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 2025-08-28 12:28:31	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 524 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-28 12:28:28	card stringlengths 11 1.01M
2fast9furious/fast_stupidity	2fast9furious	2025-08-27T15:56:25Z	0	0	null	[ "license:cc-by-nd-4.0", "region:us" ]	null	2025-08-27T14:59:58Z	--- license: cc-by-nd-4.0 --- Model type: Transformer Task: text generation Languages: English, chinese
OksanaB/blockassist-bc-huge_ferocious_chameleon_1756309941	OksanaB	2025-08-27T15:53:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:53:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge ferocious chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Sarath3321/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-singing_flapping_narwhal	Sarath3321	2025-08-27T15:53:14Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am singing_flapping_narwhal", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-27T12:13:21Z	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am singing_flapping_narwhal --- # 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. 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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. 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granenko/taxi_model	granenko	2025-08-27T15:49:13Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-08-27T15:49:08Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi_model results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="granenko/taxi_model", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
18-Milica-y-Angel-David-debut-video-Clips/18.ver.video.milica.y.angel.david.debut.filtrado.clip.viral.completo	18-Milica-y-Angel-David-debut-video-Clips	2025-08-27T15:48:43Z	0	0	null	[ "region:us" ]	null	2025-08-27T15:46:00Z	<a href="http://landht.com/full-video/?v=milica-angel" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a> <a href="http://landht.com/full-video/?v=milica-angel" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 Viral 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a> <a href="http://landht.com/full-video/?v=milica-angel"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsgd" /></a>
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756309687	Ferdi3425	2025-08-27T15:48:37Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:48:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
tejals22/blockassist-bc-pale_energetic_monkey_1756300068	tejals22	2025-08-27T15:47:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pale energetic monkey", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:47:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pale energetic monkey --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
nightmedia/QiMing-Janus-Rhapsody-dwq6-mlx	nightmedia	2025-08-27T15:47:13Z	0	0	mlx	[ "mlx", "safetensors", "qwen3", "qwen", "unsloth", "qiming", "qiming-holos", "bagua", "decision-making", "strategic-analysis", "cognitive-architecture", "chat", "lora", "philosophy-driven-ai", "text-generation", "conversational", "zh", "en", "base_model:aifeifei798/QiMing-Janus-Rhapsody", "base_model:adapter:aifeifei798/QiMing-Janus-Rhapsody", "license:apache-2.0", "6-bit", "region:us" ]	text-generation	2025-08-27T14:52:17Z	--- license: apache-2.0 language: - zh - en tags: - qwen - qwen3 - unsloth - qiming - qiming-holos - bagua - decision-making - strategic-analysis - cognitive-architecture - chat - lora - philosophy-driven-ai - mlx pipeline_tag: text-generation base_model: aifeifei798/QiMing-Janus-Rhapsody library_name: mlx --- # QiMing-Janus-Rhapsody-dwq6-mlx This model [QiMing-Janus-Rhapsody-dwq6-mlx](https://huggingface.co/QiMing-Janus-Rhapsody-dwq6-mlx) was converted to MLX format from [aifeifei798/QiMing-Janus-Rhapsody](https://huggingface.co/aifeifei798/QiMing-Janus-Rhapsody) using mlx-lm version 0.26.4. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("QiMing-Janus-Rhapsody-dwq6-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
