \| 934.0 \| 1231.0 \| 0.7587 \| 0.7563 \| \| 0.0 \| 13.0 \| 3016 \| 1.4335 \| 0.0058 \| 6762.4940 \| 4687.4036 \| 2756.0 \| 3270.0 \| 0.8428 \| 2750.0 \| 0.8410 \| 1806.0 \| 1819.0 \| 2026.0 \| 0.8978 \| 0.8914 \| 935.0 \| 937.0 \| 1231.0 \| 0.7612 \| 0.7595 \| \| 0.0 \| 14.0 \| 3248 \| 1.4568 \| 0.0058 \| 6872.6248 \| 4763.7405 \| 2760.0 \| 3270.0 \| 0.8440 \| 2757.0 \| 0.8431 \| 1814.0 \| 1823.0 \| 2026.0 \| 0.8998 \| 0.8954 \| 934.0 \| 937.0 \| 1231.0 \| 0.7612 \| 0.7587 \| \| 0.0 \| 15.0 \| 3480 \| 1.4631 \| 0.0058 \| 6902.2813 \| 4784.2968 \| 2750.0 \| 3270.0 \| 0.8410 \| 2739.0 \| 0.8376 \| 1792.0 \| 1809.0 \| 2026.0 \| 0.8929 \| 0.8845 \| 938.0 \| 941.0 \| 1231.0 \| 0.7644 \| 0.7620 \| \| 0.0 \| 16.0 \| 3712 \| 1.4765 \| 0.0058 \| 6965.4556 \| 4828.0859 \| 2754.0 \| 3270.0 \| 0.8422 \| 2743.0 \| 0.8388 \| 1797.0 \| 1814.0 \| 2026.0 \| 0.8954 \| 0.8870 \| 937.0 \| 940.0 \| 1231.0 \| 0.7636 \| 0.7612 \| \| 0.0 \| 17.0 \| 3944 \| 1.4796 \| 0.0058 \| 6980.1585 \| 4838.2772 \| 2751.0 \| 3270.0 \| 0.8413 \| 2745.0 \| 0.8394 \| 1799.0 \| 1812.0 \| 2026.0 \| 0.8944 \| 0.8880 \| 937.0 \| 939.0 \| 1231.0 \| 0.7628 \| 0.7612 \| \| 0.0 \| 18.0 \| 4176 \| 1.4793 \| 0.0058 \| 6978.7939 \| 4837.3313 \| 2755.0 \| 3270.0 \| 0.8425 \| 2748.0 \| 0.8404 \| 1799.0 \| 1813.0 \| 2026.0 \| 0.8949 \| 0.8880 \| 940.0 \| 942.0 \| 1231.0 \| 0.7652 \| 0.7636 \| \| 0.0 \| 19.0 \| 4408 \| 1.4822 \| 0.0058 \| 6992.2377 \| 4846.6498 \| 2752.0 \| 3270.0 \| 0.8416 \| 2742.0 \| 0.8385 \| 1798.0 \| 1815.0 \| 2026.0 \| 0.8959 \| 0.8875 \| 935.0 \| 937.0 \| 1231.0 \| 0.7612 \| 0.7595 \| \| 0.0 \| 20.0 \| 4640 \| 1.4798 \| 0.0058 \| 6980.8944 \| 4838.7873 \| 2753.0 \| 3270.0 \| 0.8419 \| 2745.0 \| 0.8394 \| 1798.0 \| 1812.0 \| 2026.0 \| 0.8944 \| 0.8875 \| 938.0 \| 941.0 \| 1231.0 \| 0.7644 \| 0.7620 \| \| 0.0 \| 21.0 \| 4872 \| 1.4847 \| 0.0058 \| 7004.0401 \| 4854.8307 \| 2755.0 \| 3270.0 \| 0.8425 \| 2748.0 \| 0.8404 \| 1801.0 \| 1815.0 \| 2026.0 \| 0.8959 \| 0.8889 \| 938.0 \| 940.0 \| 1231.0 \| 0.7636 \| 0.7620 \| \| 0.0 \| 22.0 \| 5104 \| 1.4801 \| 0.0058 \| 6982.3382 \| 4839.7880 \| 2754.0 \| 3270.0 \| 0.8422 \| 2746.0 \| 0.8398 \| 1797.0 \| 1812.0 \| 2026.0 \| 0.8944 \| 0.8870 \| 940.0 \| 942.0 \| 1231.0 \| 0.7652 \| 0.7636 \| \| 0.0 \| 23.0 \| 5336 \| 1.4791 \| 0.0058 \| 6977.9730 \| 4836.7623 \| 2756.0 \| 3270.0 \| 0.8428 \| 2747.0 \| 0.8401 \| 1801.0 \| 1816.0 \| 2026.0 \| 0.8963 \| 0.8889 \| 937.0 \| 940.0 \| 1231.0 \| 0.7636 \| 0.7612 \| \| 0.0 \| 24.0 \| 5568 \| 1.4821 \| 0.0058 \| 6991.9891 \| 4846.4775 \| 2751.0 \| 3270.0 \| 0.8413 \| 2743.0 \| 0.8388 \| 1797.0 \| 1812.0 \| 2026.0 \| 0.8944 \| 0.8870 \| 937.0 \| 939.0 \| 1231.0 \| 0.7628 \| 0.7612 \| \| 0.0 \| 25.0 \| 5800 \| 1.4844 \| 0.0058 \| 7003.0013 \| 4854.1106 \| 2754.0 \| 3270.0 \| 0.8422 \| 2746.0 \| 0.8398 \| 1799.0 \| 1812.0 \| 2026.0 \| 0.8944 \| 0.8880 \| 938.0 \| 942.0 \| 1231.0 \| 0.7652 \| 0.7620 \| \| 0.0 \| 26.0 \| 6032 \| 1.4848 \| 0.0058 \| 7004.8082 \| 4855.3631 \| 2760.0 \| 3270.0 \| 0.8440 \| 2750.0 \| 0.8410 \| 1800.0 \| 1816.0 \| 2026.0 \| 0.8963 \| 0.8885 \| 941.0 \| 944.0 \| 1231.0 \| 0.7669 \| 0.7644 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755554535	IvanJAjebu	2025-08-18T22:04:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:03:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
unitova/blockassist-bc-zealous_sneaky_raven_1755553093	unitova	2025-08-18T22:03:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:03:30Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - zealous sneaky raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lemonhat/Qwen2.5-Coder-7B-Instruct-swe_5k_v1_tag4	lemonhat	2025-08-18T22:02:10Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-Coder-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-7B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T22:00:40Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-Coder-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: swe_5k_v1_tag4 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. --> # swe_5k_v1_tag4 This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the swe_5k_v1_tag4 dataset. It achieves the following results on the evaluation set: - Loss: 0.3938 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 4 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.3715 \| 0.9709 \| 100 \| 0.3940 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
emre0005/blockassist-bc-humming_winged_okapi_1755554481	emre0005	2025-08-18T22:02:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "humming winged okapi", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:01:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - humming winged okapi --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
zenqqq/blockassist-bc-restless_reptilian_caterpillar_1755554381	zenqqq	2025-08-18T22:00:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "restless reptilian caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-18T22:00:28Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - restless reptilian caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
aliangdw/rfm_v3	aliangdw	2025-08-18T22:00:21Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "reward-model", "rfm", "vision-language", "multimodal", "base_model:Qwen/Qwen2.5-VL-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-08-18T21:53:40Z	--- license: apache-2.0 base_model: Qwen/Qwen2.5-VL-3B-Instruct tags: - reward-model - rfm - vision-language - multimodal library_name: transformers --- # aliangdw/rfm_v3 This is a Reward Function Model (RFM) for vision-language preference learning and similarity assessment. ## Model Details - Base Model: Qwen/Qwen2.5-VL-3B-Instruct - Model Type: qwen2_5_vl - Architecture: RFMModel - Task: Vision-Language Reward Modeling - Training Method: FSDP (Fully Sharded Data Parallel) ## Usage ```python from transformers import AutoProcessor, AutoModel import torch # Load model and processor processor = AutoProcessor.from_pretrained("aliangdw/rfm_v3", trust_remote_code=True) model = AutoModel.from_pretrained("aliangdw/rfm_v3", trust_remote_code=True) # Example usage for preference scoring # inputs = processor(images=images, text=text, return_tensors="pt") # outputs = model(inputs, sample_type="preference") ``` ## Model Capabilities This RFM model can perform: 1. Preference Prediction: Given two trajectories A and B, predict which one is preferred 2. Similarity Assessment: Evaluate how similar a trajectory is to a reference 3. Progress Estimation**: Estimate task completion progress ## Training The model was trained using: - FSDP for distributed training - Mixed precision (bfloat16) - Custom loss functions for preference and similarity learning ## Files This repository contains: - Model weights in SafeTensors format - Configuration files - Tokenizer/Processor files ## Citation If you use this model, please cite:

dodo1234/mistral-classification-model-mergeeed

dodo1234

2025-08-18T22:58:44Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-08-18T22:58:14Z

--- base_model: mistral-classification-model-mergeed tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** dodo1234 - **License:** apache-2.0 - **Finetuned from model :** mistral-classification-model-mergeed This mistral 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)

crystalline7/1363028

crystalline7

2025-08-18T22:58:33Z

0

null

[ "region:us" ]

null

2025-08-18T22:58:29Z

[View on Civ Archive](https://civarchive.com/models/1295323?modelVersionId=1461911)

crystalline7/1180705

crystalline7

2025-08-18T22:58:24Z

0

null

[ "region:us" ]

null

2025-08-18T22:58:21Z

[View on Civ Archive](https://civarchive.com/models/1134818?modelVersionId=1275898)

seraphimzzzz/1200252

seraphimzzzz

2025-08-18T22:58:17Z

0

null

[ "region:us" ]

null

2025-08-18T22:58:14Z

[View on Civ Archive](https://civarchive.com/models/1152263?modelVersionId=1296011)

razor534/blockassist-bc-lazy_extinct_termite_1755557840

razor534

2025-08-18T22:58:12Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "lazy extinct termite", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:58:03Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - lazy extinct termite --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

ultratopaz/1623777

ultratopaz

2025-08-18T22:58:07Z

0

null

[ "region:us" ]

null

2025-08-18T22:58:00Z

[View on Civ Archive](https://civarchive.com/models/693933?modelVersionId=1723260)

ultratopaz/1359879

ultratopaz

2025-08-18T22:57:55Z

0

null

[ "region:us" ]

null

2025-08-18T22:57:52Z

[View on Civ Archive](https://civarchive.com/models/1292588?modelVersionId=1458722)

crystalline7/1454395

crystalline7

2025-08-18T22:57:48Z

0

null

[ "region:us" ]

null

2025-08-18T22:57:45Z

[View on Civ Archive](https://civarchive.com/models/1375798?modelVersionId=1554509)

ultratopaz/1488700

ultratopaz

2025-08-18T22:57:31Z

0

null

[ "region:us" ]

null

2025-08-18T22:57:23Z

[View on Civ Archive](https://civarchive.com/models/1285488?modelVersionId=1588898)

ultratopaz/1299082

ultratopaz

2025-08-18T22:56:12Z

0

null

[ "region:us" ]

null

2025-08-18T22:56:10Z

[View on Civ Archive](https://civarchive.com/models/1239292?modelVersionId=1396676)

databoyface/python-tf-ome-keras-v4.1

databoyface

2025-08-18T22:56:09Z

0

1

null

[ "en", "dataset:databoyface/python-tf-ome-src-v4.1", "license:mit", "region:us" ]

null

2025-08-18T15:00:10Z

--- license: mit datasets: - databoyface/python-tf-ome-src-v4.1 language: - en --- # Orthogonal Model of Emotions A Text Classifier created using TensorFlow and Keras ## Author C.J. Pitchford ## Published 18 August 2025 ## Model and Weights Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding (Embedding) (None, 1000, 64) 6400000 bidirectional (Bidirection (None, 1000, 128) 66048 al) global_max_pooling1d (Glob (None, 128) 0 alMaxPooling1D) dense (Dense) (None, 64) 8256 dropout (Dropout) (None, 64) 0 dense_1 (Dense) (None, 47) 3055 ================================================================= Total params: 6477359 (24.71 MB) Trainable params: 6477359 (24.71 MB) Non-trainable params: 0 (0.00 Byte) ## Usage import numpy as np import tensorflow as tf import tensorflow.keras.preprocessing.text as text import pickle from tensorflow.keras.preprocessing.sequence import pad_sequences # 1. Load pre-trained model model = tf.keras.models.load_model('OME4tf/ome-4a-model.h5') # 2. Load tokenizer and label encoder with open('OME4tf/ome-4a-tokenizer.pkl', 'rb') as f: tokenizer = pickle.load(f) with open('OME4tf/ome-4a-label_encoder.pkl', 'rb') as f: label_encoder = pickle.load(f) # 3. Test model with prediction on text "I failed to hide my distress." text = "I failed to hide my distress." text_seq = tokenizer.texts_to_sequences([text]) max_len = 1000 text_seq = pad_sequences(text_seq, maxlen=max_len, padding='post') pred_probs = model.predict(text_seq) pred_label = np.argmax(pred_probs, axis=1) print(f"Statement: {text}\nPrediction: {label_encoder.classes_[pred_label][0]}") ## Additional Tokenizer and label encoder included as JSON to avoid using `pickle` files.

seraphimzzzz/1316758

seraphimzzzz

2025-08-18T22:55:58Z

0

null

[ "region:us" ]

null

2025-08-18T22:55:54Z

[View on Civ Archive](https://civarchive.com/models/1254992?modelVersionId=1414887)

seraphimzzzz/1263759

seraphimzzzz

2025-08-18T22:54:58Z

0

null

[ "region:us" ]

null

2025-08-18T22:54:56Z

[View on Civ Archive](https://civarchive.com/models/1208027?modelVersionId=1360518)

crystalline7/1444117

crystalline7

2025-08-18T22:53:33Z

0

null

[ "region:us" ]

null

2025-08-18T22:53:30Z

[View on Civ Archive](https://civarchive.com/models/1366927?modelVersionId=1544263)

crystalline7/1439734

crystalline7

2025-08-18T22:52:44Z

0

null

[ "region:us" ]

null

2025-08-18T22:52:41Z

[View on Civ Archive](https://civarchive.com/models/361569?modelVersionId=1539769)

Dejiat/blockassist-bc-savage_unseen_bobcat_1755557483

Dejiat

2025-08-18T22:52:08Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:52:03Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

g-assismoraes/Qwen3-4B-Base-fpi-alpha0.12-var-hatebr-ep30-g5

g-assismoraes

2025-08-18T22:51:51Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-18T22:48:07Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755557298

IvanJAjebu

2025-08-18T22:50:00Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:49:35Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Dejiat/blockassist-bc-savage_unseen_bobcat_1755557262

Dejiat

2025-08-18T22:48:21Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:48:17Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

donoway/ARC-Challenge_Llama-3.2-1B-hifbxhfd

donoway

2025-08-18T22:48:20Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-18T22:36:19Z

--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: ARC-Challenge_Llama-3.2-1B-hifbxhfd results: [] ---  # ARC-Challenge_Llama-3.2-1B-hifbxhfd This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.9794 - Model Preparation Time: 0.0063 - Mdl: 2147.9346 - Accumulated Loss: 1488.8348 - Correct Preds: 120.0 - Total Preds: 299.0 - Accuracy: 0.4013 - Correct Gen Preds: 116.0 - Gen Accuracy: 0.3880 - Correct Gen Preds 32: 15.0 - Correct Preds 32: 15.0 - Total Labels 32: 64.0 - Accuracy 32: 0.2344 - Gen Accuracy 32: 0.2344 - Correct Gen Preds 33: 41.0 - Correct Preds 33: 43.0 - Total Labels 33: 73.0 - Accuracy 33: 0.5890 - Gen Accuracy 33: 0.5616 - Correct Gen Preds 34: 31.0 - Correct Preds 34: 31.0 - Total Labels 34: 78.0 - Accuracy 34: 0.3974 - Gen Accuracy 34: 0.3974 - Correct Gen Preds 35: 29.0 - Correct Preds 35: 31.0 - Total Labels 35: 83.0 - Accuracy 35: 0.3735 - Gen Accuracy 35: 0.3494 - Correct Gen Preds 36: 0.0 - Correct Preds 36: 0.0 - Total Labels 36: 1.0 - Accuracy 36: 0.0 - Gen Accuracy 36: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 112 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 32 | Correct Preds 32 | Total Labels 32 | Accuracy 32 | Gen Accuracy 32 | Correct Gen Preds 33 | Correct Preds 33 | Total Labels 33 | Accuracy 33 | Gen Accuracy 33 | Correct Gen Preds 34 | Correct Preds 34 | Total Labels 34 | Accuracy 34 | Gen Accuracy 34 | Correct Gen Preds 35 | Correct Preds 35 | Total Labels 35 | Accuracy 35 | Gen Accuracy 35 | Correct Gen Preds 36 | Correct Preds 36 | Total Labels 36 | Accuracy 36 | Gen Accuracy 36 | |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:| | No log | 0 | 0 | 1.6389 | 0.0063 | 706.9523 | 490.0220 | 66.0 | 299.0 | 0.2207 | 66.0 | 0.2207 | 62.0 | 62.0 | 64.0 | 0.9688 | 0.9688 | 0.0 | 0.0 | 73.0 | 0.0 | 0.0 | 4.0 | 4.0 | 78.0 | 0.0513 | 0.0513 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 1.4701 | 1.0 | 3 | 1.5924 | 0.0063 | 686.8892 | 476.1153 | 76.0 | 299.0 | 0.2542 | 75.0 | 0.2508 | 7.0 | 7.0 | 64.0 | 0.1094 | 0.1094 | 68.0 | 69.0 | 73.0 | 0.9452 | 0.9315 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 1.4771 | 2.0 | 6 | 1.4077 | 0.0063 | 607.2453 | 420.9104 | 77.0 | 299.0 | 0.2575 | 74.0 | 0.2475 | 36.0 | 37.0 | 64.0 | 0.5781 | 0.5625 | 7.0 | 7.0 | 73.0 | 0.0959 | 0.0959 | 0.0 | 0.0 | 78.0 | 0.0 | 0.0 | 31.0 | 33.0 | 83.0 | 0.3976 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 1.0843 | 3.0 | 9 | 1.4049 | 0.0063 | 606.0456 | 420.0788 | 99.0 | 299.0 | 0.3311 | 98.0 | 0.3278 | 24.0 | 25.0 | 64.0 | 0.3906 | 0.375 | 40.0 | 40.0 | 73.0 | 0.5479 | 0.5479 | 21.0 | 21.0 | 78.0 | 0.2692 | 0.2692 | 13.0 | 13.0 | 83.0 | 0.1566 | 0.1566 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.4069 | 4.0 | 12 | 2.1331 | 0.0063 | 920.1642 | 637.8092 | 114.0 | 299.0 | 0.3813 | 105.0 | 0.3512 | 24.0 | 26.0 | 64.0 | 0.4062 | 0.375 | 24.0 | 26.0 | 73.0 | 0.3562 | 0.3288 | 22.0 | 25.0 | 78.0 | 0.3205 | 0.2821 | 35.0 | 37.0 | 83.0 | 0.4458 | 0.4217 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0958 | 5.0 | 15 | 3.1166 | 0.0063 | 1344.3795 | 931.8528 | 106.0 | 299.0 | 0.3545 | 91.0 | 0.3043 | 11.0 | 15.0 | 64.0 | 0.2344 | 0.1719 | 28.0 | 30.0 | 73.0 | 0.4110 | 0.3836 | 18.0 | 22.0 | 78.0 | 0.2821 | 0.2308 | 34.0 | 39.0 | 83.0 | 0.4699 | 0.4096 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0596 | 6.0 | 18 | 4.9794 | 0.0063 | 2147.9346 | 1488.8348 | 120.0 | 299.0 | 0.4013 | 116.0 | 0.3880 | 15.0 | 15.0 | 64.0 | 0.2344 | 0.2344 | 41.0 | 43.0 | 73.0 | 0.5890 | 0.5616 | 31.0 | 31.0 | 78.0 | 0.3974 | 0.3974 | 29.0 | 31.0 | 83.0 | 0.3735 | 0.3494 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0006 | 7.0 | 21 | 5.7281 | 0.0063 | 2470.9158 | 1712.7083 | 118.0 | 299.0 | 0.3946 | 116.0 | 0.3880 | 20.0 | 20.0 | 64.0 | 0.3125 | 0.3125 | 35.0 | 36.0 | 73.0 | 0.4932 | 0.4795 | 31.0 | 31.0 | 78.0 | 0.3974 | 0.3974 | 30.0 | 31.0 | 83.0 | 0.3735 | 0.3614 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0001 | 8.0 | 24 | 6.5268 | 0.0063 | 2815.4597 | 1951.5280 | 116.0 | 299.0 | 0.3880 | 115.0 | 0.3846 | 25.0 | 25.0 | 64.0 | 0.3906 | 0.3906 | 31.0 | 31.0 | 73.0 | 0.4247 | 0.4247 | 28.0 | 28.0 | 78.0 | 0.3590 | 0.3590 | 31.0 | 32.0 | 83.0 | 0.3855 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 9.0 | 27 | 6.9695 | 0.0063 | 3006.4105 | 2083.8849 | 113.0 | 299.0 | 0.3779 | 112.0 | 0.3746 | 27.0 | 27.0 | 64.0 | 0.4219 | 0.4219 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 26.0 | 26.0 | 78.0 | 0.3333 | 0.3333 | 31.0 | 32.0 | 83.0 | 0.3855 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 10.0 | 30 | 7.2697 | 0.0063 | 3135.8946 | 2173.6365 | 113.0 | 299.0 | 0.3779 | 112.0 | 0.3746 | 28.0 | 28.0 | 64.0 | 0.4375 | 0.4375 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 25.0 | 25.0 | 78.0 | 0.3205 | 0.3205 | 31.0 | 32.0 | 83.0 | 0.3855 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 11.0 | 33 | 7.4778 | 0.0063 | 3225.6685 | 2235.8630 | 113.0 | 299.0 | 0.3779 | 112.0 | 0.3746 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 27.0 | 27.0 | 73.0 | 0.3699 | 0.3699 | 25.0 | 25.0 | 78.0 | 0.3205 | 0.3205 | 31.0 | 32.0 | 83.0 | 0.3855 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 12.0 | 36 | 7.5700 | 0.0063 | 3265.4444 | 2263.4336 | 113.0 | 299.0 | 0.3779 | 113.0 | 0.3779 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 25.0 | 25.0 | 78.0 | 0.3205 | 0.3205 | 31.0 | 31.0 | 83.0 | 0.3735 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 13.0 | 39 | 7.6337 | 0.0063 | 3292.8992 | 2282.4638 | 115.0 | 299.0 | 0.3846 | 115.0 | 0.3846 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 27.0 | 27.0 | 78.0 | 0.3462 | 0.3462 | 31.0 | 31.0 | 83.0 | 0.3735 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 14.0 | 42 | 7.7177 | 0.0063 | 3329.1652 | 2307.6014 | 115.0 | 299.0 | 0.3846 | 114.0 | 0.3813 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 26.0 | 26.0 | 78.0 | 0.3333 | 0.3333 | 31.0 | 32.0 | 83.0 | 0.3855 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 15.0 | 45 | 7.7144 | 0.0063 | 3327.7168 | 2306.5975 | 113.0 | 299.0 | 0.3779 | 112.0 | 0.3746 | 28.0 | 28.0 | 64.0 | 0.4375 | 0.4375 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 25.0 | 25.0 | 78.0 | 0.3205 | 0.3205 | 31.0 | 32.0 | 83.0 | 0.3855 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 16.0 | 48 | 7.7456 | 0.0063 | 3341.2047 | 2315.9466 | 114.0 | 299.0 | 0.3813 | 114.0 | 0.3813 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 26.0 | 26.0 | 78.0 | 0.3333 | 0.3333 | 31.0 | 31.0 | 83.0 | 0.3735 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 17.0 | 51 | 7.7246 | 0.0063 | 3332.1346 | 2309.6597 | 113.0 | 299.0 | 0.3779 | 112.0 | 0.3746 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 27.0 | 27.0 | 73.0 | 0.3699 | 0.3699 | 25.0 | 25.0 | 78.0 | 0.3205 | 0.3205 | 31.0 | 32.0 | 83.0 | 0.3855 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 18.0 | 54 | 7.7910 | 0.0063 | 3360.7844 | 2329.5182 | 113.0 | 299.0 | 0.3779 | 112.0 | 0.3746 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 24.0 | 24.0 | 78.0 | 0.3077 | 0.3077 | 31.0 | 32.0 | 83.0 | 0.3855 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 19.0 | 57 | 7.8124 | 0.0063 | 3369.9946 | 2335.9022 | 114.0 | 299.0 | 0.3813 | 114.0 | 0.3813 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 26.0 | 26.0 | 78.0 | 0.3333 | 0.3333 | 31.0 | 31.0 | 83.0 | 0.3735 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 20.0 | 60 | 7.7234 | 0.0063 | 3331.5996 | 2309.2889 | 115.0 | 299.0 | 0.3846 | 115.0 | 0.3846 | 30.0 | 30.0 | 64.0 | 0.4688 | 0.4688 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 26.0 | 26.0 | 78.0 | 0.3333 | 0.3333 | 31.0 | 31.0 | 83.0 | 0.3735 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 21.0 | 63 | 7.7679 | 0.0063 | 3350.8072 | 2322.6025 | 115.0 | 299.0 | 0.3846 | 115.0 | 0.3846 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 27.0 | 27.0 | 78.0 | 0.3462 | 0.3462 | 31.0 | 31.0 | 83.0 | 0.3735 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 22.0 | 66 | 7.7526 | 0.0063 | 3344.1986 | 2318.0219 | 115.0 | 299.0 | 0.3846 | 115.0 | 0.3846 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 27.0 | 27.0 | 78.0 | 0.3462 | 0.3462 | 31.0 | 31.0 | 83.0 | 0.3735 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 23.0 | 69 | 7.7910 | 0.0063 | 3360.7764 | 2329.5127 | 112.0 | 299.0 | 0.3746 | 112.0 | 0.3746 | 28.0 | 28.0 | 64.0 | 0.4375 | 0.4375 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 25.0 | 25.0 | 78.0 | 0.3205 | 0.3205 | 31.0 | 31.0 | 83.0 | 0.3735 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 24.0 | 72 | 7.7183 | 0.0063 | 3329.3986 | 2307.7632 | 115.0 | 299.0 | 0.3846 | 115.0 | 0.3846 | 30.0 | 30.0 | 64.0 | 0.4688 | 0.4688 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 26.0 | 26.0 | 78.0 | 0.3333 | 0.3333 | 31.0 | 31.0 | 83.0 | 0.3735 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 25.0 | 75 | 7.7304 | 0.0063 | 3334.6225 | 2311.3842 | 114.0 | 299.0 | 0.3813 | 114.0 | 0.3813 | 30.0 | 30.0 | 64.0 | 0.4688 | 0.4688 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 25.0 | 25.0 | 78.0 | 0.3205 | 0.3205 | 31.0 | 31.0 | 83.0 | 0.3735 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 26.0 | 78 | 7.7551 | 0.0063 | 3345.2652 | 2318.7612 | 114.0 | 299.0 | 0.3813 | 113.0 | 0.3779 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 25.0 | 25.0 | 78.0 | 0.3205 | 0.3205 | 31.0 | 32.0 | 83.0 | 0.3855 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 27.0 | 81 | 7.7737 | 0.0063 | 3353.3180 | 2324.3429 | 116.0 | 299.0 | 0.3880 | 115.0 | 0.3846 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 27.0 | 27.0 | 78.0 | 0.3462 | 0.3462 | 31.0 | 32.0 | 83.0 | 0.3855 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 28.0 | 84 | 7.7507 | 0.0063 | 3343.3752 | 2317.4511 | 116.0 | 299.0 | 0.3880 | 115.0 | 0.3846 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 27.0 | 27.0 | 78.0 | 0.3462 | 0.3462 | 31.0 | 32.0 | 83.0 | 0.3855 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 29.0 | 87 | 7.7632 | 0.0063 | 3348.7890 | 2321.2037 | 116.0 | 299.0 | 0.3880 | 115.0 | 0.3846 | 30.0 | 30.0 | 64.0 | 0.4688 | 0.4688 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 26.0 | 26.0 | 78.0 | 0.3333 | 0.3333 | 31.0 | 32.0 | 83.0 | 0.3855 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 30.0 | 90 | 7.7401 | 0.0063 | 3338.8227 | 2314.2955 | 116.0 | 299.0 | 0.3880 | 115.0 | 0.3846 | 30.0 | 30.0 | 64.0 | 0.4688 | 0.4688 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 26.0 | 26.0 | 78.0 | 0.3333 | 0.3333 | 31.0 | 32.0 | 83.0 | 0.3855 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 31.0 | 93 | 7.7578 | 0.0063 | 3346.4502 | 2319.5825 | 113.0 | 299.0 | 0.3779 | 113.0 | 0.3779 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 25.0 | 25.0 | 78.0 | 0.3205 | 0.3205 | 31.0 | 31.0 | 83.0 | 0.3735 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 32.0 | 96 | 7.7958 | 0.0063 | 3362.8384 | 2330.9420 | 112.0 | 299.0 | 0.3746 | 112.0 | 0.3746 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 27.0 | 27.0 | 73.0 | 0.3699 | 0.3699 | 25.0 | 25.0 | 78.0 | 0.3205 | 0.3205 | 31.0 | 31.0 | 83.0 | 0.3735 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 33.0 | 99 | 7.7635 | 0.0063 | 3348.8919 | 2321.2750 | 114.0 | 299.0 | 0.3813 | 114.0 | 0.3813 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 26.0 | 26.0 | 78.0 | 0.3333 | 0.3333 | 31.0 | 31.0 | 83.0 | 0.3735 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 34.0 | 102 | 7.7796 | 0.0063 | 3355.8332 | 2326.0863 | 114.0 | 299.0 | 0.3813 | 113.0 | 0.3779 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 25.0 | 25.0 | 78.0 | 0.3205 | 0.3205 | 31.0 | 32.0 | 83.0 | 0.3855 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 35.0 | 105 | 7.7913 | 0.0063 | 3360.8969 | 2329.5962 | 113.0 | 299.0 | 0.3779 | 113.0 | 0.3779 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 25.0 | 25.0 | 78.0 | 0.3205 | 0.3205 | 31.0 | 31.0 | 83.0 | 0.3735 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 36.0 | 108 | 7.7978 | 0.0063 | 3363.7162 | 2331.5504 | 112.0 | 299.0 | 0.3746 | 112.0 | 0.3746 | 29.0 | 29.0 | 64.0 | 0.4531 | 0.4531 | 28.0 | 28.0 | 73.0 | 0.3836 | 0.3836 | 24.0 | 24.0 | 78.0 | 0.3077 | 0.3077 | 31.0 | 31.0 | 83.0 | 0.3735 | 0.3735 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755557207

AminuPeril

2025-08-18T22:47:18Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:47:12Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755555569

vwzyrraz7l

2025-08-18T22:46:01Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:45:58Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Dejiat/blockassist-bc-savage_unseen_bobcat_1755557050

Dejiat

2025-08-18T22:44:51Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:44:49Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

emre0005/blockassist-bc-humming_winged_okapi_1755557030

emre0005

2025-08-18T22:44:33Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "humming winged okapi", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:44:27Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - humming winged okapi --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

seraphimzzzz/1410215

seraphimzzzz

2025-08-18T22:42:54Z

0

null

[ "region:us" ]

null

2025-08-18T22:42:47Z

[View on Civ Archive](https://civarchive.com/models/1252497?modelVersionId=1510150)

crystalline7/1474712

crystalline7

2025-08-18T22:42:36Z

0

null

[ "region:us" ]

null

2025-08-18T22:42:32Z

[View on Civ Archive](https://civarchive.com/models/1333275?modelVersionId=1574869)

ultratopaz/1488010

ultratopaz

2025-08-18T22:42:25Z

0

null

[ "region:us" ]

null

2025-08-18T22:42:18Z

[View on Civ Archive](https://civarchive.com/models/1266645?modelVersionId=1588242)

johngreendr1/b3b0ee9e-8d46-4e86-96b5-3dfd60d79bf1

johngreendr1

2025-08-18T22:42:15Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:jhflow/mistral7b-lora-multi-turn-v2", "base_model:adapter:jhflow/mistral7b-lora-multi-turn-v2", "region:us" ]

null

2025-08-18T19:21:57Z

--- base_model: jhflow/mistral7b-lora-multi-turn-v2 library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1