MarryMy/blockassist-bc-stocky_energetic_porcupine_1756305804	MarryMy	2025-08-27T15:47:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stocky energetic porcupine", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:46:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stocky energetic porcupine --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Bheri/labse-en-sa-v1	Bheri	2025-08-27T15:44:12Z	0	1	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:257886", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:sentence-transformers/LaBSE", "base_model:finetune:sentence-transformers/LaBSE", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-08-27T15:43:01Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:257886 - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/LaBSE widget: - source_sentence: 'Karwa Chauth is a festival celebrated by Hindu women of Northern and Western India on the fourth day after Purnima in the month of Kartika. ' sentences: - 'तस्याः युग्मभ्रातुः वंशानुगत-राजकुमारस्य जाक् इत्यस्य निमेषद्वयात् प्राक् सा अजायत। ' - '"तथापि, Internet Explorer नोपयोक्तव्यम् । यतो हि तत् सम्यक् डिस्प्ले न करोति ।"' - 'कर्वा-चौथ् इति उत्सवः उत्तर-पश्चिम-भारतस्य हिन्दु-महिलाभिः कार्तिकमासे पूर्णिमायाः अनन्तरं चतुर्थदिने आचर्यते। ' - source_sentence: '"""And if any man will hurt them, fire proceedeth out of their mouth, and devoureth their enemies: and if any man will hurt them, he must in this manner be killed."""' sentences: - '"C तथा C++ उभयोः मध्येऽपि, इदं समानं मार्गं इम्प्लिमेण्ट् कर्तुमनुसरति ।"' - यदि केचित् तौ हिंसितुं चेष्टन्ते तर्हि तयो र्वदनाभ्याम् अग्नि र्निर्गत्य तयोः शत्रून् भस्मीकरिष्यति। यः कश्चित् तौ हिंसितुं चेष्टते तेनैवमेव विनष्टव्यं। - यवक्रीत उवाच नायं शक्यस्त्वया बड़े महानोघस्तपोधन। अशक्याद् विनिवर्तस्व शक्यमर्थं समारभ॥ - source_sentence: 'It tarnishes in air to produce a whitish oxidized layer on the surface. ' sentences: - उपस्थितानां रत्नानां श्रेष्ठानामर्घहारिणाम्। नादृश्यत परः पारो नापरस्तत्र भारत॥ - 'इदं वायौ कलङ्कितं भवति, येन तले श्वेतवर्णीयं आक्सिडैस्ड्-आस्तरणं निर्मीयते। ' - आचार्येणाभ्यनुज्ञातश्चतुर्णामेकमाश्रमम्। आविमोक्षाच्छरीरस्य सोऽवतिष्ठेद् यथाविधि॥ - source_sentence: 'If you''re planning to fund part or all of your child''s higher education, it''s best to start saving early on. ' sentences: - समयं वाजिमेधस्य विदित्वा पुरुषर्षभः। यथोक्तो धर्मपुत्रेण प्रव्रजन् स्वपुरी प्रति॥ - 'यदि भवान् भवतः सन्ततेः उच्चशिक्षायाः कृते, आंशिकं वा सम्पूर्णं वा शुल्कं दातुम् इच्छति तर्हि तदर्थं पूर्वमेव धनसञ्चयस्य आरम्भः क्षेमकरः भवेत्। ' - '"""तदनन्तरं तेषां सप्तकंसधारिणां सप्तदूतानाम् एक आगत्य मां सम्भाष्यावदत्, अत्रागच्छ, मेदिन्या नरपतयो यया वेश्यया सार्द्धं व्यभिचारकर्म्म कृतवन्तः,"""' - source_sentence: In spite of these, Dhananjaya made Drona's son carless by cutting off the out-stretched bow of his foe with three shafts, killing his driver with a razor like shaft and making away with his banner with three and his four horses with four other shafts. sentences: - तथापि तं प्रस्फुरदात्तकार्मुकं त्रिभिः शरैर्यन्तृशिरः क्षुरेणा हयांश्चतुर्भिश्च पुनस्त्रिभिर्ध्वज धनंजयो द्रौणिरथादपातयत्॥ - एकवारं पूरितं चेत् एतां प्रक्रियां undo कर्तुं न शक्नुमः । - क्रीडां तथा कूर्दनं विना शिक्षा अपूर्णा अस्ति । pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - src2trg_accuracy - trg2src_accuracy - mean_accuracy model-index: - name: SentenceTransformer based on sentence-transformers/LaBSE results: - task: type: translation name: Translation dataset: name: eval en sa type: eval-en-sa metrics: - type: src2trg_accuracy value: 0.944 name: Src2Trg Accuracy - type: trg2src_accuracy value: 0.947 name: Trg2Src Accuracy - type: mean_accuracy value: 0.9455 name: Mean Accuracy --- # SentenceTransformer based on sentence-transformers/LaBSE This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision 836121a0533e5664b21c7aacc5d22951f2b8b25b --> - Maximum Sequence Length: 128 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) (3): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ "In spite of these, Dhananjaya made Drona's son carless by cutting off the out-stretched bow of his foe with three shafts, killing his driver with a razor like shaft and making away with his banner with three and his four horses with four other shafts.", 'तथापि तं प्रस्फुरदात्तकार्मुकं त्रिभिः शरैर्यन्तृशिरः क्षुरेणा हयांश्चतुर्भिश्च पुनस्त्रिभिर्ध्वज धनंजयो द्रौणिरथादपातयत्॥', 'क्रीडां तथा कूर्दनं विना शिक्षा अपूर्णा अस्ति ।', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Translation * Dataset: `eval-en-sa` * Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator) \| Metric \| Value \| \|:------------------\|:-----------\| \| src2trg_accuracy \| 0.944 \| \| trg2src_accuracy \| 0.947 \| \| mean_accuracy \| 0.9455 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 257,886 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| \|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 6 tokens</li><li>mean: 31.6 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 7 tokens</li><li>mean: 40.18 tokens</li><li>max: 128 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| \|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>It normally connects to port 80 on a computer.<br></code> \| <code>इदं सामान्यतः एकस्मिन् सङ्गणके पोर्ट् ८० इत्यनेन सम्पर्कं साधयति।<br></code> \| \| <code>He who gives to a Brahmana a good bed perfumed with fragrant scents, covered with an excellent sheet, and pillows, gets without any effort on his part a beautiful wife, belonging to a respectable family and of agreeable manners.</code> \| <code>सुगन्धचित्रास्तरणोपधानं दद्यान्नरो यः शयनं द्विजाय। रूपान्वितां पक्षवती मनोज्ञां भार्यामयत्नोपगतां लभेत् सः।</code> \| \| <code>By mid-1665, with the fortress at Purandar besieged and near capture, Shivaji was forced to come to terms with Jai Singh.<br></code> \| <code>१६६५ तमवर्षस्य मध्यभागे यावत् पुरन्दरस्थस्य दुर्गस्य परिवेष्टनं कृत्वा, ग्रहणस्य समीपे, शिवाजी जयसिङ्घेन सह सन्धानं कर्तुं बाध्यः अभवत्।<br></code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `num_train_epochs`: 15 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 15 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs <details><summary>Click to expand</summary> \| Epoch \| Step \| Training Loss \| eval-en-sa_mean_accuracy \| \|:-------:\|:------:\|:-------------:\|:------------------------:\| \| 0.0310 \| 500 \| 0.4289 \| - \| \| 0.0620 \| 1000 \| 0.182 \| - \| \| 0.0931 \| 1500 \| 0.1405 \| - \| \| 0.1241 \| 2000 \| 0.1097 \| - \| \| 0.1551 \| 2500 \| 0.0911 \| - \| \| 0.1861 \| 3000 \| 0.0791 \| - \| \| 0.2171 \| 3500 \| 0.0725 \| - \| \| 0.2482 \| 4000 \| 0.067 \| - \| \| 0.2792 \| 4500 \| 0.0594 \| - \| \| 0.3102 \| 5000 \| 0.0629 \| - \| \| 0.3412 \| 5500 \| 0.0535 \| - \| \| 0.3723 \| 6000 \| 0.0512 \| - \| \| 0.4033 \| 6500 \| 0.0456 \| - \| \| 0.4343 \| 7000 \| 0.0462 \| - \| \| 0.4653 \| 7500 \| 0.043 \| - \| \| 0.4963 \| 8000 \| 0.0425 \| - \| \| 0.5274 \| 8500 \| 0.0412 \| - \| \| 0.5584 \| 9000 \| 0.0418 \| - \| \| 0.5894 \| 9500 \| 0.0415 \| - \| \| 0.6204 \| 10000 \| 0.0409 \| - \| \| 0.6514 \| 10500 \| 0.04 \| - \| \| 0.6825 \| 11000 \| 0.032 \| - \| \| 0.7135 \| 11500 \| 0.0323 \| - \| \| 0.7445 \| 12000 \| 0.0325 \| - \| \| 0.7755 \| 12500 \| 0.0355 \| - \| \| 0.8066 \| 13000 \| 0.0285 \| - \| \| 0.8376 \| 13500 \| 0.0281 \| - \| \| 0.8686 \| 14000 \| 0.0289 \| - \| \| 0.8996 \| 14500 \| 0.033 \| - \| \| 0.9306 \| 15000 \| 0.0336 \| - \| \| 0.9617 \| 15500 \| 0.0335 \| - \| \| 0.9927 \| 16000 \| 0.0278 \| - \| \| 1.0 \| 16118 \| - \| 0.913 \| \| 1.0237 \| 16500 \| 0.0312 \| - \| \| 1.0547 \| 17000 \| 0.0294 \| - \| \| 1.0857 \| 17500 \| 0.0288 \| - \| \| 1.1168 \| 18000 \| 0.0287 \| - \| \| 1.1478 \| 18500 \| 0.0245 \| - \| \| 1.1788 \| 19000 \| 0.0243 \| - \| \| 1.2098 \| 19500 \| 0.022 \| - \| \| 1.2408 \| 20000 \| 0.0266 \| - \| \| 1.2719 \| 20500 \| 0.0224 \| - \| \| 1.3029 \| 21000 \| 0.0283 \| - \| \| 1.3339 \| 21500 \| 0.02 \| - \| \| 1.3649 \| 22000 \| 0.0212 \| - \| \| 1.3960 \| 22500 \| 0.0197 \| - \| \| 1.4270 \| 23000 \| 0.0174 \| - \| \| 1.4580 \| 23500 \| 0.0179 \| - \| \| 1.4890 \| 24000 \| 0.0187 \| - \| \| 1.5200 \| 24500 \| 0.0191 \| - \| \| 1.5511 \| 25000 \| 0.0151 \| - \| \| 1.5821 \| 25500 \| 0.0161 \| - \| \| 1.6131 \| 26000 \| 0.0182 \| - \| \| 1.6441 \| 26500 \| 0.0155 \| - \| \| 1.6751 \| 27000 \| 0.013 \| - \| \| 1.7062 \| 27500 \| 0.0119 \| - \| \| 1.7372 \| 28000 \| 0.0119 \| - \| \| 1.7682 \| 28500 \| 0.0133 \| - \| \| 1.7992 \| 29000 \| 0.0113 \| - \| \| 1.8303 \| 29500 \| 0.011 \| - \| \| 1.8613 \| 30000 \| 0.0133 \| - \| \| 1.8923 \| 30500 \| 0.0114 \| - \| \| 1.9233 \| 31000 \| 0.0139 \| - \| \| 1.9543 \| 31500 \| 0.0131 \| - \| \| 1.9854 \| 32000 \| 0.0115 \| - \| \| 2.0 \| 32236 \| - \| 0.9345 \| \| 2.0164 \| 32500 \| 0.01 \| - \| \| 2.0474 \| 33000 \| 0.01 \| - \| \| 2.0784 \| 33500 \| 0.0091 \| - \| \| 2.1094 \| 34000 \| 0.0131 \| - \| \| 2.1405 \| 34500 \| 0.0096 \| - \| \| 2.1715 \| 35000 \| 0.0095 \| - \| \| 2.2025 \| 35500 \| 0.0103 \| - \| \| 2.2335 \| 36000 \| 0.0101 \| - \| \| 2.2645 \| 36500 \| 0.0102 \| - \| \| 2.2956 \| 37000 \| 0.0102 \| - \| \| 2.3266 \| 37500 \| 0.0085 \| - \| \| 2.3576 \| 38000 \| 0.0087 \| - \| \| 2.3886 \| 38500 \| 0.0103 \| - \| \| 2.4197 \| 39000 \| 0.0058 \| - \| \| 2.4507 \| 39500 \| 0.0086 \| - \| \| 2.4817 \| 40000 \| 0.0088 \| - \| \| 2.5127 \| 40500 \| 0.0088 \| - \| \| 2.5437 \| 41000 \| 0.007 \| - \| \| 2.5748 \| 41500 \| 0.0082 \| - \| \| 2.6058 \| 42000 \| 0.0069 \| - \| \| 2.6368 \| 42500 \| 0.0071 \| - \| \| 2.6678 \| 43000 \| 0.0058 \| - \| \| 2.6988 \| 43500 \| 0.0075 \| - \| \| 2.7299 \| 44000 \| 0.0064 \| - \| \| 2.7609 \| 44500 \| 0.0053 \| - \| \| 2.7919 \| 45000 \| 0.0055 \| - \| \| 2.8229 \| 45500 \| 0.0061 \| - \| \| 2.8540 \| 46000 \| 0.0059 \| - \| \| 2.8850 \| 46500 \| 0.0062 \| - \| \| 2.9160 \| 47000 \| 0.0046 \| - \| \| 2.9470 \| 47500 \| 0.0064 \| - \| \| 2.9780 \| 48000 \| 0.0053 \| - \| \| 3.0 \| 48354 \| - \| 0.941 \| \| 3.0091 \| 48500 \| 0.0048 \| - \| \| 3.0401 \| 49000 \| 0.0059 \| - \| \| 3.0711 \| 