Azurastar2903/Llama-3.2-1B-rk3588-1.2.1

Azurastar2903

2025-08-18T22:41:28Z

0

transformers

[ "transformers", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "arxiv:2405.16406", "license:llama3.2", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-08-18T22:03:15Z

--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers license: llama3.2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version\ \ Release Date: September 25, 2024\n\n“Agreement” means the terms and conditions\ \ for use, reproduction, distribution and modification of the Llama Materials set\ \ forth herein.\n\n“Documentation” means the specifications, manuals and documentation\ \ accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview.\n\ \n“Licensee” or “you” means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf),\ \ of the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\n“Llama 3.2”\ \ means the foundational large language models and software and algorithms, including\ \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\ \ code, fine-tuning enabling code and other elements of the foregoing distributed\ \ by Meta at https://www.llama.com/llama-downloads.\n\n“Llama Materials” means,\ \ collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion\ \ thereof) made available under this Agreement.\n\n“Meta” or “we” means Meta Platforms\ \ Ireland Limited (if you are located in or, if you are an entity, your principal\ \ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if\ \ you are located outside of the EEA or Switzerland). \n\nBy clicking “I Accept”\ \ below or by using or distributing any portion or element of the Llama Materials,\ \ you agree to be bound by this Agreement.\n\n1. License Rights and Redistribution.\n\ a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable\ \ and royalty-free limited license under Meta’s intellectual property or other rights\ \ owned by Meta embodied in the Llama Materials to use, reproduce, distribute,\ \ copy, create derivative works of, and make modifications to the Llama Materials.\ \ \nb. Redistribution and Use. \ni. If you distribute or make available the Llama\ \ Materials (or any derivative works thereof), or a product or service (including\ \ another AI model) that contains any of them, you shall (A) provide a copy of this\ \ Agreement with any such Llama Materials; and (B) prominently display “Built with\ \ Llama” on a related website, user interface, blogpost, about page, or product\ \ documentation. If you use the Llama Materials or any outputs or results of the\ \ Llama Materials to create, train, fine tune, or otherwise improve an AI model,\ \ which is distributed or made available, you shall also include “Llama” at the\ \ beginning of any such AI model name.\nii. If you receive Llama Materials, or any\ \ derivative works thereof, from a Licensee as part of an integrated end user product,\ \ then Section 2 of this Agreement will not apply to you. \niii. You must retain\ \ in all copies of the Llama Materials that you distribute the following attribution\ \ notice within a “Notice” text file distributed as a part of such copies: “Llama\ \ 3.2 is licensed under the Llama 3.2 Community License, Copyright © Meta Platforms,\ \ Inc. All Rights Reserved.”\niv. Your use of the Llama Materials must comply with\ \ applicable laws and regulations (including trade compliance laws and regulations)\ \ and adhere to the Acceptable Use Policy for the Llama Materials (available at\ \ https://www.llama.com/llama3_2/use-policy), which is hereby incorporated by reference\ \ into this Agreement.\n \n2. Additional Commercial Terms. If, on the Llama 3.2\ \ version release date, the monthly active users of the products or services made\ \ available by or for Licensee, or Licensee’s affiliates, is greater than 700 million\ \ monthly active users in the preceding calendar month, you must request a license\ \ from Meta, which Meta may grant to you in its sole discretion, and you are not\ \ authorized to exercise any of the rights under this Agreement unless or until\ \ Meta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS\ \ REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM\ \ ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS\ \ ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION,\ \ ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR\ \ PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING\ \ OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR\ \ USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability.\ \ IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY,\ \ WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING\ \ OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL,\ \ INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE\ \ BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\n\ a. No trademark licenses are granted under this Agreement, and in connection with\ \ the Llama Materials, neither Meta nor Licensee may use any name or mark owned\ \ by or associated with the other or any of its affiliates, except as required\ \ for reasonable and customary use in describing and redistributing the Llama Materials\ \ or as set forth in this Section 5(a). Meta hereby grants you a license to use\ \ “Llama” (the “Mark”) solely as required to comply with the last sentence of Section\ \ 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at\ \ https://about.meta.com/brand/resources/meta/company-brand/). All goodwill arising\ \ out of your use of the Mark will inure to the benefit of Meta.\nb. Subject to\ \ Meta’s ownership of Llama Materials and derivatives made by or for Meta, with\ \ respect to any derivative works and modifications of the Llama Materials that\ \ are made by you, as between you and Meta, you are and will be the owner of such\ \ derivative works and modifications.\nc. If you institute litigation or other proceedings\ \ against Meta or any entity (including a cross-claim or counterclaim in a lawsuit)\ \ alleging that the Llama Materials or Llama 3.2 outputs or results, or any portion\ \ of any of the foregoing, constitutes infringement of intellectual property or\ \ other rights owned or licensable by you, then any licenses granted to you under\ \ this Agreement shall terminate as of the date such litigation or claim is filed\ \ or instituted. You will indemnify and hold harmless Meta from and against any\ \ claim by any third party arising out of or related to your use or distribution\ \ of the Llama Materials.\n6. Term and Termination. The term of this Agreement will\ \ commence upon your acceptance of this Agreement or access to the Llama Materials\ \ and will continue in full force and effect until terminated in accordance with\ \ the terms and conditions herein. Meta may terminate this Agreement if you are\ \ in breach of any term or condition of this Agreement. Upon termination of this\ \ Agreement, you shall delete and cease use of the Llama Materials. Sections 3,\ \ 4 and 7 shall survive the termination of this Agreement. \n7. Governing Law and\ \ Jurisdiction. This Agreement will be governed and construed under the laws of\ \ the State of California without regard to choice of law principles, and the UN\ \ Convention on Contracts for the International Sale of Goods does not apply to\ \ this Agreement. The courts of California shall have exclusive jurisdiction of\ \ any dispute arising out of this Agreement. \n### Llama 3.2 Acceptable Use Policy\n\ Meta is committed to promoting safe and fair use of its tools and features, including\ \ Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy\ \ (“**Policy**”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\n\ #### Prohibited Uses\nWe want everyone to use Llama 3.2 safely and responsibly.\ \ You agree you will not use, or allow others to use, Llama 3.2 to:\n1. Violate\ \ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\ \ contribute to, encourage, plan, incite, or further illegal or unlawful activity\ \ or content, such as:\n 1. Violence or terrorism\n 2. Exploitation\ \ or harm to children, including the solicitation, creation, acquisition, or dissemination\ \ of child exploitative content or failure to report Child Sexual Abuse Material\n\ \ 3. Human trafficking, exploitation, and sexual violence\n 4. The\ \ illegal distribution of information or materials to minors, including obscene\ \ materials, or failure to employ legally required age-gating in connection with\ \ such information or materials.\n 5. Sexual solicitation\n 6. Any\ \ other criminal activity\n 1. Engage in, promote, incite, or facilitate the\ \ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\ \ 2. Engage in, promote, incite, or facilitate discrimination or other unlawful\ \ or harmful conduct in the provision of employment, employment benefits, credit,\ \ housing, other economic benefits, or other essential goods and services\n 3.\ \ Engage in the unauthorized or unlicensed practice of any profession including,\ \ but not limited to, financial, legal, medical/health, or related professional\ \ practices\n 4. Collect, process, disclose, generate, or infer private or sensitive\ \ information about individuals, including information about individuals’ identity,\ \ health, or demographic information, unless you have obtained the right to do so\ \ in accordance with applicable law\n 5. Engage in or facilitate any action or\ \ generate any content that infringes, misappropriates, or otherwise violates any\ \ third-party rights, including the outputs or results of any products or services\ \ using the Llama Materials\n 6. Create, generate, or facilitate the creation\ \ of malicious code, malware, computer viruses or do anything else that could disable,\ \ overburden, interfere with or impair the proper working, integrity, operation\ \ or appearance of a website or computer system\n 7. Engage in any action, or\ \ facilitate any action, to intentionally circumvent or remove usage restrictions\ \ or other safety measures, or to enable functionality disabled by Meta \n2. Engage\ \ in, promote, incite, facilitate, or assist in the planning or development of activities\ \ that present a risk of death or bodily harm to individuals, including use of Llama\ \ 3.2 related to the following:\n 8. Military, warfare, nuclear industries or\ \ applications, espionage, use for materials or activities that are subject to the\ \ International Traffic Arms Regulations (ITAR) maintained by the United States\ \ Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989\ \ or the Chemical Weapons Convention Implementation Act of 1997\n 9. Guns and\ \ illegal weapons (including weapon development)\n 10. Illegal drugs and regulated/controlled\ \ substances\n 11. Operation of critical infrastructure, transportation technologies,\ \ or heavy machinery\n 12. Self-harm or harm to others, including suicide, cutting,\ \ and eating disorders\n 13. Any content intended to incite or promote violence,\ \ abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive\ \ or mislead others, including use of Llama 3.2 related to the following:\n 14.\ \ Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n\ \ 15. Generating, promoting, or furthering defamatory content, including the\ \ creation of defamatory statements, images, or other content\n 16. Generating,\ \ promoting, or further distributing spam\n 17. Impersonating another individual\ \ without consent, authorization, or legal right\n 18. Representing that the\ \ use of Llama 3.2 or outputs are human-generated\n 19. Generating or facilitating\ \ false online engagement, including fake reviews and other means of fake online\ \ engagement \n4. Fail to appropriately disclose to end users any known dangers\ \ of your AI system 5. Interact with third party tools, models, or software designed\ \ to generate unlawful content or engage in unlawful or harmful conduct and/or represent\ \ that the outputs of such tools, models, or software are associated with Meta or\ \ Llama 3.2\n\nWith respect to any multimodal models included in Llama 3.2, the\ \ rights granted under Section 1(a) of the Llama 3.2 Community License Agreement\ \ are not being granted to you if you are an individual domiciled in, or a company\ \ with a principal place of business in, the European Union. This restriction does\ \ not apply to end users of a product or service that incorporates any such multimodal\ \ models.\n\nPlease report any violation of this Policy, software “bug,” or other\ \ problems that could lead to a violation of this Policy through one of the following\ \ means:\n\n* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\n\ * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\n\ * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\n\ * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama\ \ 3.2: LlamaUseReport@meta.com" extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location ? By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- # Llama-3.2-1B-RK3588-1.2.1 This version of Llama-3.2-1B has been converted to run on the RK3588 NPU using w8a8_g256 quantization. This model has been optimized with the following LoRA: Compatible with RKLLM version: 1.2.1 ## Useful links: [Official RKLLM GitHub](https://github.com/airockchip/rknn-llm) [RockhipNPU Reddit](https://reddit.com/r/RockchipNPU) [EZRKNN-LLM](https://github.com/Pelochus/ezrknn-llm/) Pretty much anything by these folks: [marty1885](https://github.com/marty1885) and [happyme531](https://huggingface.co/happyme531) Converted using https://github.com/c0zaut/ez-er-rkllm-toolkit # Original Model Card for base model, Llama-3.2-1B, below: ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. **Model Developer:** Meta **Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. | | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff | | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | | Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 | | | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | | | Llama 3.2 Quantized (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 8k | Yes | Yes | Up to 9T tokens | December 2023 | | | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | | **Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. **Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date:** Sept 25, 2024 **Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. **License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). **Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. **Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-1B, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function. Make sure to update your transformers installation via pip install --upgrade transformers. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-1B" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto" ) pipe("The key to life is") ``` ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-1B --include "original/*" --local-dir Llama-3.2-1B ``` ## Hardware and Software **Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. **Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. **Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. | | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) | | :---- | :---: | ----- | :---: | :---: | :---: | | Llama 3.2 1B | 370k | \- | 700 | 107 | 0 | | Llama 3.2 3B | 460k | \- | 700 | 133 | 0 | | Llama 3.2 1B SpinQuant | 1.7 | 0 | 700 | *Negligible*\*\* | 0 | | Llama 3.2 3B SpinQuant | 2.4 | 0 | 700 | *Negligible*\*\* | 0 | | Llama 3.2 1B QLora | 1.3k | 0 | 700 | 0.381 | 0 | | Llama 3.2 3B QLora | 1.6k | 0 | 700 | 0.461 | 0 | | Total | 833k | 86k | | 240 | 0 | \*\* The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data **Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). **Data Freshness:** The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models | Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B | | ----- | ----- | :---: | :---: | :---: | :---: | :---: | | General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 | | | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 | | | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 | | Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 | | | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 | | | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 | | Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 | ### Instruction Tuned Models | Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B bf16 | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B bf16 | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B | | :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | General | | MMLU | 5 | macro\_avg/acc | 49.3 | 43.3 | 47.3 | 49.0 | 63.4 | 60.5 | 62 | 62.4 | 69.4 | | Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 39.2 | 40.9 | 41.2 | 40.1 | 40.3 | 40.8 | 40.7 | 40.9 | | Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 14.9 | 16.7 | 16.8 | 19.0 | 19.1 | 19.2 | 19.1 | 17.2 | | Instruction following | | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 51.5 | 58.4 | 55.6 | 77.4 | 73.9 | 73.5 | 75.9 | 80.4 | | Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 33.1 | 40.6 | 46.5 | 77.7 | 72.9 | 75.7 | 77.9 | 84.5 | | | | MATH (CoT) | 0 | final\_em | 30.6 | 20.5 | 25.3 | 31.0 | 48.0 | 44.2 | 45.3 | 49.2 | 51.9 | | Reasoning | | ARC-C | 0 | acc | 59.4 | 54.3 | 57 | 60.7 | 78.6 | 75.6 | 77.6 | 77.6 | 83.4 | | | | GPQA | 0 | acc | 27.2 | 25.9 | 26.3 | 25.9 | 32.8 | 32.8 | 31.7 | 33.9 | 32.8 | | | | Hellaswag | 0 | acc | 41.2 | 38.1 | 41.3 | 41.5 | 69.8 | 66.3 | 68 | 66.3 | 78.7 | | Tool Use | | BFCL V2 | 0 | acc | 25.7 | 14.3 | 15.9 | 23.7 | 67.0 | 53.4 | 60.1 | 63.5 | 67.1 | | | | Nexus | 0 | macro\_avg/acc | 13.5 | 5.2 | 9.6 | 12.5 | 34.3 | 32.4 | 31.5 | 30.1 | 38.5 | | Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | N/A | N/A | N/A | 19.8 | N/A | N/A | N/A | 27.3 | | | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | N/A | N/A | N/A | 63.3 | N/A | N/A | N/A | 72.2 | | | | NIH/Multi-needle | 0 | recall | 75.0 | N/A | N/A | N/A | 84.7 | N/A | N/A | N/A | 98.8 | | Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 13.7 | 18.2 | 24.4 | 58.2 | 48.9 | 54.3 | 56.8 | 68.9 | \*\*for comparison purposes only. Model not released. ### Multilingual Benchmarks | Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | General | MMLU (5-shot, macro_avg/acc) | Portuguese | 39.8 | 34.9 | 38.9 | 40.2 | 54.5 | 50.9 | 53.3 | 53.4 | 62.1 | | | | Spanish | 41.5 | 36.0 | 39.8 | 41.8 | 55.1 | 51.9 | 53.6 | 53.6 | 62.5 | | | | Italian | 39.8 | 34.9 | 38.1 | 40.6 | 53.8 | 49.9 | 52.1 | 51.7 | 61.6 | | | | German | 39.2 | 34.9 | 37.5 | 39.6 | 53.3 | 50.0 | 52.2 | 51.3 | 60.6 | | | | French | 40.5 | 34.8 | 39.2 | 40.8 | 54.6 | 51.2 | 53.3 | 53.3 | 62.3 | | | | Hindi | 33.5 | 30.0 | 32.1 | 34.0 | 43.3 | 40.4 | 42.0 | 42.1 | 50.9 | | | | Thai | 34.7 | 31.2 | 32.4 | 34.9 | 44.5 | 41.3 | 44.0 | 42.2 | 50.3 | \*\*for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. | Category | Decode (tokens/sec) | Time-to-first-token (sec) | Prefill (tokens/sec) | Model size (PTE file size in MB) | Memory size (RSS in MB) | | :---- | ----- | ----- | ----- | ----- | ----- | | 1B BF16 (baseline) | 19.2 | 1.0 | 60.3 | 2358 | 3,185 | | 1B SpinQuant | 50.2 (2.6x) | 0.3 (-76.9%) | 260.5 (4.3x) | 1083 (-54.1%) | 1,921 (-39.7%) | | 1B QLoRA | 45.8 (2.4x) | 0.3 (-76.0%) | 252.0 (4.2x) | 1127 (-52.2%) | 2,255 (-29.2%) | | 3B BF16 (baseline) | 7.6 | 3.0 | 21.2 | 6129 | 7,419 | | 3B SpinQuant | 19.7 (2.6x) | 0.7 (-76.4%) | 89.7 (4.2x) | 2435 (-60.3%) | 3,726 (-49.8%) | | 3B QLoRA | 18.5 (2.4x) | 0.7 (-76.1%) | 88.8 (4.2x) | 2529 (-58.7%) | 4,060 (-45.3%) | (\*) The performance measurement is done using an adb binary-based approach. (\*\*) It is measured on an Android OnePlus 12 device. (\*\*\*) Time-to-first-token (TTFT) is measured with prompt length=64 *Footnote:* - *Decode (tokens/second) is for how quickly it keeps generating. Higher is better.* - *Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better.* - *Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better* - *Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch* - *RSS size \- Memory usage in resident set size (RSS)* ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment **Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct **Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). **Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. **Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems **Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases **Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. **Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations **Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. **Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: **1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. **2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. **3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community **Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). **Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). **Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations **Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. **Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.