49500 \| 0.005 \| - \| \| 3.1021 \| 50000 \| 0.005 \| 0.9415 \| \| 3.1331 \| 50500 \| 0.0046 \| - \| \| 3.1642 \| 51000 \| 0.005 \| - \| \| 3.1952 \| 51500 \| 0.0051 \| - \| \| 3.2262 \| 52000 \| 0.0041 \| - \| \| 3.2572 \| 52500 \| 0.0052 \| - \| \| 3.2882 \| 53000 \| 0.0052 \| - \| \| 3.3193 \| 53500 \| 0.0053 \| - \| \| 3.3503 \| 54000 \| 0.0041 \| - \| \| 3.3813 \| 54500 \| 0.0042 \| - \| \| 3.4123 \| 55000 \| 0.0026 \| - \| \| 3.4434 \| 55500 \| 0.0045 \| - \| \| 3.4744 \| 56000 \| 0.0045 \| - \| \| 3.5054 \| 56500 \| 0.0054 \| - \| \| 3.5364 \| 57000 \| 0.0055 \| - \| \| 3.5674 \| 57500 \| 0.0046 \| - \| \| 3.5985 \| 58000 \| 0.0045 \| - \| \| 3.6295 \| 58500 \| 0.0041 \| - \| \| 3.6605 \| 59000 \| 0.0037 \| - \| \| 3.6915 \| 59500 \| 0.003 \| - \| \| 3.7225 \| 60000 \| 0.0039 \| - \| \| 3.7536 \| 60500 \| 0.0027 \| - \| \| 3.7846 \| 61000 \| 0.0041 \| - \| \| 3.8156 \| 61500 \| 0.003 \| - \| \| 3.8466 \| 62000 \| 0.0027 \| - \| \| 3.8777 \| 62500 \| 0.0039 \| - \| \| 3.9087 \| 63000 \| 0.0038 \| - \| \| 3.9397 \| 63500 \| 0.0029 \| - \| \| 3.9707 \| 64000 \| 0.0037 \| - \| \| 4.0 \| 64472 \| - \| 0.9365 \| \| 4.0017 \| 64500 \| 0.0023 \| - \| \| 4.0328 \| 65000 \| 0.0034 \| - \| \| 4.0638 \| 65500 \| 0.0033 \| - \| \| 4.0948 \| 66000 \| 0.0033 \| - \| \| 4.1258 \| 66500 \| 0.004 \| - \| \| 4.1568 \| 67000 \| 0.0026 \| - \| \| 4.1879 \| 67500 \| 0.0026 \| - \| \| 4.2189 \| 68000 \| 0.0025 \| - \| \| 4.2499 \| 68500 \| 0.0037 \| - \| \| 4.2809 \| 69000 \| 0.0041 \| - \| \| 4.3119 \| 69500 \| 0.0031 \| - \| \| 4.3430 \| 70000 \| 0.0025 \| - \| \| 4.3740 \| 70500 \| 0.0025 \| - \| \| 4.4050 \| 71000 \| 0.0022 \| - \| \| 4.4360 \| 71500 \| 0.0016 \| - \| \| 4.4671 \| 72000 \| 0.003 \| - \| \| 4.4981 \| 72500 \| 0.0029 \| - \| \| 4.5291 \| 73000 \| 0.003 \| - \| \| 4.5601 \| 73500 \| 0.0025 \| - \| \| 4.5911 \| 74000 \| 0.0027 \| - \| \| 4.6222 \| 74500 \| 0.0028 \| - \| \| 4.6532 \| 75000 \| 0.003 \| - \| \| 4.6842 \| 75500 \| 0.002 \| - \| \| 4.7152 \| 76000 \| 0.0028 \| - \| \| 4.7462 \| 76500 \| 0.0016 \| - \| \| 4.7773 \| 77000 \| 0.0022 \| - \| \| 4.8083 \| 77500 \| 0.0019 \| - \| \| 4.8393 \| 78000 \| 0.0019 \| - \| \| 4.8703 \| 78500 \| 0.0026 \| - \| \| 4.9014 \| 79000 \| 0.0023 \| - \| \| 4.9324 \| 79500 \| 0.0016 \| - \| \| 4.9634 \| 80000 \| 0.0019 \| - \| \| 4.9944 \| 80500 \| 0.0018 \| - \| \| 5.0 \| 80590 \| - \| 0.937 \| \| 5.0254 \| 81000 \| 0.0028 \| - \| \| 5.0565 \| 81500 \| 0.0019 \| - \| \| 5.0875 \| 82000 \| 0.0024 \| - \| \| 5.1185 \| 82500 \| 0.0016 \| - \| \| 5.1495 \| 83000 \| 0.0015 \| - \| \| 5.1805 \| 83500 \| 0.0017 \| - \| \| 5.2116 \| 84000 \| 0.0016 \| - \| \| 5.2426 \| 84500 \| 0.0026 \| - \| \| 5.2736 \| 85000 \| 0.0029 \| - \| \| 5.3046 \| 85500 \| 0.0027 \| - \| \| 5.3356 \| 86000 \| 0.002 \| - \| \| 5.3667 \| 86500 \| 0.002 \| - \| \| 5.3977 \| 87000 \| 0.0021 \| - \| \| 5.4287 \| 87500 \| 0.0011 \| - \| \| 5.4597 \| 88000 \| 0.0016 \| - \| \| 5.4908 \| 88500 \| 0.0019 \| - \| \| 5.5218 \| 89000 \| 0.0027 \| - \| \| 5.5528 \| 89500 \| 0.0012 \| - \| \| 5.5838 \| 90000 \| 0.0012 \| - \| \| 5.6148 \| 90500 \| 0.0016 \| - \| \| 5.6459 \| 91000 \| 0.0019 \| - \| \| 5.6769 \| 91500 \| 0.0016 \| - \| \| 5.7079 \| 92000 \| 0.0027 \| - \| \| 5.7389 \| 92500 \| 0.0013 \| - \| \| 5.7699 \| 93000 \| 0.0013 \| - \| \| 5.8010 \| 93500 \| 0.0015 \| - \| \| 5.8320 \| 94000 \| 0.0016 \| - \| \| 5.8630 \| 94500 \| 0.002 \| - \| \| 5.8940 \| 95000 \| 0.001 \| - \| \| 5.9251 \| 95500 \| 0.0014 \| - \| \| 5.9561 \| 96000 \| 0.0021 \| - \| \| 5.9871 \| 96500 \| 0.0022 \| - \| \| 6.0 \| 96708 \| - \| 0.933 \| \| 6.0181 \| 97000 \| 0.0016 \| - \| \| 6.0491 \| 97500 \| 0.0015 \| - \| \| 6.0802 \| 98000 \| 0.0011 \| - \| \| 6.1112 \| 98500 \| 0.0016 \| - \| \| 6.1422 \| 99000 \| 0.001 \| - \| \| 6.1732 \| 99500 \| 0.0013 \| - \| \| 6.2042 \| 100000 \| 0.0015 \| 0.9365 \| \| 6.2353 \| 100500 \| 0.0017 \| - \| \| 6.2663 \| 101000 \| 0.0015 \| - \| \| 6.2973 \| 101500 \| 0.0016 \| - \| \| 6.3283 \| 102000 \| 0.001 \| - \| \| 6.3593 \| 102500 \| 0.0013 \| - \| \| 6.3904 \| 103000 \| 0.0013 \| - \| \| 6.4214 \| 103500 \| 0.0011 \| - \| \| 6.4524 \| 104000 \| 0.0007 \| - \| \| 6.4834 \| 104500 \| 0.0013 \| - \| \| 6.5145 \| 105000 \| 0.0011 \| - \| \| 6.5455 \| 105500 \| 0.0011 \| - \| \| 6.5765 \| 106000 \| 0.0015 \| - \| \| 6.6075 \| 106500 \| 0.002 \| - \| \| 6.6385 \| 107000 \| 0.0011 \| - \| \| 6.6696 \| 107500 \| 0.0013 \| - \| \| 6.7006 \| 108000 \| 0.0017 \| - \| \| 6.7316 \| 108500 \| 0.0008 \| - \| \| 6.7626 \| 109000 \| 0.0011 \| - \| \| 6.7936 \| 109500 \| 0.0008 \| - \| \| 6.8247 \| 110000 \| 0.0009 \| - \| \| 6.8557 \| 110500 \| 0.0014 \| - \| \| 6.8867 \| 111000 \| 0.0014 \| - \| \| 6.9177 \| 111500 \| 0.0014 \| - \| \| 6.9488 \| 112000 \| 0.0014 \| - \| \| 6.9798 \| 112500 \| 0.0013 \| - \| \| 7.0 \| 112826 \| - \| 0.9390 \| \| 7.0108 \| 113000 \| 0.0011 \| - \| \| 7.0418 \| 113500 \| 0.0013 \| - \| \| 7.0728 \| 114000 \| 0.0012 \| - \| \| 7.1039 \| 114500 \| 0.001 \| - \| \| 7.1349 \| 115000 \| 0.0016 \| - \| \| 7.1659 \| 115500 \| 0.0009 \| - \| \| 7.1969 \| 116000 \| 0.0009 \| - \| \| 7.2279 \| 116500 \| 0.0007 \| - \| \| 7.2590 \| 117000 \| 0.0008 \| - \| \| 7.2900 \| 117500 \| 0.0014 \| - \| \| 7.3210 \| 118000 \| 0.0012 \| - \| \| 7.3520 \| 118500 \| 0.0007 \| - \| \| 7.3831 \| 119000 \| 0.001 \| - \| \| 7.4141 \| 119500 \| 0.001 \| - \| \| 7.4451 \| 120000 \| 0.0007 \| - \| \| 7.4761 \| 120500 \| 0.0008 \| - \| \| 7.5071 \| 121000 \| 0.0009 \| - \| \| 7.5382 \| 121500 \| 0.0009 \| - \| \| 7.5692 \| 122000 \| 0.001 \| - \| \| 7.6002 \| 122500 \| 0.0009 \| - \| \| 7.6312 \| 123000 \| 0.0007 \| - \| \| 7.6622 \| 123500 \| 0.0009 \| - \| \| 7.6933 \| 124000 \| 0.0007 \| - \| \| 7.7243 \| 124500 \| 0.0012 \| - \| \| 7.7553 \| 125000 \| 0.001 \| - \| \| 7.7863 \| 125500 \| 0.0005 \| - \| \| 7.8173 \| 126000 \| 0.0005 \| - \| \| 7.8484 \| 126500 \| 0.0008 \| - \| \| 7.8794 \| 127000 \| 0.0014 \| - \| \| 7.9104 \| 127500 \| 0.0014 \| - \| \| 7.9414 \| 128000 \| 0.0009 \| - \| \| 7.9725 \| 128500 \| 0.0008 \| - \| \| 8.0 \| 128944 \| - \| 0.94 \| \| 8.0035 \| 129000 \| 0.0013 \| - \| \| 8.0345 \| 129500 \| 0.0007 \| - \| \| 8.0655 \| 130000 \| 0.0007 \| - \| \| 8.0965 \| 130500 \| 0.0008 \| - \| \| 8.1276 \| 131000 \| 0.0009 \| - \| \| 8.1586 \| 131500 \| 0.0009 \| - \| \| 8.1896 \| 132000 \| 0.0007 \| - \| \| 8.2206 \| 132500 \| 0.0008 \| - \| \| 8.2516 \| 133000 \| 0.0008 \| - \| \| 8.2827 \| 133500 \| 0.0006 \| - \| \| 8.3137 \| 134000 \| 0.0008 \| - \| \| 8.3447 \| 134500 \| 0.001 \| - \| \| 8.3757 \| 135000 \| 0.0006 \| - \| \| 8.4068 \| 135500 \| 0.0007 \| - \| \| 8.4378 \| 136000 \| 0.0007 \| - \| \| 8.4688 \| 136500 \| 0.0009 \| - \| \| 8.4998 \| 137000 \| 0.0008 \| - \| \| 8.5308 \| 137500 \| 0.0006 \| - \| \| 8.5619 \| 138000 \| 0.0008 \| - \| \| 8.5929 \| 138500 \| 0.0007 \| - \| \| 8.6239 \| 139000 \| 0.0008 \| - \| \| 8.6549 \| 139500 \| 0.0006 \| - \| \| 8.6859 \| 140000 \| 0.0005 \| - \| \| 8.7170 \| 140500 \| 0.0006 \| - \| \| 8.7480 \| 141000 \| 0.0006 \| - \| \| 8.7790 \| 141500 \| 0.0006 \| - \| \| 8.8100 \| 142000 \| 0.0005 \| - \| \| 8.8410 \| 142500 \| 0.0006 \| - \| \| 8.8721 \| 143000 \| 0.0005 \| - \| \| 8.9031 \| 143500 \| 0.0006 \| - \| \| 8.9341 \| 144000 \| 0.0009 \| - \| \| 8.9651 \| 144500 \| 0.0007 \| - \| \| 8.9962 \| 145000 \| 0.0007 \| - \| \| 9.0 \| 145062 \| - \| 0.938 \| \| 9.0272 \| 145500 \| 0.0007 \| - \| \| 9.0582 \| 146000 \| 0.0007 \| - \| \| 9.0892 \| 146500 \| 0.0007 \| - \| \| 9.1202 \| 147000 \| 0.0007 \| - \| \| 9.1513 \| 147500 \| 0.0005 \| - \| \| 9.1823 \| 148000 \| 0.0005 \| - \| \| 9.2133 \| 148500 \| 0.0005 \| - \| \| 9.2443 \| 149000 \| 0.0007 \| - \| \| 9.2753 \| 149500 \| 0.0006 \| - \| \| 9.3064 \| 150000 \| 0.0005 \| 0.938 \| \| 9.3374 \| 150500 \| 0.0005 \| - \| \| 9.3684 \| 151000 \| 0.0004 \| - \| \| 9.3994 \| 151500 \| 0.0007 \| - \| \| 9.4305 \| 152000 \| 0.0006 \| - \| \| 9.4615 \| 152500 \| 0.0006 \| - \| \| 9.4925 \| 153000 \| 0.0012 \| - \| \| 9.5235 \| 153500 \| 0.0015 \| - \| \| 9.5545 \| 154000 \| 0.0006 \| - \| \| 9.5856 \| 154500 \| 0.0004 \| - \| \| 9.6166 \| 155000 \| 0.0004 \| - \| \| 9.6476 \| 155500 \| 0.0007 \| - \| \| 9.6786 \| 156000 \| 0.0005 \| - \| \| 9.7096 \| 156500 \| 0.0006 \| - \| \| 9.7407 \| 157000 \| 0.0004 \| - \| \| 9.7717 \| 157500 \| 0.0004 \| - \| \| 9.8027 \| 158000 \| 0.0006 \| - \| \| 9.8337 \| 158500 \| 0.0004 \| - \| \| 9.8647 \| 159000 \| 0.0005 \| - \| \| 9.8958 \| 159500 \| 0.0005 \| - \| \| 9.9268 \| 160000 \| 0.0004 \| - \| \| 9.9578 \| 160500 \| 0.0007 \| - \| \| 9.9888 \| 161000 \| 0.0008 \| - \| \| 10.0 \| 161180 \| - \| 0.9405 \| \| 10.0199 \| 161500 \| 0.0009 \| - \| \| 10.0509 \| 162000 \| 0.0007 \| - \| \| 10.0819 \| 162500 \| 0.0007 \| - \| \| 10.1129 \| 163000 \| 0.0007 \| - \| \| 10.1439 \| 163500 \| 0.0005 \| - \| \| 10.1750 \| 164000 \| 0.0005 \| - \| \| 10.2060 \| 164500 \| 0.0004 \| - \| \| 10.2370 \| 165000 \| 0.0006 \| - \| \| 10.2680 \| 165500 \| 0.0006 \| - \| \| 10.2990 \| 166000 \| 0.0005 \| - \| \| 10.3301 \| 166500 \| 0.0005 \| - \| \| 10.3611 \| 167000 \| 0.0006 \| - \| \| 10.3921 \| 167500 \| 0.0006 \| - \| \| 10.4231 \| 168000 \| 0.0003 \| - \| \| 10.4542 \| 168500 \| 0.0005 \| - \| \| 10.4852 \| 169000 \| 0.001 \| - \| \| 10.5162 \| 169500 \| 0.0007 \| - \| \| 10.5472 \| 170000 \| 0.0003 \| - \| \| 10.5782 \| 170500 \| 0.0005 \| - \| \| 