KS190/diffusion_pick_0816

KS190

2025-08-18T22:40:31Z

0

lerobot

[ "lerobot", "safetensors", "diffusion", "robotics", "dataset:KS190/pick_0816", "arxiv:2303.04137", "license:apache-2.0", "region:us" ]

robotics

2025-08-18T22:39:30Z

--- datasets: KS190/pick_0816 library_name: lerobot license: apache-2.0 model_name: diffusion pipeline_tag: robotics tags: - diffusion - robotics - lerobot --- # Model Card for diffusion  [Diffusion Policy](https://huggingface.co/papers/2303.04137) treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash lerobot-record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - **License:** apache-2.0

lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755555300

lisaozill03

2025-08-18T22:39:58Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:39:55Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

emre0005/blockassist-bc-humming_winged_okapi_1755556696

emre0005

2025-08-18T22:39:06Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "humming winged okapi", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:39:02Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - humming winged okapi --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755556578

IvanJAjebu

2025-08-18T22:38:02Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:37:37Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

crystalline7/1445316

crystalline7

2025-08-18T22:37:39Z

0

null

[ "region:us" ]

null

2025-08-18T22:37:32Z

[View on Civ Archive](https://civarchive.com/models/1146289?modelVersionId=1545549)

crystalline7/357893

crystalline7

2025-08-18T22:37:13Z

0

null

[ "region:us" ]

null

2025-08-18T22:37:10Z

[View on Civ Archive](https://civarchive.com/models/388958?modelVersionId=437409)

AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755556574

AminuPeril

2025-08-18T22:36:46Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:36:39Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

crystalline7/1274312

crystalline7

2025-08-18T22:36:07Z

0

null

[ "region:us" ]

null

2025-08-18T22:36:04Z

[View on Civ Archive](https://civarchive.com/models/1217433?modelVersionId=1371374)

crystalline7/1520088

crystalline7

2025-08-18T22:35:53Z

0

null

[ "region:us" ]

null

2025-08-18T22:35:50Z

[View on Civ Archive](https://civarchive.com/models/1433264?modelVersionId=1620124)

ultratopaz/1374056

ultratopaz

2025-08-18T22:35:25Z

0

null

[ "region:us" ]

null

2025-08-18T22:35:21Z

[View on Civ Archive](https://civarchive.com/models/1305038?modelVersionId=1472649)

ultratopaz/1204501

ultratopaz

2025-08-18T22:35:10Z

0

null

[ "region:us" ]

null

2025-08-18T22:35:07Z

[View on Civ Archive](https://civarchive.com/models/1156124?modelVersionId=1300266)

ultratopaz/1301225

ultratopaz

2025-08-18T22:34:28Z

0

null

[ "region:us" ]

null

2025-08-18T22:34:25Z

[View on Civ Archive](https://civarchive.com/models/1241196?modelVersionId=1398885)

crystalline7/1480092

crystalline7

2025-08-18T22:34:00Z

0

null

[ "region:us" ]

null

2025-08-18T22:33:57Z

[View on Civ Archive](https://civarchive.com/models/1398112?modelVersionId=1580334)

smirki/UIGEN-X-4B-SFT-QLoRA-epoch-2.0

smirki

2025-08-18T22:33:58Z

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-18T22:33:54Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

crystalline7/1399367

crystalline7

2025-08-18T22:33:53Z

0

null

[ "region:us" ]

null

2025-08-18T22:33:50Z

[View on Civ Archive](https://civarchive.com/models/1328064?modelVersionId=1499540)

crystalline7/1488590

crystalline7

2025-08-18T22:33:29Z

0

null

[ "region:us" ]

null

2025-08-18T22:33:21Z

[View on Civ Archive](https://civarchive.com/models/1279990?modelVersionId=1588804)

AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755556372

AminuPeril

2025-08-18T22:33:25Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:33:18Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Yukiyoke-Lab/TAR-model

Yukiyoke-Lab

2025-08-18T22:33:22Z

0

null

[ "onnx", "text-to-speech", "ja", "dataset:Yukiyoke-Lab/Tsukuyomi-chan_datasets", "base_model:Yukiyoke-Lab/Tsukuyomi-chan", "base_model:quantized:Yukiyoke-Lab/Tsukuyomi-chan", "license:other", "region:us" ]

text-to-speech

2025-08-16T08:49:44Z

--- language: - ja pipeline_tag: text-to-speech datasets: - Yukiyoke-Lab/Tsukuyomi-chan_datasets license: other base_model: - Yukiyoke-Lab/Tsukuyomi-chan --- インストール方法は下に記載しています。ライセンスに同意し、よくREADMEを読んだ上でダウンロード・インストールを行ってください。 # モデルについて本モデルは、「つくよみちゃん」、その他の音源を学習・マージして作成されています。 **「つくよみちゃん」公式が提供している合成音声モデルではありません。** つくよみちゃん以外の合成音声モデルは公開しておりません。 ハイパーパラメータ、マージ時のパラメータ等やエポック数等、一部情報を削除しています。 つくよみちゃんモデルに関しては、[別リポジトリ](https://huggingface.co/Yukiyoke-Lab/Tsukuyomi-chan)を参照してください。 ONNX形式ファイルは、Safetensors形式ファイルを変換して作成しています。 変換には、[litagin02/Style-Bert-VITS2/convert_bert_onnx.py](https://github.com/litagin02/Style-Bert-VITS2/blob/dev/convert_bert_onnx.py)を利用しています。 <details> <summary>デモ音声</summary> ## デモ音声 Aivisspeechにて生成した無調整音声です。 音声の調整はパラメータ調整を含め、行っておりません。 <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/668a2a06aafa84bf3c3f1797/gttoi1AYXDwC4TGc4h7ZI.wav"></audio> ``` やったー！テストで満点取れた！私とっても嬉しいな！どうして私の意見を無視するの？許せない！ムカつく！あんたなんか死ねばいいのに。あはははっ！この漫画めっちゃ笑える、見てよこれ、ふふふ、あはは。あなたがいなくなって、私は一人になっちゃって、泣いちゃいそうなほど悲しい。 Aivisspeechバージョン：Aivisspeech 1.1.0-preview.2 モデルバージョン：1.0.0 ``` <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/668a2a06aafa84bf3c3f1797/yEDIC7ppt16svu9tCkTV5.wav"></audio> ``` 音声合成は、機械学習を活用して、テキストから人の声を再現する技術です。この技術は、言語の構造を解析し、それに基づいて音声を生成します。この分野の最新の研究成果を使うと、より自然で表現豊かな音声の生成が可能である。深層学習の応用により、感情やアクセントを含む声質の微妙な変化も再現することが出来る。 Aivisspeechバージョン：Aivisspeech 1.1.0-preview.2 モデルバージョン：1.0.0 ``` </details> ## Aivisspeech対応ファイル Aivisspeech形式ファイルは、拡張子が```.aivm``` ```.aivmx```のものとします。 Aivisspeech形式ファイルは、以下の通りにファイル名を命名する。 ```TAR-v1.0.0.aivmx``` ```TAR-v1.0.0.aivm``` ```モデル名```-v```バージョン``` ```拡張子``` --- ## バージョニングセマンティックバージョニングに基づいてバージョニングをし、音声モデルの形式にかかわらず、すべてでバージョンを統一します。 > Aivisspeech・Style-Birt-Vits2対応ファイルその他に関わらず、アップデートした場合に、共通して同一バージョンを割り振ります。バージョンアップの基準は、マージ前モデルの追加学習等で音声の音質が変化した場合、マイナーバージョンをアップします。メジャーバージョンは、メタデータ編集等により互換性が無くなった場合にアップします。 > Aivisspeechでは、ハイパーパラメータ・UUIDを変更すると同一モデル扱いではなくなるため。 --- # ライセンス・クレジット表記本リポジトリ・モデルは```Aivis Common Model License (ACML) - Non Commercial 1.0```でライセンスされています。 ## 共通ライセンスすべての他のライセンスよりも共通ライセンスが優先されます。 ACMLライセンスよりも、共通ライセンスが優先されます。本モデルの音声合成には、フリー素材キャラクター「つくよみちゃん」（© Rei Yumesaki）が無料公開している音声データを一部使用し、そこから作成されたマージモデルです。 **「つくよみちゃん」公式が提供している合成音声モデルではありません。** ■つくよみちゃんコーパス（CV.夢前黎） https://tyc.rei-yumesaki.net/material/corpus/ ■つくよみちゃんキャラクターライセンス https://tyc.rei-yumesaki.net/about/terms/ ■つくよみちゃん公式サイト https://tyc.rei-yumesaki.net/ - つくよみちゃんコーパスに由来する部分の取り扱いについては「つくよみちゃんコーパス」、「つくよみちゃんキャラクターライセンス」の利用規約に従うこととします。 ### 独自ライセンス独自ライセンスは、共通ライセンスと同一として解釈されます。 - このライセンスの全ては修正・変更、それに限らずを告知せずとも行うことができます。 - ライセンスは記載が無い限り改定の翌日から効力を持ちます。 - ライセンスの改変がされても改変以前に一般に公開されたコンテンツに関しては、以前のライセンスがそのまま適用されます。 - このライセンスはすべて日本語で提供され、日本語のみにより解釈されるものとします。 - クレジット表記は原則行う必要があります。 - つくよみちゃんキャラクターライセンスと異なり、ニコニコ動画、Youtubeそれに限らずクレジット表記を原則行う必要があります。 - クレジット表記の表記方法は自由ですが、つくよみちゃんのクレジット表記、マージモデルの作成者・配布先のクレジット表記を目立つ箇所に記載してください。 - クレジット表記が困難な場合、行わないことも可能です。 > 詳しくは、こちらを参考にしてください。ただし、共通・独自ライセンスも遵守する必要があります。https://tyc.rei-yumesaki.net/about/terms/credit/ ``` 音声合成には、フリー素材キャラクター「つくよみちゃん」（© Rei Yumesaki）が無料公開している音声データを一部使用し、akikukeo氏によって利用規約に基づき作成されたマージモデルを使用しています。「つくよみちゃん」公式が提供している合成音声モデルではありません。利用規約に基づき制作されています。つくよみちゃんの言動は、投稿者の言動が反映されたものであり、公式のつくよみちゃんとは一切関係ありません。 ■つくよみちゃんコーパス（CV.夢前黎） https://tyc.rei-yumesaki.net/material/corpus/ ■つくよみちゃんキャラクターライセンス https://tyc.rei-yumesaki.net/about/terms/ ■つくよみちゃん公式サイト https://tyc.rei-yumesaki.net/ 「つくよみちゃんコーパスライセンス」「つくよみちゃんキャラクターライセンス」以外にも規約が適用されます。ご注意ください。詳しくは以下マージモデル配布先をご覧ください。 ■モデル配布先（マージモデル） https://huggingface.co/Yukiyoke-Lab/TAR-model © Rei Yumesaki, YukiyokeLab, Akikukeo ``` --- Aivis Common Model License (ACML) - Non Commercial 1.0 https://github.com/Aivis-Project/ACML/blob/master/ACML-NC-1.0.md より引用 <details> <summary>Aivis Common Model License (ACML) - Non Commercial 1.0</summary> # Aivis Common Model License (ACML) - Non Commercial 1.0 このライセンスは、AI 音声合成モデルの利用条件と制限を定めるものです。音声合成技術の発展により、誰もが簡単に高品質な合成音声を作れるようになりました。この技術は、創作活動はもちろん、AI との対話や新しいサービスの開発など、さまざまな可能性を広げる革新的な手段として注目されています。現在、音声合成モデルのライセンスは制作者ごとにまちまちで、利用条件も曖昧なことが多く、使いづらい状況が続いています。 Aivis Project が策定する ACML は、音声合成モデルの自由な利用を促進しながら、制作者と利用者の双方にとって安心して活用できる環境を実現することを目指しています。また、多くの制作者に共通のライセンスとして採用していただくことで、モデルごとの規約に気を遣うことなく、誰もが安心して音声合成モデルを利用できる環境づくりを目指しています。このライセンスは、あなたに以下の権利を許諾します。 - ✅ **この音声合成モデルの利用・複製・改変・派生物の作成** - 音声合成モデルを実行して音声を生成することはもちろん、モデルの複製や改変、派生モデルの作成も自由に行えます。 - ✅ **この音声合成モデルやその派生物の配布** - このライセンスの条件に従う限り、改変の有無を問わず、自由に再配布することができます。ただし、これらの権利は「できないこと（禁止事項）」に定める制限に従うものとします。 ## 用語の定義 - **話者:** 話し手 (Speaker) のことをいいます。話者には「実在人物」の声と「キャラクター」の声の両方を含みます。 - **音声合成モデル:** 話者の声を AI に学習させることで制作された、テキストからその話者に近い合成音声を生成できる、重み (Weight) やパラメータのことを指します。 - **あなた:** このライセンスによって許可された行為を行う個人・法人のことをいいます。 - **このライセンス:** このライセンス自身 (Aivis Common Model License: ACML) のことをいいます。 - **この音声合成モデル:** このライセンスにより利用が許諾される音声合成モデルのことをいいます。 - **利用:** この音声合成モデルの実行、複製、改変、配布、組み込み、その他あらゆる利用形態を含みます。 - **派生物:** この音声合成モデルを基に作成された二次的著作物、改変物、または派生物を指します。 ## できないこと（禁止事項） - ❌ **音声合成モデルの元となった話者や無関係な他者の「本人」「原作者」「公式関係者」であるとの誤解を招く/騙すような利用** - 生成した音声を、ディープフェイクや公式なコンテンツだと誤解されるような形で公開しないでください。 - 「※非公式です」「本人とは一切関係ありません」と明記するなど、なるべく公式なものだと誤解されないよう注意を払ってください。 - ❌ **話者のイメージ・尊厳・品位・社会的評価を「傷つける」「価値を下げる」「貶める」ような利用** - 第三者が『この声がこんな用途に使われるのは嫌だ』と感じるような使い方をしないでください。 - 話者がキャラクターのときは、そのキャラクターが登場する作品に対しても同様に適用されます。 - 具体的には、下記の禁止事項が該当します。 - ❌ **実在する人物・団体・商品などを「批判」「攻撃」「嫌がらせ」「誹謗中傷」「差別」する活動への利用** - 「攻撃」とは、自他を傷つけるあらゆる行為（犯罪、戦争、暴行、自傷・自殺、薬物乱用、ヘイトスピーチ、誹謗中傷、侮辱、揶揄、アンチ活動、詐欺、クラッキング、その他あらゆる身体的・精神的・社会的加害を含むがこれらに限定されない）を、実行または扇動することをいいます。 - あなたの主張が正当であるかどうかにかかわらず、批判や攻撃を目的とした活動にはお使いいただけません。 - ❌ **人々を騙す目的で虚偽の情報やコンテンツを公開・流布する活動への利用** - フェイクニュースの作成や誤情報を広める目的ではお使いいただけません。 - ❌ **虚偽または誇大な表現によるマーケティングや倫理的に問題のあるビジネスへの利用** - 消費者の誤解を狙った悪質なマーケティング活動・倫理的に問題のある事業活動（虚偽広告、誇大広告、高額情報商材、マルチ商法、ねずみ講を含むがこれらに限定されない）やその宣伝にはお使いいただけません。 - ❌ **特定の政治的立場・政治団体・政治家・宗教団体・宗教家・排他的思想・社会的勢力・陰謀論への賛同・支援または反対・批判・非難を呼びかける活動（権利運動・署名運動・デモ・プロパガンダを含むがこれらに限定されない）への利用** - 「陰謀論」とは、科学的根拠や事実に基づかない主張（ワクチン、通信技術、医療、健康、社会問題などに関する誤った情報を含むがこれらに限定されない）のことをいいます。 - あなたの主張が正当であるかどうかにかかわらず、特定の政治・宗教・排他的思想・社会的勢力・陰謀論に関する主張を広める活動にはお使いいただけません。 - ❌ **反社会的・犯罪目的での利用** - 話者へのなりすましによるオレオレ詐欺や、反社会的勢力による利用などを含みます。 - ❌ **営利目的での利用** - 以下のような、営利を目的としない使い方でのみお使いいただけます。 - 個人による私的な創作活動 - 学校や大学など教育機関における教育・研究目的での利用 - その他、営利を目的としない活動での利用 ## できること - ✅ **上記「できないこと（禁止事項）」に該当しない、すべての非営利利用** - 禁止事項に該当しない使い方であれば、どのような用途にも自由にお使いいただけます。 - その際、下記の「なるべく守ってほしいこと」を尊重した利用をお願いします。 - クレジット表記は任意です。音声合成モデルの制作者や話者のクレジット表記を行うかどうかは、あなたの判断にお任せします。 - この音声合成モデルを他の人に配布・共有する場合は、必ずこのライセンス文書も一緒に添付してください。 - ℹ️ **この音声合成モデルを組み込んだアプリ・Web サービスを不特定多数に公開する場合:** - 不特定多数のユーザー or AI (LLM) が任意のテキストを入力して音声合成できる状況において、このライセンスをユーザーや LLM に完璧に遵守させることは、技術的・現実的に極めて困難であると考えられます。 - このため特例として、（技術的に禁止事項に該当する利用を防げる状態かに関わらず）**「アプリ・Web サービスの開発元自身がこのライセンスを遵守し、現実的な範囲でなるべく禁止事項に該当する利用が起きないよう努めていれば」お使いいただけます。** ## なるべく守ってほしいこと - ℹ️ **話者をリスペクト・尊重した利用をしてほしい** - 具体的にどのような使い方が「リスペクト」となるかは、あなたの良識にお任せします。 - 話者がキャラクターのときは、そのキャラクターが登場する作品も十分にリスペクトしてください。 - キャラクター愛・作品愛のある使い方をお願いします。 - ℹ️ **刺激の強い/万人向けでない表現を公開するときは、「見たくない人・見るべきでない人の目に入らない」よう十分配慮してほしい** - 表現に合わせた適切なゾーニングをお願いします。 - ゾーニングの例:「年齢制限を掛ける」「SNS のセンシティブ設定をオンにする」「コンテンツの前に注意書きを設置する」 - ℹ️ **常識の範囲内で、良識ある利用をしてほしい** - 上記はあくまで任意であり、遵守されていなくてもライセンス違反にはなりません。 ## 免責事項 - この音声合成モデルは「現状のまま」提供されており、商品性や特定の目的への適合性、権利の非侵害などについて、明示的または黙示的を問わず、いかなる保証もありません。 - 音声合成モデルの制作者は、この音声合成モデルの利用や取り扱いまたはその結果に関連して、契約や不法行為など、いかなる形の請求や損害賠償、その他の責任についても、一切の責任を負いません。あなた自身の責任においてお使いください。 - このライセンスのいずれかの条項が無効または執行不能と判断された場合でも、残りの条項は完全に有効に存続するものとします。 - このライセンスのいずれかの条項の権利行使を行わなかったとしても、それは当該条項または他の条項の権利放棄とはみなされません。 - このライセンスはすべて日本語で提供され、日本語のみにより解釈されるものとします。上記の免責事項は、適用される法令の下で許容される最大限の範囲で適用されるものとします。 </details> --- ## インストール方法インストール方法についてのサポートは行っておりません。 **Aivisspeech版を推奨しています。** ### Aivisspeech ```Aivisspeech 1.1.0-preview.2```にて動作確認しています。手動でダウンロードしてAivisspeechにインストール頂くか、以下URLを「設定」「音声合成モデルの管理」「URLからインストール」に入力してください。 ```https://huggingface.co/Yukiyoke-Lab/TAR-model/resolve/main/TAR-v1.0.2.aivmx?download=true``` ### SBV2 必要ファイルをダウンロード頂き、指定ディレクトリへと配置してください。 --- ## ライセンス改定ライセンスは記載が無い限り改定の翌日から効力を持ちます。 日本時間 2025年8月17日第2号 日本時間 2025年8月17日第3号 日本時間 2025年8月17日第4号 日本時間 2025年8月17日第5号