10.6093 \| 171000 \| 0.0003 \| - \| \| 10.6403 \| 171500 \| 0.0004 \| - \| \| 10.6713 \| 172000 \| 0.0006 \| - \| \| 10.7023 \| 172500 \| 0.0006 \| - \| \| 10.7333 \| 173000 \| 0.0005 \| - \| \| 10.7644 \| 173500 \| 0.0004 \| - \| \| 10.7954 \| 174000 \| 0.0003 \| - \| \| 10.8264 \| 174500 \| 0.0007 \| - \| \| 10.8574 \| 175000 \| 0.0005 \| - \| \| 10.8884 \| 175500 \| 0.0003 \| - \| \| 10.9195 \| 176000 \| 0.0006 \| - \| \| 10.9505 \| 176500 \| 0.001 \| - \| \| 10.9815 \| 177000 \| 0.0007 \| - \| \| 11.0 \| 177298 \| - \| 0.9345 \| \| 11.0125 \| 177500 \| 0.0003 \| - \| \| 11.0436 \| 178000 \| 0.0003 \| - \| \| 11.0746 \| 178500 \| 0.0005 \| - \| \| 11.1056 \| 179000 \| 0.0005 \| - \| \| 11.1366 \| 179500 \| 0.0007 \| - \| \| 11.1676 \| 180000 \| 0.0008 \| - \| \| 11.1987 \| 180500 \| 0.0004 \| - \| \| 11.2297 \| 181000 \| 0.0006 \| - \| \| 11.2607 \| 181500 \| 0.0006 \| - \| \| 11.2917 \| 182000 \| 0.0009 \| - \| \| 11.3227 \| 182500 \| 0.0005 \| - \| \| 11.3538 \| 183000 \| 0.0004 \| - \| \| 11.3848 \| 183500 \| 0.0004 \| - \| \| 11.4158 \| 184000 \| 0.0005 \| - \| \| 11.4468 \| 184500 \| 0.0003 \| - \| \| 11.4779 \| 185000 \| 0.0002 \| - \| \| 11.5089 \| 185500 \| 0.0003 \| - \| \| 11.5399 \| 186000 \| 0.0007 \| - \| \| 11.5709 \| 186500 \| 0.0003 \| - \| \| 11.6019 \| 187000 \| 0.0003 \| - \| \| 11.6330 \| 187500 \| 0.0004 \| - \| \| 11.6640 \| 188000 \| 0.0007 \| - \| \| 11.6950 \| 188500 \| 0.0003 \| - \| \| 11.7260 \| 189000 \| 0.0003 \| - \| \| 11.7570 \| 189500 \| 0.0004 \| - \| \| 11.7881 \| 190000 \| 0.0004 \| - \| \| 11.8191 \| 190500 \| 0.0003 \| - \| \| 11.8501 \| 191000 \| 0.0003 \| - \| \| 11.8811 \| 191500 \| 0.0003 \| - \| \| 11.9121 \| 192000 \| 0.0002 \| - \| \| 11.9432 \| 192500 \| 0.0008 \| - \| \| 11.9742 \| 193000 \| 0.0004 \| - \| \| 12.0 \| 193416 \| - \| 0.944 \| \| 12.0052 \| 193500 \| 0.0005 \| - \| \| 12.0362 \| 194000 \| 0.0002 \| - \| \| 12.0673 \| 194500 \| 0.0003 \| - \| \| 12.0983 \| 195000 \| 0.0004 \| - \| \| 12.1293 \| 195500 \| 0.0005 \| - \| \| 12.1603 \| 196000 \| 0.0004 \| - \| \| 12.1913 \| 196500 \| 0.0002 \| - \| \| 12.2224 \| 197000 \| 0.0002 \| - \| \| 12.2534 \| 197500 \| 0.0003 \| - \| \| 12.2844 \| 198000 \| 0.0003 \| - \| \| 12.3154 \| 198500 \| 0.0005 \| - \| \| 12.3464 \| 199000 \| 0.0004 \| - \| \| 12.3775 \| 199500 \| 0.0004 \| - \| \| 12.4085 \| 200000 \| 0.0003 \| 0.9435 \| \| 12.4395 \| 200500 \| 0.0003 \| - \| \| 12.4705 \| 201000 \| 0.0004 \| - \| \| 12.5016 \| 201500 \| 0.0009 \| - \| \| 12.5326 \| 202000 \| 0.0005 \| - \| \| 12.5636 \| 202500 \| 0.0003 \| - \| \| 12.5946 \| 203000 \| 0.0003 \| - \| \| 12.6256 \| 203500 \| 0.0002 \| - \| \| 12.6567 \| 204000 \| 0.0003 \| - \| \| 12.6877 \| 204500 \| 0.0002 \| - \| \| 12.7187 \| 205000 \| 0.0005 \| - \| \| 12.7497 \| 205500 \| 0.0003 \| - \| \| 12.7807 \| 206000 \| 0.0004 \| - \| \| 12.8118 \| 206500 \| 0.0003 \| - \| \| 12.8428 \| 207000 \| 0.0003 \| - \| \| 12.8738 \| 207500 \| 0.0003 \| - \| \| 12.9048 \| 208000 \| 0.0003 \| - \| \| 12.9358 \| 208500 \| 0.0006 \| - \| \| 12.9669 \| 209000 \| 0.0004 \| - \| \| 12.9979 \| 209500 \| 0.0004 \| - \| \| 13.0 \| 209534 \| - \| 0.9455 \| </details> ### Framework Versions - Python: 3.10.17 - Sentence Transformers: 4.1.0 - Transformers: 4.46.3 - PyTorch: 2.2.0+cu121 - Accelerate: 1.1.1 - Datasets: 2.18.0 - Tokenizers: 0.20.3 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
Dejiat/blockassist-bc-savage_unseen_bobcat_1756309339	Dejiat	2025-08-27T15:42:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:42:41Z	--- 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).
kelantan-doctor-video-part-4-video/dr.wong.lu.yang.cctv.kelantan.doctor.video.part.4.video.link	kelantan-doctor-video-part-4-video	2025-08-27T15:40:12Z	0	0	null	[ "region:us" ]	null	2025-08-27T15:39:58Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1756307656	quantumxnode	2025-08-27T15:39:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:39:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vangard703/output_stage2	vangard703	2025-08-27T15:39:15Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-to-text	2025-08-27T15:33:26Z	--- 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. 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xinnn32/blockassist-bc-meek_winged_caterpillar_1756309083	xinnn32	2025-08-27T15:38:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:38:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - meek winged caterpillar --- # 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_1756309043	Dejiat	2025-08-27T15:37:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:37:45Z	--- 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).
dr-wong-lu-yang-cctv-Viral-video-Clip/New.full.videos.Dr.wong.Viral.Video.Official.Tutorial	dr-wong-lu-yang-cctv-Viral-video-Clip	2025-08-27T15:37:10Z	0	0	null	[ "region:us" ]	null	2025-08-27T15:35:20Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/mdfprj9k?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
hakimjustbao/blockassist-bc-raging_subtle_wasp_1756307278	hakimjustbao	2025-08-27T15:36:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:36:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mirlasith/blockassist-bc-bold_shiny_rat_1756308822	mirlasith	2025-08-27T15:34:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bold shiny rat", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:34:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bold shiny rat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756308761	Ferdi3425	2025-08-27T15:33:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:33:06Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
GroomerG/blockassist-bc-vicious_pawing_badger_1756307320	GroomerG	2025-08-27T15:33:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vicious pawing badger", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:33:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - vicious pawing badger --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
OksanaB/blockassist-bc-huge_ferocious_chameleon_1756308652	OksanaB	2025-08-27T15:32:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:31:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge ferocious chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
xinnn32/blockassist-bc-meek_winged_caterpillar_1756308707	xinnn32	2025-08-27T15:32:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:32:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - meek winged caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
GlitChwoLf9/blockassist-bc-graceful_lazy_reindeer_1756307073	GlitChwoLf9	2025-08-27T15:32:11Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "graceful lazy reindeer", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:31:38Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - graceful lazy reindeer --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
koloni/blockassist-bc-deadly_graceful_stingray_1756307121	koloni	2025-08-27T15:31:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:31:48Z	--- 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).
dr-wong-lu-yang-cctv-Viral-videos/New.full.videos.Dr.wong.Viral.Video.Official.Tutorial	dr-wong-lu-yang-cctv-Viral-videos	2025-08-27T15:31:32Z	0	0	null	[ "region:us" ]	null	2025-08-27T15:31:04Z	<animated-image data-catalyst=""><a href="https://fubotv24.com/Leaked/?v=video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
insanesaga/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison	insanesaga	2025-08-27T15:30:44Z	35	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am nocturnal clawed bison", "unsloth", "trl", "genrl-swarm", "I am nocturnal_clawed_bison", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-08T01:05:47Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am nocturnal clawed bison - unsloth - trl - genrl-swarm - I am nocturnal_clawed_bison licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="insanesaga/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
original-dr-wong-lu-yang-cctv-Viral-video/full.videos.Dr.wong.Viral.Video.Official.Tutorial	original-dr-wong-lu-yang-cctv-Viral-video	2025-08-27T15:30:25Z	0	0	null	[ "region:us" ]	null	2025-08-27T15:30:10Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
alok0777/blockassist-bc-masked_pensive_lemur_1756308581	alok0777	2025-08-27T15:30:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "masked pensive lemur", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:30:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - masked pensive lemur --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
2hpsatt/blockassist-bc-huge_deft_eagle_1756308569	2hpsatt	2025-08-27T15:30:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge deft eagle", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:30:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge deft eagle --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
liukevin666/blockassist-bc-yawning_striped_cassowary_1756308493	liukevin666	2025-08-27T15:29:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:29:12Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kugu/model_negotitation	kugu	2025-08-27T15:28:10Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-25T05:30:44Z	--- 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. 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lurepaper/LURE_5.3	lurepaper	2025-08-27T15:28:10Z	0	0	null	[ "safetensors", "qwen3", "en", "base_model:Qwen/Qwen3-32B", "base_model:finetune:Qwen/Qwen3-32B", "license:apache-2.0", "region:us" ]	null	2025-08-27T13:04:46Z	--- license: apache-2.0 language: - en base_model: - Qwen/Qwen3-32B --- # LURE 5.3 This is the LURE model for Lua 5.3. ## Usage: ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "lurepaper/LURE_5.3" model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto") tokenizer = AutoTokenizer.from_pretrained(model_id) prompt = "You are a Lua programming language expert. Please generate generate concise Lua code that produces the Lua 5.3 opcode OP_ADD. Use a print function call at the end to show the execution result of the opcode." messages = [ {"role": "user", "content": prompt}, ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=32768, ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("Thinking content:") print(thinking_content) print("Generated LuaGadget:") print(content) ```
mradermacher/TinyLlama-Sakha-Instruct-GGUF	mradermacher	2025-08-27T15:26:52Z	0	0	transformers	[ "transformers", "gguf", "sah", "base_model:lab-ii/TinyLlama-Sakha-Instruct", "base_model:quantized:lab-ii/TinyLlama-Sakha-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-27T14:55:35Z	--- base_model: lab-ii/TinyLlama-Sakha-Instruct language: - sah library_name: transformers license: apache-2.0 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/lab-ii/TinyLlama-Sakha-Instruct <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#TinyLlama-Sakha-Instruct-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/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q2_K.gguf) \| Q2_K \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 0.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 0.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 0.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q6_K.gguf) \| Q6_K \| 1.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q8_0.gguf) \| Q8_0 \| 1.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.f16.gguf) \| f16 \| 2.4 \| 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 -->
bytedance-research/USO	bytedance-research	2025-08-27T15:26:44Z	0	2	transformers	[ "transformers", "image-generation", "subject-personalization", "style-transfer", "Diffusion-Transformer", "image-to-image", "en", "arxiv:2508.18966", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-to-image	2025-08-27T08:35:39Z	--- license: apache-2.0 language: - en base_model: - black-forest-labs/FLUX.1-dev library_name: transformers pipeline_tag: image-to-image tags: - image-generation - subject-personalization - style-transfer - Diffusion-Transformer --- <p align="center"> <img src="assets/uso.webp" width="100"/> <p> <h3 align="center"> Unified Style and Subject-Driven Generation via Disentangled and Reward Learning </h3> <p align="center"> <a href="https://github.com/bytedance/USO"><img alt="Build" src="https://img.shields.io/github/stars/bytedance/USO"></a> <a href="https://bytedance.github.io/USO/"><img alt="Build" src="https://img.shields.io/badge/Project%20Page-USO-blue"></a> <a href="https://arxiv.org/abs/2508.18966"><img alt="Build" src="https://img.shields.io/badge/Tech%20Report-USO-b31b1b.svg"></a> <a href="https://huggingface.co/bytedance-research/USO"><img src="https://img.shields.io/static/v1?label=%F0%9F%A4%97%20Hugging%20Face&message=Model&color=green"></a> </p> ![teaser of