crystalline7/1622588

crystalline7

2025-08-18T22:32:50Z

0

null

[ "region:us" ]

null

2025-08-18T22:32:43Z

[View on Civ Archive](https://civarchive.com/models/1131118?modelVersionId=1722074)

crystalline7/1560430

crystalline7

2025-08-18T22:32:26Z

0

null

[ "region:us" ]

null

2025-08-18T22:32:26Z

[View on Civ Archive](https://civarchive.com/models/1467698?modelVersionId=1659988)

crystalline7/1425654

crystalline7

2025-08-18T22:32:15Z

0

null

[ "region:us" ]

null

2025-08-18T22:32:13Z

[View on Civ Archive](https://civarchive.com/models/1350620?modelVersionId=1525575)

seraphimzzzz/1179513

seraphimzzzz

2025-08-18T22:32:08Z

0

null

[ "region:us" ]

null

2025-08-18T22:32:05Z

[View on Civ Archive](https://civarchive.com/models/1132879?modelVersionId=1274669)

ultratopaz/294585

ultratopaz

2025-08-18T22:31:56Z

0

null

[ "region:us" ]

null

2025-08-18T22:31:53Z

[View on Civ Archive](https://civarchive.com/models/328548?modelVersionId=368183)

fatmhd1995/phi35_ft_llm_4_annotation_rnd3_without_hpt

fatmhd1995

2025-08-18T22:31:51Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-08-18T22:28:58Z

--- base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** fatmhd1995 - **License:** apache-2.0 - **Finetuned from model :** unsloth/phi-3.5-mini-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

aliangdw/rfm_v3.1

aliangdw

2025-08-18T22:31:37Z

0

transformers

[ "transformers", "safetensors", "qwen2_5_vl", "reward-model", "rfm", "vision-language", "multimodal", "base_model:Qwen/Qwen2.5-VL-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2025-08-18T22:24:28Z

--- license: apache-2.0 base_model: Qwen/Qwen2.5-VL-3B-Instruct tags: - reward-model - rfm - vision-language - multimodal library_name: transformers --- # aliangdw/rfm_v3.1 This is a Reward Function Model (RFM) for vision-language preference learning and similarity assessment. ## Model Details - **Base Model**: Qwen/Qwen2.5-VL-3B-Instruct - **Model Type**: qwen2_5_vl - **Architecture**: RFMModel - **Task**: Vision-Language Reward Modeling - **Training Method**: FSDP (Fully Sharded Data Parallel) ## Usage ```python from transformers import AutoProcessor, AutoModel import torch # Load model and processor processor = AutoProcessor.from_pretrained("aliangdw/rfm_v3.1", trust_remote_code=True) model = AutoModel.from_pretrained("aliangdw/rfm_v3.1", trust_remote_code=True) # Example usage for preference scoring # inputs = processor(images=images, text=text, return_tensors="pt") # outputs = model(**inputs, sample_type="preference") ``` ## Model Capabilities This RFM model can perform: 1. **Preference Prediction**: Given two trajectories A and B, predict which one is preferred 2. **Similarity Assessment**: Evaluate how similar a trajectory is to a reference 3. **Progress Estimation**: Estimate task completion progress ## Training The model was trained using: - FSDP for distributed training - Mixed precision (bfloat16) - Custom loss functions for preference and similarity learning ## Files This repository contains: - Model weights in SafeTensors format - Configuration files - Tokenizer/Processor files ## Citation If you use this model, please cite:

ultratopaz/1348382

ultratopaz

2025-08-18T22:31:35Z

0

null

[ "region:us" ]

null

2025-08-18T22:31:33Z

[View on Civ Archive](https://civarchive.com/models/1282728?modelVersionId=1447202)

seraphimzzzz/1497204

seraphimzzzz

2025-08-18T22:31:28Z

0

null

[ "region:us" ]

null

2025-08-18T22:31:25Z

[View on Civ Archive](https://civarchive.com/models/1412810?modelVersionId=1597175)

ultratopaz/1245990

ultratopaz

2025-08-18T22:31:12Z

0

null

[ "region:us" ]

null

2025-08-18T22:31:10Z

[View on Civ Archive](https://civarchive.com/models/1192275?modelVersionId=1342384)

seraphimzzzz/1474526

seraphimzzzz

2025-08-18T22:31:05Z

0

null

[ "region:us" ]

null

2025-08-18T22:31:02Z

[View on Civ Archive](https://civarchive.com/models/1393239?modelVersionId=1574735)

seraphimzzzz/1209032

seraphimzzzz

2025-08-18T22:30:35Z

0

null

[ "region:us" ]

null

2025-08-18T22:30:33Z

[View on Civ Archive](https://civarchive.com/models/1159581?modelVersionId=1304854)

AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755556199

AminuPeril

2025-08-18T22:30:30Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:30:23Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

seraphimzzzz/1435841

seraphimzzzz

2025-08-18T22:30:29Z

0

null

[ "region:us" ]

null

2025-08-18T22:30:26Z

[View on Civ Archive](https://civarchive.com/models/1359635?modelVersionId=1535908)

seraphimzzzz/1332514

seraphimzzzz

2025-08-18T22:30:22Z

0

null

[ "region:us" ]

null

2025-08-18T22:30:20Z

[View on Civ Archive](https://civarchive.com/models/1268989?modelVersionId=1431376)

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755556140

IvanJAjebu

2025-08-18T22:30:19Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:30:11Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

kimono998/Wordle-curr-neg-3_lora_adapter_iter_15

kimono998

2025-08-18T22:30:16Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-18T22:30:11Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Guilherme34/Maya-lora

Guilherme34

2025-08-18T22:30:02Z

0

peft

[ "peft", "safetensors", "base_model:adapter:unsloth/orpheus-3b-0.1-ft", "lora", "transformers", "unsloth", "text-generation", "arxiv:1910.09700", "base_model:unsloth/orpheus-3b-0.1-ft", "region:us" ]

text-generation

2025-08-18T22:29:25Z

--- base_model: unsloth/orpheus-3b-0.1-ft library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:unsloth/orpheus-3b-0.1-ft - lora - transformers - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.17.0

seraphimzzzz/1232441

seraphimzzzz

2025-08-18T22:29:42Z

0

null

[ "region:us" ]

null

2025-08-18T22:29:39Z

[View on Civ Archive](https://civarchive.com/models/1180572?modelVersionId=1328557)

seraphimzzzz/1391154

seraphimzzzz

2025-08-18T22:29:35Z

0

null

[ "region:us" ]

null

2025-08-18T22:29:32Z

[View on Civ Archive](https://civarchive.com/models/1320686?modelVersionId=1491055)

crystalline7/128713

crystalline7

2025-08-18T22:29:29Z

0

null

[ "region:us" ]

null

2025-08-18T22:29:27Z

[View on Civ Archive](https://civarchive.com/models/151523?modelVersionId=169426)

crystalline7/1465942

crystalline7

2025-08-18T22:29:23Z

0

null

[ "region:us" ]

null

2025-08-18T22:29:20Z

[View on Civ Archive](https://civarchive.com/models/1385916?modelVersionId=1566155)

ultratopaz/1488216

ultratopaz

2025-08-18T22:29:11Z

0

null

[ "region:us" ]

null

2025-08-18T22:29:03Z

[View on Civ Archive](https://civarchive.com/models/1295945?modelVersionId=1588426)

mradermacher/Cydonia-24B-v4.1-GGUF

mradermacher

2025-08-18T22:28:48Z

0

1

transformers

[ "transformers", "gguf", "en", "base_model:TheDrummer/Cydonia-24B-v4.1", "base_model:quantized:TheDrummer/Cydonia-24B-v4.1", "endpoints_compatible", "region:us", "conversational" ]

null

2025-08-18T17:45:22Z

--- base_model: TheDrummer/Cydonia-24B-v4.1 language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About         static quants of https://huggingface.co/TheDrummer/Cydonia-24B-v4.1  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Cydonia-24B-v4.1-GGUF).*** weighted/imatrix quants are available at https://huggingface.co/mradermacher/Cydonia-24B-v4.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q2_K.gguf) | Q2_K | 9.0 | | | [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q3_K_S.gguf) | Q3_K_S | 10.5 | | | [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q3_K_M.gguf) | Q3_K_M | 11.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q3_K_L.gguf) | Q3_K_L | 12.5 | | | [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.IQ4_XS.gguf) | IQ4_XS | 13.0 | | | [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q4_K_S.gguf) | Q4_K_S | 13.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q4_K_M.gguf) | Q4_K_M | 14.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q5_K_S.gguf) | Q5_K_S | 16.4 | | | [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q5_K_M.gguf) | Q5_K_M | 16.9 | | | [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q6_K.gguf) | Q6_K | 19.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Cydonia-24B-v4.1-GGUF/resolve/main/Cydonia-24B-v4.1.Q8_0.gguf) | Q8_0 | 25.2 | fast, best quality | 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.

lemonhat/Qwen2.5-Coder-7B-Instruct-swe_5k_v1_tag5mini

lemonhat

2025-08-18T22:28:00Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-Coder-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-7B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-18T22:26:34Z

--- library_name: transformers license: other base_model: Qwen/Qwen2.5-Coder-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: swe_5k_v1_tag5mini results: [] ---  # swe_5k_v1_tag5mini This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the swe_5k_v1_tag5mini dataset. It achieves the following results on the evaluation set: - Loss: 0.4714 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 4 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.6295 | 0.3650 | 100 | 0.4949 | | 0.6747 | 0.7299 | 200 | 0.4726 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

hohotok/blockassist-bc-dormant_rapid_salmon_1755555414

hohotok

2025-08-18T22:27:21Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant rapid salmon", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:27:13Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant rapid salmon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

Dejiat/blockassist-bc-savage_unseen_bobcat_1755555921

Dejiat

2025-08-18T22:26:02Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:25:58Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

JinghuiLuAstronaut/smolvla_spatial

JinghuiLuAstronaut

2025-08-18T22:25:22Z

2

0

lerobot

[ "lerobot", "safetensors", "robotics", "smolvla", "dataset:IPEC-COMMUNITY/libero_spatial_no_noops_1.0.0_lerobot", "arxiv:2506.01844", "base_model:lerobot/smolvla_base", "base_model:finetune:lerobot/smolvla_base", "license:apache-2.0", "region:us" ]

robotics

2025-08-09T23:17:25Z

--- base_model: lerobot/smolvla_base datasets: IPEC-COMMUNITY/libero_spatial_no_noops_1.0.0_lerobot library_name: lerobot license: apache-2.0 model_name: smolvla pipeline_tag: robotics tags: - lerobot - robotics - smolvla --- # Model Card for smolvla  [SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - **License:** apache-2.0

AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755555793

AminuPeril

2025-08-18T22:23:46Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:23:39Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF

mradermacher

2025-08-18T22:23:34Z

16

0

transformers

[ "transformers", "gguf", "en", "base_model:hlwei/qwen-2.5-0.5b-r1-Sudoku", "base_model:quantized:hlwei/qwen-2.5-0.5b-r1-Sudoku", "endpoints_compatible", "region:us", "conversational" ]

null

2025-02-09T15:24:38Z

--- base_model: hlwei/qwen-2.5-0.5b-r1-Sudoku language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/hlwei/qwen-2.5-0.5b-r1-Sudoku  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#qwen-2.5-0.5b-r1-Sudoku-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/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q3_K_S.gguf) | Q3_K_S | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q2_K.gguf) | Q2_K | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.IQ4_XS.gguf) | IQ4_XS | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q3_K_L.gguf) | Q3_K_L | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q5_K_S.gguf) | Q5_K_S | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q5_K_M.gguf) | Q5_K_M | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q6_K.gguf) | Q6_K | 0.6 | very good quality | | [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.Q8_0.gguf) | Q8_0 | 0.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/qwen-2.5-0.5b-r1-Sudoku-GGUF/resolve/main/qwen-2.5-0.5b-r1-Sudoku.f16.gguf) | f16 | 1.1 | 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.

efficient-nlp/stt-1b-en_fr-quantized

efficient-nlp

2025-08-18T22:23:05Z

0

moshi

[ "moshi", "gguf", "stt", "audio", "automatic-speech-recognition", "en", "fr", "license:cc-by-4.0", "region:us" ]

automatic-speech-recognition

2025-08-18T22:09:11Z

--- license: cc-by-4.0 language: - en - fr library_name: moshi tags: - audio - automatic-speech-recognition --- # Moshi Streaming Speech-to-Text (Quantized) This is a quantized version of Kyutai’s [stt-1b-en_fr](https://huggingface.co/kyutai/stt-1b-en_fr) model. The original model is a 1B parameter streaming speech-to-text model for English and French. This fork contains the same model, quantized to Q8_0 and Q4_K GGUF formats for reduced memory usage and faster inference.

kohya-ss/misc-models

kohya-ss

2025-08-18T22:22:09Z

0

31

null

[ "region:us" ]

null

2023-05-11T23:38:36Z

## qwenimage-blob_emoji-4-s020-6.safetensors Blob emoji LoRA. The training captions are like `Yellow blob emoji with smiling face with smiling eyes. The background is gray.`, so `blob emoji` or `blob emoji with face ...` etc. act as trigger words. - Blob emoji with face holds a sign says "Blob Emoji" in front of Japanese Shrine. --w 1024 --h 1024 --s 50 --d 1001 ![sample1](yellow_blob_1.png) - Blob emoji face drives a red sport car along a curved road on a cliff overlooking the sea. The sea is dotted with whitecaps. The sky is blue, and cumulonimbus clouds float on the horizon. --w 1664 --h 928 --s 50 --d 12345678 ![sample2](yellow_blob_2.png) ### Dataset Creation Procedure The dataset was created following these steps: - The SVG files from [C1710/blobmoji](https://github.com/C1710/blobmoji) (licensed under ASL 2.0) were used. Specifically, 118 different yellow blob emojis were selected from the SVG files. - `cairosvg` was used to convert these SVGs into 512x512 pixel transparent PNGs. - A script was then used to pad the images to 640x640 pixels and generate four versions of each image with different background colors: white, light gray, gray, and black. This resulted in a total of 472 images. - The captions were generated based on the official Unicode names of the emojis. The prefix `Yellow blob emoji with ` and the suffix `. The background is <color>.` were added to each name. - For example: `Yellow blob emoji with smiling face with smiling eyes. The background is gray.` - Note: For some emojis (e.g., devil, zombie), the word `Yellow` was omitted from the prefix. ### Dataset Definition ``` # general configurations [general] resolution = [640, 640] batch_size = 16 enable_bucket = true bucket_no_upscale = false caption_extension = ".txt" [[datasets]] image_directory = "path/to/images_and_captions_dir" cache_directory = "path/to/cache_dir" ``` ### Training Command ``` accelerate launch --num_cpu_threads_per_process 1 --mixed_precision bf16 --rdzv_backend=c10d \ src/musubi_tuner/qwen_image_train_network.py \ --dit path/to/dit.safetensors --vae path/to/vae.safetensors \ --text_encoder path/to/vlm.safetensors \ --dataset_config path/to/blob_emoji_v1_640_bs16.toml \ --output_dir path/to/output_dir \ --learning_rate 2e-4 \ --timestep_sampling shift --weighting_scheme none --discrete_flow_shift 2.0 \ --max_train_epochs 16 --mixed_precision bf16 --seed 42 --gradient_checkpointing \ --network_module=networks.lora_qwen_image \ --network_dim=4 --network_args loraplus_lr_ratio=4 \ --save_every_n_epochs=1 --max_data_loader_n_workers 2 \ --persistent_data_loader_workers \ --logging_dir ./logs --log_prefix qwenimage-blob4-2e4- \ --output_name qwenimage-blob4-2e4 \ --optimizer_type adamw8bit --flash_attn --split_attn \ --log_with tensorboard \ --sample_every_n_epochs 1 --sample_prompts path/to/prompts_qwen_blob_emoji.txt \ --fp8_base --fp8_scaled ``` ### Training Details - Training was conducted on a Windows machine with a multi-GPU setup (2x RTX A6000). - If you are not using a Windows environment or not performing multi-GPU training, please remove the `--rdzv_backend=c10d` argument. - Please note that due to the 2-GPU setup, the effective batch size is 32. To achieve the same results with limited VRAM, increase the gradient accumulation steps. However, you should be able to train successfully with a lower batch size by adjusting the learning rate. - The model was trained for 6 epochs (90 steps), which took approximately 1 hour with the Power Limit set to 60%. - Finally, the weights from all 6 epochs were merged using the LoRA Post-Hoc EMA script from Musubi Tuner with `sigma_rel=0.2`. ## fp-1f-kisekae-1024-v4-2-PfPHEMA.safetensors Post-Hoc EMA (with Power function sigma_rel=0.2) version of the following LoRA. The usage is the same. ## fp-1f-kisekae-1024-v4-2.safetensors Experimental LoRA for FramePack One Frame kisekaeichi. The target index is 5. The prompt is as follows: ``` The girl stays in the same pose, but her outfit changes into a <costume description>, then she changes into another girl wearing the same outfit. ``` `costume description` is something like `school uniform` etc. A detailed description may improve the results. For example: "T-shirt with writing on it" or "Girl with long hair" This model is trained with 1024x1024 resolution. Please use at roughly the same resolution. ## fp-1f-chibi-1024.safetensors Experimental LoRA for FramePack One Frame Inference. The target index is 9. The prompt is as follows: ``` An anime character transforms: her head grows larger, her body becomes shorter and smaller, eyes become bigger and cuter. She turns into a chibi (super-deformed) version, with cartoonishly cute proportions. The transformation is quick and playful. ``` This model is trained with 1024x1024 resolution. Please use at roughly the same resolution. If the effect is too strong, lower the multiplier (strength) to 0.8 or less. ## FramePack-dance-lora-d8.safetensors Experimental LoRA for FramePack. This is for testing purposes and the effect is weak. Please set the prompt to something like `A woman is spinning on her tiptoes` . `. ## flux-hasui-lora-d4-sigmoid-raw-gs1.0.safetensors Experimental LoRA for FLUX.1 dev. Trained with `sd-scripts` (Aug. 11) `sd3` branch. __NOTE:__ This settings requires > 26GB VRAM. Please add `--fp8_base` to enable fp8 training to reduce VRAM usage. ``` accelerate launch --mixed_precision bf16 --num_cpu_threads_per_process 1 flux_train_network.py --pretrained_model_name_or_path flux1/flux1-dev.sft --clip_l sd3/clip_l.safetensors --t5xxl sd3/t5xxl_fp16.safetensors --ae flux1/ae_dev.sft --cache_latents_to_disk --save_model_as safetensors --sdpa --persistent_data_loader_workers --max_data_loader_n_workers 2 --seed 42 --gradient_checkpointing --mixed_precision bf16 --save_precision bf16 --network_module networks.lora_flux --network_dim 4 --optimizer_type adamw8bit --learning_rate 1e-3 --network_train_unet_only --cache_text_encoder_outputs --cache_text_encoder_outputs_to_disk --highvram --max_train_epochs 4 --save_every_n_epochs 1 --dataset_config hasui_1024_bs1.toml --output_dir flux/lora --output_name lora-name --timestep_sampling sigmoid --model_prediction_type raw --guidance_scale 1.0 ``` .toml is below. ```.toml [general] flip_aug = true color_aug = false [[datasets]] enable_bucket = true resolution = [1024,1024] bucket_reso_steps = 64 max_bucket_reso = 2048 min_bucket_reso = 128 bucket_no_upscale = false batch_size = 1 random_crop = false shuffle_caption = false [[datasets.subsets]] image_dir = "path/to/train/images" num_repeats = 1 caption_extension = ".txt" ``` ## sdxl-negprompt8-v1m.safetensors Negative embeddings for sdxl. Num vectors per token = 8 ## stable-cascade-c-lora-hasui-v02.safetensors Sample of LoRA for Stable Cascade Stage C. Feb 22, 2024 Update: Fixed a bug that LoRA is not applied to some modules (to_q/k/v and to_out) in Attention. __This is an experimental model, so the format of the weights may change in the future.__ - a painting of an anthropomorphic penguin sitting in a cafe reading a book and having a coffee --w 1024 --h 1024 --d 1 ![sample1](penguin.png) - a painting of japanese shrine in winter with snowfall --w 832 --h 1152 --d 1234 ![sample2](shrine.png) This model is trained with 169 images with captions. U-Net only, dim=4, conv_dim=4, alpha=1, lr=1e-3, 4 epochs, mixed precision bf16, 8bit AdamW, batch size 8, resolution 1024x1024 with aspect ratio bucketing. VRAM usage is approximately 22 GB.

AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755555588

AminuPeril

2025-08-18T22:20:21Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:20:13Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

vjkhambe/dqn-SpaceInvadersNoFrameskip-v4

vjkhambe

2025-08-18T22:20:12Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2025-08-18T21:15:46Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 250.50 +/- 147.45 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga vjkhambe -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga vjkhambe -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga vjkhambe ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 1e-05), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

AminuPeril/blockassist-bc-ravenous_leggy_caribou_1755555192

AminuPeril

2025-08-18T22:13:48Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "ravenous leggy caribou", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:13:41Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - ravenous leggy caribou --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755553635

vwzyrraz7l

2025-08-18T22:13:42Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:13:38Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tall hunting vulture --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

haihp02/aa465003-5d10-46d8-a8e0-d20b7380fe9b

haihp02

2025-08-18T22:12:58Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-08-18T22:12:47Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

NexVeridian/OpenReasoning-Nemotron-14B-6bit

NexVeridian

2025-08-18T22:12:40Z

0

mlx

[ "mlx", "safetensors", "qwen2", "nvidia", "code", "text-generation", "conversational", "en", "base_model:nvidia/OpenReasoning-Nemotron-14B", "base_model:quantized:nvidia/OpenReasoning-Nemotron-14B", "license:cc-by-4.0", "6-bit", "region:us" ]

text-generation

2025-08-18T22:06:54Z

--- license: cc-by-4.0 language: - en base_model: nvidia/OpenReasoning-Nemotron-14B pipeline_tag: text-generation library_name: mlx tags: - nvidia - code - mlx --- # NexVeridian/OpenReasoning-Nemotron-14B-6bit This model [NexVeridian/OpenReasoning-Nemotron-14B-6bit](https://huggingface.co/NexVeridian/OpenReasoning-Nemotron-14B-6bit) was converted to MLX format from [nvidia/OpenReasoning-Nemotron-14B](https://huggingface.co/nvidia/OpenReasoning-Nemotron-14B) using mlx-lm version **0.26.3**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("NexVeridian/OpenReasoning-Nemotron-14B-6bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755554998

IvanJAjebu

2025-08-18T22:11:46Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:11:23Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755553607

lisaozill03

2025-08-18T22:11:37Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:11:34Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

SharpAI/yolo11n-onnx-cpu-ort

SharpAI

2025-08-18T22:10:49Z

0

ultralytics

[ "ultralytics", "onnx", "yolo", "object-detection", "computer-vision", "unknown", "aegis-ai", "license:agpl-3.0", "region:us" ]

object-detection

2025-08-18T21:42:17Z

--- title: YOLO Model tags: - yolo - object-detection - computer-vision - unknown - aegis-ai library_name: ultralytics license: agpl-3.0 --- # YOLO Model This model has been converted and optimized using the **Aegis AI Model Conversion Tool**. ## Model Details - **Original Model**: Unknown - **Format**: UNKNOWN - **Task**: Object Detection - **Framework**: Ultralytics YOLO - **License**: AGPL-3.0 ## Performance Metrics | Metric | Value | |--------|--------| | Average FPS | N/A | | Inference Time | N/A ms | | Memory Usage | N/A MB | | Target Hardware | cpu | ## Hardware Information - **Platform**: Unknown - **Device**: cpu - **Optimization**: Hardware-specific optimizations applied ## Usage ### Loading the Model ```python # For ONNX models import onnxruntime as ort session = ort.InferenceSession("model.onnx") # For PyTorch models from ultralytics import YOLO model = YOLO("model.pt") # For TensorRT models (NVIDIA GPU) # Requires TensorRT runtime model = YOLO("model.engine") ``` ### Inference ```python import numpy as np from PIL import Image # Load your image image = Image.open("path/to/image.jpg") # Run inference results = model(image) # Process results for result in results: boxes = result.boxes # Bounding boxes classes = result.names # Class names ``` ## Conversion Details This model was converted using the Aegis AI Model Conversion Tool with the following configuration: - **Precision**: fp32 - **Optimization Level**: standard - **Hardware Target**: cpu - **Conversion Date**: 2025-08-18 15:10:46 ## Model Architecture Based on the YOLO (You Only Look Once) architecture, this model provides real-time object detection capabilities with optimized performance for the target hardware. ### Input - **Shape**: 640x640 - **Format**: RGB images - **Normalization**: [0-1] range ### Output - **Bounding Boxes**: Object locations - **Confidence Scores**: Detection confidence - **Class Predictions**: Object categories ## Benchmarking The model has been benchmarked on the target hardware with the following results: ```json {} ``` ## Hardware Compatibility This model has been optimized for: - **Primary**: cpu - **Platform**: Unknown For other hardware configurations, consider using the Aegis AI Model Conversion Tool to create optimized versions. ## Citation If you use this model in your research or project, please cite: ```bibtex @misc{aegis-ai-converted-model, title={Aegis AI Converted YOLO Model}, author={Aegis AI Team}, year={2025}, howpublished={\url{https://github.com/aegis-ai/model-conversion-tool}} } ``` ## Related Models - [Original YOLO Models](https://github.com/ultralytics/ultralytics) - [Aegis AI Model Zoo](https://huggingface.co/aegis-ai) ## Support For issues with this converted model or the conversion tool: - [GitHub Issues](https://github.com/aegis-ai/model-conversion-tool/issues) - [Aegis AI Documentation](https://docs.aegis-ai.com) --- *This model was automatically converted and uploaded by the Aegis AI Model Conversion Tool.*

donoway/ARC-Challenge_Llama-3.2-1B-zfgomj43

donoway

2025-08-18T22:09:49Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-18T21:58:26Z