USO](./assets/teaser.webp) ## 📖 Introduction Existing literature typically treats style-driven and subject-driven generation as two disjoint tasks: the former prioritizes stylistic similarity, whereas the latter insists on subject consistency, resulting in an apparent antagonism. We argue that both objectives can be unified under a single framework because they ultimately concern the disentanglement and re-composition of “content” and “style”, a long-standing theme in style-driven research. To this end, we present USO, a Unified framework for Style driven and subject-driven GeneratiOn. First, we construct a large-scale triplet dataset consisting of content images, style images, and their corresponding stylized content images. Second, we introduce a disentangled learning scheme that simultaneously aligns style features and disentangles content from style through two complementary objectives, style-alignment training and content–style disentanglement training. Third, we incorporate a style reward-learning paradigm to further enhance the model’s performance. ## ⚡️ Quick Start ### 🔧 Requirements and Installation Install the requirements ```bash ## create a virtual environment with python >= 3.10 <= 3.12, like python -m venv uso_env source uso_env/bin/activate ## or conda create -n uso_env python=3.10 -y conda activate uso_env ## then install the requirements by you need pip install -r requirements.txt # legacy installation command ``` Then download checkpoints in one of the following ways: - Suppose you already have some of the checkpoints ```bash # 1. download USO official checkpoints pip install huggingface_hub huggingface-cli download bytedance-research/USO --local-dir <YOU_SAVE_DIR> --local-dir-use-symlinks False # 2. Then set the environment variable for FLUX.1 base model export AE="YOUR_AE_PATH" export FLUX_DEV="YOUR_FLUX_DEV_PATH" export T5="YOUR_T5_PATH" export CLIP="YOUR_CLIP_PATH" # or export HF_HOME="YOUR_HF_HOME" # 3. Then set the environment variable for USO export LORA="<YOU_SAVE_DIR>/uso_flux_v1.0/dit_lora.safetensors" export PROJECTION_MODEL="<YOU_SAVE_DIR>/uso_flux_v1.0/projector.safetensors" ``` - Directly run the inference scripts, the checkpoints will be downloaded automatically by the `hf_hub_download` function in the code. ### ✍️ Inference Start from the examples below to explore and spark your creativity. ✨ ```bash # the first image is a content reference, and the rest are style references. # for subject-driven generation python inference.py --prompt "The man in flower shops carefully match bouquets, conveying beautiful emotions and blessings with flowers. " --image_paths "assets/gradio_examples/identity1.jpg" --width 1024 --height 1024 # for style-driven generation # please keep the first image path empty python inference.py --prompt "A cat sleeping on a chair." --image_paths "" "assets/gradio_examples/style1.webp" --width 1024 --height 1024 # for ip-style generation python inference.py --prompt "The woman gave an impassioned speech on the podium." --image_paths "assets/gradio_examples/identity2.webp" "assets/gradio_examples/style2.webp" --width 1024 --height 1024 # for multi-style generation # please keep the first image path empty python inference.py --prompt "A handsome man." --image_paths "" "assets/gradio_examples/style3.webp" "assets/gradio_examples/style4.webp" --width 1024 --height 1024 ``` ## 📄 Disclaimer <p> We open-source this project for academic research. The vast majority of images used in this project are either generated or from open-source datasets. If you have any concerns, please contact us, and we will promptly remove any inappropriate content. Our project is released under the Apache 2.0 License. If you apply to other base models, please ensure that you comply with the original licensing terms. <br><br>This research aims to advance the field of generative AI. Users are free to create images using this tool, provided they comply with local laws and exercise responsible usage. The developers are not liable for any misuse of the tool by users.</p> ## Citation We also appreciate it if you could give a star ⭐ to our [Github repository](https://github.com/bytedance/USO). Thanks a lot! If you find this project useful for your research, please consider citing our paper: ```bibtex @article{wu2025uso, title={USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning}, author={Shaojin Wu and Mengqi Huang and Yufeng Cheng and Wenxu Wu and Jiahe Tian and Yiming Luo and Fei Ding and Qian He}, year={2025}, eprint={2508.18966}, archivePrefix={arXiv}, primaryClass={cs.CV}, } ```
dr-wong-lu-yang-cctv-Viral-videoss/New.full.videos.Dr.wong.Viral.Video.Official.Tutorial	dr-wong-lu-yang-cctv-Viral-videoss	2025-08-27T15:26:12Z	0	0	null	[ "region:us" ]	null	2025-08-27T15:25:42Z	<animated-image data-catalyst=""><a href="https://fubotv24.com/Leaked/?v=video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
PaddlePaddle/PP-DocBee-7B	PaddlePaddle	2025-08-27T15:25:46Z	15	1	PaddleOCR	[ "PaddleOCR", "paddlepaddle", "qwen2_vl", "OCR", "PaddlePaddle", "doc_vlm", "image-to-text", "en", "zh", "license:apache-2.0", "region:us" ]	image-to-text	2025-06-06T04:10:39Z	--- license: apache-2.0 library_name: PaddleOCR language: - en - zh pipeline_tag: image-to-text tags: - OCR - PaddlePaddle - PaddleOCR - doc_vlm --- # PP-DocBee-7B ## Introduction The PaddleOCR team has developed PP-DocBee-7B, a multimodal large model focusing on document understanding, and it performs excellently in Chinese document understanding tasks. The model is fine-tuned and optimized using nearly 5 million multimodal datasets for document understanding, including general VQA, OCR, charts, text-rich documents, mathematics and complex reasoning, synthetic data, and pure text data, with different training data ratios set. On several authoritative English document understanding evaluation lists in academia, PP-DocBee has basically achieved SOTA for models of the same parameter scale. In terms of internal business Chinese scenario indicators, PP-DocBee also outperforms the current popular open-source and closed-source models. The key accuracy metrics are as follow: \| Model \| Model Storage Size(GB) \| Total Score \| \|-------\|--------------------------\|-----------\| \| PP-DocBee-2B \| 4.2 \| 765 \| \| PP-DocBee-7B \| 15.8 \| - \| Note: The total scores of the above models are test results from an internal evaluation set, where all images have a resolution (height, width) of (1680, 1204), with a total of 1196 data entries, covering scenarios such as financial reports, laws and regulations, scientific and technical papers, manuals, humanities papers, contracts, research reports, etc. There are no plans for public release at the moment. ## Quick Start ### Installation 1. PaddlePaddle Please refer to the following commands to install PaddlePaddle using pip: ```bash # for CUDA11.8 python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ # for CUDA12.6 python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/ # for CPU python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ ``` For details about PaddlePaddle installation, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/en/install/quick). 2. PaddleOCR Install the latest version of the PaddleOCR inference package from PyPI: ```bash python -m pip install paddleocr ``` ### Model Usage You can quickly experience the functionality with a single command: ```bash paddleocr doc_vlm \ --model_name PP-DocBee-7B \ -i "{'image': 'https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png', 'query': 'Recognize the content of this table and output it in markdown format.'}" ``` You can also integrate the model inference of the document visual-language module into your project. Before running the following code, please download the sample image to your local machine. ```python from paddleocr import DocVLM model = DocVLM(model_name="PP-DocBee-7B") results = model.predict( input={ "image": "https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png", "query": "Recognize the content of this table and output it in markdown format." }, batch_size=1 ) for res in results: res.print() res.save_to_json(f"./output/