--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: ARC-Challenge_Llama-3.2-1B-zfgomj43 results: [] ---  # ARC-Challenge_Llama-3.2-1B-zfgomj43 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5531 - Model Preparation Time: 0.0057 - Mdl: 669.9700 - Accumulated Loss: 464.3878 - Correct Preds: 132.0 - Total Preds: 299.0 - Accuracy: 0.4415 - Correct Gen Preds: 114.0 - Gen Accuracy: 0.3813 - Correct Gen Preds 32: 12.0 - Correct Preds 32: 20.0 - Total Labels 32: 64.0 - Accuracy 32: 0.3125 - Gen Accuracy 32: 0.1875 - Correct Gen Preds 33: 23.0 - Correct Preds 33: 24.0 - Total Labels 33: 73.0 - Accuracy 33: 0.3288 - Gen Accuracy 33: 0.3151 - Correct Gen Preds 34: 42.0 - Correct Preds 34: 47.0 - Total Labels 34: 78.0 - Accuracy 34: 0.6026 - Gen Accuracy 34: 0.5385 - Correct Gen Preds 35: 37.0 - Correct Preds 35: 41.0 - Total Labels 35: 83.0 - Accuracy 35: 0.4940 - Gen Accuracy 35: 0.4458 - Correct Gen Preds 36: 0.0 - Correct Preds 36: 0.0 - Total Labels 36: 1.0 - Accuracy 36: 0.0 - Gen Accuracy 36: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 112 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 32 | Correct Preds 32 | Total Labels 32 | Accuracy 32 | Gen Accuracy 32 | Correct Gen Preds 33 | Correct Preds 33 | Total Labels 33 | Accuracy 33 | Gen Accuracy 33 | Correct Gen Preds 34 | Correct Preds 34 | Total Labels 34 | Accuracy 34 | Gen Accuracy 34 | Correct Gen Preds 35 | Correct Preds 35 | Total Labels 35 | Accuracy 35 | Gen Accuracy 35 | Correct Gen Preds 36 | Correct Preds 36 | Total Labels 36 | Accuracy 36 | Gen Accuracy 36 | |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:| | No log | 0 | 0 | 1.6389 | 0.0057 | 706.9523 | 490.0220 | 66.0 | 299.0 | 0.2207 | 66.0 | 0.2207 | 62.0 | 62.0 | 64.0 | 0.9688 | 0.9688 | 0.0 | 0.0 | 73.0 | 0.0 | 0.0 | 4.0 | 4.0 | 78.0 | 0.0513 | 0.0513 | 0.0 | 0.0 | 83.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 1.4865 | 1.0 | 6 | 1.4608 | 0.0057 | 630.1492 | 436.7861 | 79.0 | 299.0 | 0.2642 | 77.0 | 0.2575 | 56.0 | 57.0 | 64.0 | 0.8906 | 0.875 | 11.0 | 12.0 | 73.0 | 0.1644 | 0.1507 | 1.0 | 1.0 | 78.0 | 0.0128 | 0.0128 | 9.0 | 9.0 | 83.0 | 0.1084 | 0.1084 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 1.0972 | 2.0 | 12 | 1.4324 | 0.0057 | 617.8780 | 428.2804 | 120.0 | 299.0 | 0.4013 | 119.0 | 0.3980 | 36.0 | 36.0 | 64.0 | 0.5625 | 0.5625 | 24.0 | 24.0 | 73.0 | 0.3288 | 0.3288 | 25.0 | 25.0 | 78.0 | 0.3205 | 0.3205 | 34.0 | 35.0 | 83.0 | 0.4217 | 0.4096 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.4836 | 3.0 | 18 | 1.5531 | 0.0057 | 669.9700 | 464.3878 | 132.0 | 299.0 | 0.4415 | 114.0 | 0.3813 | 12.0 | 20.0 | 64.0 | 0.3125 | 0.1875 | 23.0 | 24.0 | 73.0 | 0.3288 | 0.3151 | 42.0 | 47.0 | 78.0 | 0.6026 | 0.5385 | 37.0 | 41.0 | 83.0 | 0.4940 | 0.4458 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.1277 | 4.0 | 24 | 2.8384 | 0.0057 | 1224.4029 | 848.6914 | 125.0 | 299.0 | 0.4181 | 116.0 | 0.3880 | 18.0 | 23.0 | 64.0 | 0.3594 | 0.2812 | 28.0 | 30.0 | 73.0 | 0.4110 | 0.3836 | 35.0 | 35.0 | 78.0 | 0.4487 | 0.4487 | 35.0 | 37.0 | 83.0 | 0.4458 | 0.4217 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0009 | 5.0 | 30 | 4.5527 | 0.0057 | 1963.8919 | 1361.2661 | 121.0 | 299.0 | 0.4047 | 112.0 | 0.3746 | 19.0 | 24.0 | 64.0 | 0.375 | 0.2969 | 32.0 | 34.0 | 73.0 | 0.4658 | 0.4384 | 35.0 | 35.0 | 78.0 | 0.4487 | 0.4487 | 26.0 | 28.0 | 83.0 | 0.3373 | 0.3133 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0005 | 6.0 | 36 | 7.0651 | 0.0057 | 3047.6390 | 2112.4624 | 120.0 | 299.0 | 0.4013 | 115.0 | 0.3846 | 25.0 | 29.0 | 64.0 | 0.4531 | 0.3906 | 33.0 | 33.0 | 73.0 | 0.4521 | 0.4521 | 32.0 | 32.0 | 78.0 | 0.4103 | 0.4103 | 25.0 | 26.0 | 83.0 | 0.3133 | 0.3012 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 7.0 | 42 | 7.6423 | 0.0057 | 3296.6319 | 2285.0511 | 127.0 | 299.0 | 0.4247 | 118.0 | 0.3946 | 26.0 | 29.0 | 64.0 | 0.4531 | 0.4062 | 34.0 | 34.0 | 73.0 | 0.4658 | 0.4658 | 28.0 | 31.0 | 78.0 | 0.3974 | 0.3590 | 30.0 | 33.0 | 83.0 | 0.3976 | 0.3614 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 8.0 | 48 | 7.8574 | 0.0057 | 3389.4254 | 2349.3707 | 127.0 | 299.0 | 0.4247 | 117.0 | 0.3913 | 19.0 | 23.0 | 64.0 | 0.3594 | 0.2969 | 34.0 | 36.0 | 73.0 | 0.4932 | 0.4658 | 30.0 | 31.0 | 78.0 | 0.3974 | 0.3846 | 34.0 | 36.0 | 83.0 | 0.4337 | 0.4096 | 0.0 | 1.0 | 1.0 | 1.0 | 0.0 | | 0.0 | 9.0 | 54 | 7.5145 | 0.0057 | 3241.4943 | 2246.8326 | 126.0 | 299.0 | 0.4214 | 111.0 | 0.3712 | 18.0 | 25.0 | 64.0 | 0.3906 | 0.2812 | 36.0 | 38.0 | 73.0 | 0.5205 | 0.4932 | 28.0 | 30.0 | 78.0 | 0.3846 | 0.3590 | 29.0 | 32.0 | 83.0 | 0.3855 | 0.3494 | 0.0 | 1.0 | 1.0 | 1.0 | 0.0 | | 0.0 | 10.0 | 60 | 7.0254 | 0.0057 | 3030.5166 | 2100.5941 | 127.0 | 299.0 | 0.4247 | 98.0 | 0.3278 | 14.0 | 30.0 | 64.0 | 0.4688 | 0.2188 | 32.0 | 37.0 | 73.0 | 0.5068 | 0.4384 | 28.0 | 33.0 | 78.0 | 0.4231 | 0.3590 | 24.0 | 27.0 | 83.0 | 0.3253 | 0.2892 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 11.0 | 66 | 7.2467 | 0.0057 | 3125.9883 | 2166.7700 | 127.0 | 299.0 | 0.4247 | 113.0 | 0.3779 | 17.0 | 26.0 | 64.0 | 0.4062 | 0.2656 | 37.0 | 39.0 | 73.0 | 0.5342 | 0.5068 | 34.0 | 36.0 | 78.0 | 0.4615 | 0.4359 | 25.0 | 26.0 | 83.0 | 0.3133 | 0.3012 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 12.0 | 72 | 7.4089 | 0.0057 | 3195.9474 | 2215.2619 | 123.0 | 299.0 | 0.4114 | 116.0 | 0.3880 | 20.0 | 24.0 | 64.0 | 0.375 | 0.3125 | 35.0 | 37.0 | 73.0 | 0.5068 | 0.4795 | 33.0 | 34.0 | 78.0 | 0.4359 | 0.4231 | 28.0 | 28.0 | 83.0 | 0.3373 | 0.3373 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 13.0 | 78 | 7.2529 | 0.0057 | 3128.6606 | 2168.6223 | 126.0 | 299.0 | 0.4214 | 121.0 | 0.4047 | 21.0 | 24.0 | 64.0 | 0.375 | 0.3281 | 37.0 | 38.0 | 73.0 | 0.5205 | 0.5068 | 37.0 | 38.0 | 78.0 | 0.4872 | 0.4744 | 26.0 | 26.0 | 83.0 | 0.3133 | 0.3133 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 14.0 | 84 | 7.2595 | 0.0057 | 3131.5101 | 2170.5974 | 127.0 | 299.0 | 0.4247 | 120.0 | 0.4013 | 21.0 | 24.0 | 64.0 | 0.375 | 0.3281 | 38.0 | 40.0 | 73.0 | 0.5479 | 0.5205 | 35.0 | 37.0 | 78.0 | 0.4744 | 0.4487 | 26.0 | 26.0 | 83.0 | 0.3133 | 0.3133 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 15.0 | 90 | 7.2704 | 0.0057 | 3136.2190 | 2173.8614 | 125.0 | 299.0 | 0.4181 | 118.0 | 0.3946 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 37.0 | 39.0 | 73.0 | 0.5342 | 0.5068 | 35.0 | 37.0 | 78.0 | 0.4744 | 0.4487 | 27.0 | 27.0 | 83.0 | 0.3253 | 0.3253 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 16.0 | 96 | 7.2516 | 0.0057 | 3128.0812 | 2168.2206 | 124.0 | 299.0 | 0.4147 | 118.0 | 0.3946 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 37.0 | 38.0 | 73.0 | 0.5205 | 0.5068 | 35.0 | 36.0 | 78.0 | 0.4615 | 0.4487 | 27.0 | 28.0 | 83.0 | 0.3373 | 0.3253 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 17.0 | 102 | 7.2158 | 0.0057 | 3112.6486 | 2157.5236 | 123.0 | 299.0 | 0.4114 | 118.0 | 0.3946 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 37.0 | 37.0 | 73.0 | 0.5068 | 0.5068 | 35.0 | 37.0 | 78.0 | 0.4744 | 0.4487 | 27.0 | 27.0 | 83.0 | 0.3253 | 0.3253 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 18.0 | 108 | 7.2719 | 0.0057 | 3136.8490 | 2174.2980 | 124.0 | 299.0 | 0.4147 | 117.0 | 0.3913 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 36.0 | 37.0 | 73.0 | 0.5068 | 0.4932 | 35.0 | 37.0 | 78.0 | 0.4744 | 0.4487 | 27.0 | 28.0 | 83.0 | 0.3373 | 0.3253 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 19.0 | 114 | 7.3003 | 0.0057 | 3149.1089 | 2182.7960 | 123.0 | 299.0 | 0.4114 | 118.0 | 0.3946 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 37.0 | 37.0 | 73.0 | 0.5068 | 0.5068 | 35.0 | 37.0 | 78.0 | 0.4744 | 0.4487 | 27.0 | 27.0 | 83.0 | 0.3253 | 0.3253 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 20.0 | 120 | 7.2762 | 0.0057 | 3138.7201 | 2175.5950 | 125.0 | 299.0 | 0.4181 | 118.0 | 0.3946 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 37.0 | 38.0 | 73.0 | 0.5205 | 0.5068 | 35.0 | 37.0 | 78.0 | 0.4744 | 0.4487 | 27.0 | 28.0 | 83.0 | 0.3373 | 0.3253 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 21.0 | 126 | 7.2258 | 0.0057 | 3116.9751 | 2160.5225 | 123.0 | 299.0 | 0.4114 | 119.0 | 0.3980 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 37.0 | 38.0 | 73.0 | 0.5205 | 0.5068 | 35.0 | 35.0 | 78.0 | 0.4487 | 0.4487 | 28.0 | 28.0 | 83.0 | 0.3373 | 0.3373 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 22.0 | 132 | 7.2761 | 0.0057 | 3138.6438 | 2175.5421 | 123.0 | 299.0 | 0.4114 | 118.0 | 0.3946 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 37.0 | 38.0 | 73.0 | 0.5205 | 0.5068 | 35.0 | 36.0 | 78.0 | 0.4615 | 0.4487 | 27.0 | 27.0 | 83.0 | 0.3253 | 0.3253 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 23.0 | 138 | 7.2421 | 0.0057 | 3123.9891 | 2165.3842 | 125.0 | 299.0 | 0.4181 | 119.0 | 0.3980 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 38.0 | 39.0 | 73.0 | 0.5342 | 0.5205 | 35.0 | 36.0 | 78.0 | 0.4615 | 0.4487 | 27.0 | 28.0 | 83.0 | 0.3373 | 0.3253 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 24.0 | 144 | 7.2574 | 0.0057 | 3130.5871 | 2169.9576 | 125.0 | 299.0 | 0.4181 | 118.0 | 0.3946 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 36.0 | 38.0 | 73.0 | 0.5205 | 0.4932 | 35.0 | 37.0 | 78.0 | 0.4744 | 0.4487 | 28.0 | 28.0 | 83.0 | 0.3373 | 0.3373 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 25.0 | 150 | 7.2338 | 0.0057 | 3120.4105 | 2162.9037 | 123.0 | 299.0 | 0.4114 | 117.0 | 0.3913 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 36.0 | 38.0 | 73.0 | 0.5205 | 0.4932 | 34.0 | 35.0 | 78.0 | 0.4487 | 0.4359 | 28.0 | 28.0 | 83.0 | 0.3373 | 0.3373 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 26.0 | 156 | 7.2551 | 0.0057 | 3129.6043 | 2169.2764 | 123.0 | 299.0 | 0.4114 | 118.0 | 0.3946 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 37.0 | 38.0 | 73.0 | 0.5205 | 0.5068 | 35.0 | 36.0 | 78.0 | 0.4615 | 0.4487 | 27.0 | 27.0 | 83.0 | 0.3253 | 0.3253 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 27.0 | 162 | 7.2376 | 0.0057 | 3122.0548 | 2164.0435 | 125.0 | 299.0 | 0.4181 | 117.0 | 0.3913 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 36.0 | 38.0 | 73.0 | 0.5205 | 0.4932 | 35.0 | 37.0 | 78.0 | 0.4744 | 0.4487 | 27.0 | 28.0 | 83.0 | 0.3373 | 0.3253 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 28.0 | 168 | 7.2457 | 0.0057 | 3125.5609 | 2166.4737 | 125.0 | 299.0 | 0.4181 | 118.0 | 0.3946 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 36.0 | 38.0 | 73.0 | 0.5205 | 0.4932 | 35.0 | 37.0 | 78.0 | 0.4744 | 0.4487 | 28.0 | 28.0 | 83.0 | 0.3373 | 0.3373 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 29.0 | 174 | 7.2451 | 0.0057 | 3125.2939 | 2166.2886 | 125.0 | 299.0 | 0.4181 | 119.0 | 0.3980 | 20.0 | 24.0 | 64.0 | 0.375 | 0.3125 | 37.0 | 37.0 | 73.0 | 0.5068 | 0.5068 | 35.0 | 37.0 | 78.0 | 0.4744 | 0.4487 | 27.0 | 27.0 | 83.0 | 0.3253 | 0.3253 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 30.0 | 180 | 7.2520 | 0.0057 | 3128.2546 | 2168.3409 | 124.0 | 299.0 | 0.4147 | 118.0 | 0.3946 | 20.0 | 23.0 | 64.0 | 0.3594 | 0.3125 | 36.0 | 37.0 | 73.0 | 0.5068 | 0.4932 | 35.0 | 36.0 | 78.0 | 0.4615 | 0.4487 | 27.0 | 28.0 | 83.0 | 0.3373 | 0.3253 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 31.0 | 186 | 7.2720 | 0.0057 | 3136.8961 | 2174.3307 | 124.0 | 299.0 | 0.4147 | 117.0 | 0.3913 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 37.0 | 38.0 | 73.0 | 0.5205 | 0.5068 | 34.0 | 36.0 | 78.0 | 0.4615 | 0.4359 | 27.0 | 28.0 | 83.0 | 0.3373 | 0.3253 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 32.0 | 192 | 7.2555 | 0.0057 | 3129.7943 | 2169.4081 | 123.0 | 299.0 | 0.4114 | 117.0 | 0.3913 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 36.0 | 37.0 | 73.0 | 0.5068 | 0.4932 | 35.0 | 36.0 | 78.0 | 0.4615 | 0.4487 | 27.0 | 28.0 | 83.0 | 0.3373 | 0.3253 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | | 0.0 | 33.0 | 198 | 7.2496 | 0.0057 | 3127.2402 | 2167.6377 | 124.0 | 299.0 | 0.4147 | 119.0 | 0.3980 | 19.0 | 22.0 | 64.0 | 0.3438 | 0.2969 | 37.0 | 38.0 | 73.0 | 0.5205 | 0.5068 | 35.0 | 36.0 | 78.0 | 0.4615 | 0.4487 | 28.0 | 28.0 | 83.0 | 0.3373 | 0.3373 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

lmsys/gpt-oss-20b-bf16

lmsys

2025-08-18T22:09:15Z

15,646

5

null

[ "safetensors", "gpt_oss", "region:us" ]

null

2025-08-05T21:58:13Z

# gpt-oss-20b-bf16 ## Model Introduction This model is the bf16 version converted from [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b). ## Usage You can use this model in [SGLang](https://github.com/sgl-project/sglang) with the following instructions. ### Installation ``` # build from source git clone https://github.com/sgl-project/sglang cd sglang pip3 install pip --upgrade pip3 install -e "python[all]" # ROCm 6.3 pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/rocm6.3 git clone https://github.com/triton-lang/triton cd python/triton_kernels pip3 install . # hopper pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu126 pip3 install sgl-kernel==0.3.2 # blackwell cu128 pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu128 pip3 install https://github.com/sgl-project/whl/releases/download/v0.3.2/sgl_kernel-0.3.2+cu128-cp39-abi3-manylinux2014_x86_64.whl # blackwell cu129 pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu129 pip3 install https://github.com/sgl-project/whl/releases/download/v0.3.2/sgl_kernel-0.3.2-cp39-abi3-manylinux2014_x86_64.whl ``` ### Launch command ``` python3 -m sglang.launch_server --model lmsys/gpt-oss-20b-bf16 ``` ### For more details https://github.com/sgl-project/sglang/issues/8833

g-assismoraes/Qwen3-4B-Base-fpi-alpha1.6-var-hatebr-ep30-g5

g-assismoraes

2025-08-18T22:08:06Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-18T22:04:06Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

lmsys/gpt-oss-120b-bf16

lmsys

2025-08-18T22:07:44Z

10,337

2

null

[ "safetensors", "gpt_oss", "region:us" ]

null

2025-08-05T18:54:32Z

# gpt-oss-120b-bf16 ## Model Introduction This model is the bf16 version converted from [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b). ## Usage You can use this model in [SGLang](https://github.com/sgl-project/sglang) with the following instructions. ### Installation ``` # build from source git clone https://github.com/sgl-project/sglang cd sglang pip3 install pip --upgrade pip3 install -e "python[all]" # ROCm 6.3 pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/rocm6.3 git clone https://github.com/triton-lang/triton cd python/triton_kernels pip3 install . # hopper pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu126 pip3 install sgl-kernel==0.3.2 # blackwell cu128 pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu128 pip3 install https://github.com/sgl-project/whl/releases/download/v0.3.2/sgl_kernel-0.3.2+cu128-cp39-abi3-manylinux2014_x86_64.whl # blackwell cu129 pip3 install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu129 pip3 install https://github.com/sgl-project/whl/releases/download/v0.3.2/sgl_kernel-0.3.2-cp39-abi3-manylinux2014_x86_64.whl ``` ### Launch command ``` python3 -m sglang.launch_server --model lmsys/gpt-oss-120b-bf16 --tp 4 ``` ### For more details https://github.com/sgl-project/sglang/issues/8833

koloni/blockassist-bc-deadly_graceful_stingray_1755553249

koloni

2025-08-18T22:07:23Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:07:20Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

donoway/BoolQ_Llama-3.2-1B-n5s6b4x8

donoway

2025-08-18T22:05:41Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-18T20:27:42Z

--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer metrics: - accuracy model-index: - name: BoolQ_Llama-3.2-1B-n5s6b4x8 results: [] ---  # BoolQ_Llama-3.2-1B-n5s6b4x8 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4568 - Model Preparation Time: 0.0058 - Mdl: 6872.6248 - Accumulated Loss: 4763.7405 - Correct Preds: 2760.0 - Total Preds: 3270.0 - Accuracy: 0.8440 - Correct Gen Preds: 2757.0 - Gen Accuracy: 0.8431 - Correct Gen Preds 9642: 1814.0 - Correct Preds 9642: 1823.0 - Total Labels 9642: 2026.0 - Accuracy 9642: 0.8998 - Gen Accuracy 9642: 0.8954 - Correct Gen Preds 2822: 934.0 - Correct Preds 2822: 937.0 - Total Labels 2822: 1231.0 - Accuracy 2822: 0.7612 - Gen Accuracy 2822: 0.7587 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 120 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 9642 | Correct Preds 9642 | Total Labels 9642 | Accuracy 9642 | Gen Accuracy 9642 | Correct Gen Preds 2822 | Correct Preds 2822 | Total Labels 2822 | Accuracy 2822 | Gen Accuracy 2822 | |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:----------------------:|:------------------:|:-----------------:|:-------------:|:-----------------:|:----------------------:|:------------------:|:-----------------:|:-------------:|:-----------------:| | No log | 0 | 0 | 0.7080 | 0.0058 | 3339.8933 | 2315.0376 | 2032.0 | 3270.0 | 0.6214 | 2040.0 | 0.6239 | 2007.0 | 2008.0 | 2026.0 | 0.9911 | 0.9906 | 24.0 | 24.0 | 1231.0 | 0.0195 | 0.0195 | | 0.3324 | 1.0 | 232 | 0.4426 | 0.0058 | 2088.1525 | 1447.3970 | 2676.0 | 3270.0 | 0.8183 | 2679.0 | 0.8193 | 1808.0 | 1809.0 | 2026.0 | 0.8929 | 0.8924 | 862.0 | 867.0 | 1231.0 | 0.7043 | 0.7002 | | 0.4917 | 2.0 | 464 | 0.4887 | 0.0058 | 2305.5456 | 1598.0824 | 2697.0 | 3270.0 | 0.8248 | 2690.0 | 0.8226 | 1887.0 | 1893.0 | 2026.0 | 0.9344 | 0.9314 | 797.0 | 804.0 | 1231.0 | 0.6531 | 0.6474 | | 0.0028 | 3.0 | 696 | 0.7594 | 0.0058 | 3582.3567 | 2483.1004 | 2676.0 | 3270.0 | 0.8183 | 2659.0 | 0.8131 | 1641.0 | 1663.0 | 2026.0 | 0.8208 | 0.8100 | 1009.0 | 1013.0 | 1231.0 | 0.8229 | 0.8197 | | 0.0011 | 4.0 | 928 | 0.9795 | 0.0058 | 4620.9576 | 3203.0038 | 2731.0 | 3270.0 | 0.8352 | 2723.0 | 0.8327 | 1788.0 | 1799.0 | 2026.0 | 0.8880 | 0.8825 | 927.0 | 932.0 | 1231.0 | 0.7571 | 0.7530 | | 0.1389 | 5.0 | 1160 | 1.0611 | 0.0058 | 5005.9836 | 3469.8834 | 2739.0 | 3270.0 | 0.8376 | 2737.0 | 0.8370 | 1847.0 | 1853.0 | 2026.0 | 0.9146 | 0.9116 | 882.0 | 886.0 | 1231.0 | 0.7197 | 0.7165 | | 0.0002 | 6.0 | 1392 | 1.1056 | 0.0058 | 5215.9426 | 3615.4159 | 2749.0 | 3270.0 | 0.8407 | 2751.0 | 0.8413 | 1881.0 | 1885.0 | 2026.0 | 0.9304 | 0.9284 | 862.0 | 864.0 | 1231.0 | 0.7019 | 0.7002 | | 0.0001 | 7.0 | 1624 | 1.2332 | 0.0058 | 5817.6850 | 4032.5120 | 2754.0 | 3270.0 | 0.8422 | 2738.0 | 0.8373 | 1806.0 | 1824.0 | 2026.0 | 0.9003 | 0.8914 | 923.0 | 930.0 | 1231.0 | 0.7555 | 0.7498 | | 0.0 | 8.0 | 1856 | 1.2209 | 0.0058 | 5759.8281 | 3992.4086 | 2754.0 | 3270.0 | 0.8422 | 2753.0 | 0.8419 | 1788.0 | 1798.0 | 2026.0 | 0.8875 | 0.8825 | 956.0 | 956.0 | 1231.0 | 0.7766 | 0.7766 | | 0.0 | 9.0 | 2088 | 1.4452 | 0.0058 | 6817.7274 | 4725.6885 | 2750.0 | 3270.0 | 0.8410 | 2746.0 | 0.8398 | 1815.0 | 1825.0 | 2026.0 | 0.9008 | 0.8959 | 922.0 | 925.0 | 1231.0 | 0.7514 | 0.7490 | | 0.0 | 10.0 | 2320 | 1.4119 | 0.0058 | 6660.5648 | 4616.7517 | 2752.0 | 3270.0 | 0.8416 | 2749.0 | 0.8407 | 1797.0 | 1807.0 | 2026.0 | 0.8919 | 0.8870 | 943.0 | 945.0 | 1231.0 | 0.7677 | 0.7660 | | 0.0 | 11.0 | 2552 | 1.4389 | 0.0058 | 6788.4022 | 4705.3618 | 2753.0 | 3270.0 | 0.8419 | 2751.0 | 0.8413 | 1813.0 | 1822.0 | 2026.0 | 0.8993 | 0.8949 | 929.0 | 931.0 | 1231.0 | 0.7563 | 0.7547 | | 0.0 | 12.0 | 2784 | 1.4300 | 0.0058 | 6746.3247 | 4676.1959 | 2755.0 | 3270.0 | 0.8425 | 2752.0 | 0.8416 | 1812.0 | 1821.0 | 2026.0 | 0.8988 | 0.8944 | 931.0 | 934.0 | 1231.0 | 0.7587 | 0.7563 | | 0.0 | 13.0 | 3016 | 1.4335 | 0.0058 | 6762.4940 | 4687.4036 | 2756.0 | 3270.0 | 0.8428 | 2750.0 | 0.8410 | 1806.0 | 1819.0 | 2026.0 | 0.8978 | 0.8914 | 935.0 | 937.0 | 1231.0 | 0.7612 | 0.7595 | | 0.0 | 14.0 | 3248 | 1.4568 | 0.0058 | 6872.6248 | 4763.7405 | 2760.0 | 3270.0 | 0.8440 | 2757.0 | 0.8431 | 1814.0 | 1823.0 | 2026.0 | 0.8998 | 0.8954 | 934.0 | 937.0 | 1231.0 | 0.7612 | 0.7587 | | 0.0 | 15.0 | 3480 | 1.4631 | 0.0058 | 6902.2813 | 4784.2968 | 2750.0 | 3270.0 | 0.8410 | 2739.0 | 0.8376 | 1792.0 | 1809.0 | 2026.0 | 0.8929 | 0.8845 | 938.0 | 941.0 | 1231.0 | 0.7644 | 0.7620 | | 0.0 | 16.0 | 3712 | 1.4765 | 0.0058 | 6965.4556 | 4828.0859 | 2754.0 | 3270.0 | 0.8422 | 2743.0 | 0.8388 | 1797.0 | 1814.0 | 2026.0 | 0.8954 | 0.8870 | 937.0 | 940.0 | 1231.0 | 0.7636 | 0.7612 | | 0.0 | 17.0 | 3944 | 1.4796 | 0.0058 | 6980.1585 | 4838.2772 | 2751.0 | 3270.0 | 0.8413 | 2745.0 | 0.8394 | 1799.0 | 1812.0 | 2026.0 | 0.8944 | 0.8880 | 937.0 | 939.0 | 1231.0 | 0.7628 | 0.7612 | | 0.0 | 18.0 | 4176 | 1.4793 | 0.0058 | 6978.7939 | 4837.3313 | 2755.0 | 3270.0 | 0.8425 | 2748.0 | 0.8404 | 1799.0 | 1813.0 | 2026.0 | 0.8949 | 0.8880 | 940.0 | 942.0 | 1231.0 | 0.7652 | 0.7636 | | 0.0 | 19.0 | 4408 | 1.4822 | 0.0058 | 6992.2377 | 4846.6498 | 2752.0 | 3270.0 | 0.8416 | 2742.0 | 0.8385 | 1798.0 | 1815.0 | 2026.0 | 0.8959 | 0.8875 | 935.0 | 937.0 | 1231.0 | 0.7612 | 0.7595 | | 0.0 | 20.0 | 4640 | 1.4798 | 0.0058 | 6980.8944 | 4838.7873 | 2753.0 | 3270.0 | 0.8419 | 2745.0 | 0.8394 | 1798.0 | 1812.0 | 2026.0 | 0.8944 | 0.8875 | 938.0 | 941.0 | 1231.0 | 0.7644 | 0.7620 | | 0.0 | 21.0 | 4872 | 1.4847 | 0.0058 | 7004.0401 | 4854.8307 | 2755.0 | 3270.0 | 0.8425 | 2748.0 | 0.8404 | 1801.0 | 1815.0 | 2026.0 | 0.8959 | 0.8889 | 938.0 | 940.0 | 1231.0 | 0.7636 | 0.7620 | | 0.0 | 22.0 | 5104 | 1.4801 | 0.0058 | 6982.3382 | 4839.7880 | 2754.0 | 3270.0 | 0.8422 | 2746.0 | 0.8398 | 1797.0 | 1812.0 | 2026.0 | 0.8944 | 0.8870 | 940.0 | 942.0 | 1231.0 | 0.7652 | 0.7636 | | 0.0 | 23.0 | 5336 | 1.4791 | 0.0058 | 6977.9730 | 4836.7623 | 2756.0 | 3270.0 | 0.8428 | 2747.0 | 0.8401 | 1801.0 | 1816.0 | 2026.0 | 0.8963 | 0.8889 | 937.0 | 940.0 | 1231.0 | 0.7636 | 0.7612 | | 0.0 | 24.0 | 5568 | 1.4821 | 0.0058 | 6991.9891 | 4846.4775 | 2751.0 | 3270.0 | 0.8413 | 2743.0 | 0.8388 | 1797.0 | 1812.0 | 2026.0 | 0.8944 | 0.8870 | 937.0 | 939.0 | 1231.0 | 0.7628 | 0.7612 | | 0.0 | 25.0 | 5800 | 1.4844 | 0.0058 | 7003.0013 | 4854.1106 | 2754.0 | 3270.0 | 0.8422 | 2746.0 | 0.8398 | 1799.0 | 1812.0 | 2026.0 | 0.8944 | 0.8880 | 938.0 | 942.0 | 1231.0 | 0.7652 | 0.7620 | | 0.0 | 26.0 | 6032 | 1.4848 | 0.0058 | 7004.8082 | 4855.3631 | 2760.0 | 3270.0 | 0.8440 | 2750.0 | 0.8410 | 1800.0 | 1816.0 | 2026.0 | 0.8963 | 0.8885 | 941.0 | 944.0 | 1231.0 | 0.7669 | 0.7644 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755554535

IvanJAjebu

2025-08-18T22:04:07Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:03:40Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

unitova/blockassist-bc-zealous_sneaky_raven_1755553093

unitova

2025-08-18T22:03:33Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:03:30Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - zealous sneaky raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

lemonhat/Qwen2.5-Coder-7B-Instruct-swe_5k_v1_tag4

lemonhat

2025-08-18T22:02:10Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-Coder-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-7B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-18T22:00:40Z

--- library_name: transformers license: other base_model: Qwen/Qwen2.5-Coder-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: swe_5k_v1_tag4 results: [] ---  # swe_5k_v1_tag4 This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the swe_5k_v1_tag4 dataset. It achieves the following results on the evaluation set: - Loss: 0.3938 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 4 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.3715 | 0.9709 | 100 | 0.3940 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

emre0005/blockassist-bc-humming_winged_okapi_1755554481

emre0005

2025-08-18T22:02:00Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "humming winged okapi", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:01:55Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - humming winged okapi --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

zenqqq/blockassist-bc-restless_reptilian_caterpillar_1755554381

zenqqq

2025-08-18T22:00:38Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "restless reptilian caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-08-18T22:00:28Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - restless reptilian caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

aliangdw/rfm_v3

aliangdw

2025-08-18T22:00:21Z

0

transformers

[ "transformers", "safetensors", "qwen2_5_vl", "reward-model", "rfm", "vision-language", "multimodal", "base_model:Qwen/Qwen2.5-VL-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2025-08-18T21:53:40Z

--- license: apache-2.0 base_model: Qwen/Qwen2.5-VL-3B-Instruct tags: - reward-model - rfm - vision-language - multimodal library_name: transformers --- # aliangdw/rfm_v3 This is a Reward Function Model (RFM) for vision-language preference learning and similarity assessment. ## Model Details - **Base Model**: Qwen/Qwen2.5-VL-3B-Instruct - **Model Type**: qwen2_5_vl - **Architecture**: RFMModel - **Task**: Vision-Language Reward Modeling - **Training Method**: FSDP (Fully Sharded Data Parallel) ## Usage ```python from transformers import AutoProcessor, AutoModel import torch # Load model and processor processor = AutoProcessor.from_pretrained("aliangdw/rfm_v3", trust_remote_code=True) model = AutoModel.from_pretrained("aliangdw/rfm_v3", trust_remote_code=True) # Example usage for preference scoring # inputs = processor(images=images, text=text, return_tensors="pt") # outputs = model(**inputs, sample_type="preference") ``` ## Model Capabilities This RFM model can perform: 1. **Preference Prediction**: Given two trajectories A and B, predict which one is preferred 2. **Similarity Assessment**: Evaluate how similar a trajectory is to a reference 3. **Progress Estimation**: Estimate task completion progress ## Training The model was trained using: - FSDP for distributed training - Mixed precision (bfloat16) - Custom loss functions for preference and similarity learning ## Files This repository contains: - Model weights in SafeTensors format - Configuration files - Tokenizer/Processor files ## Citation If you use this model, please cite: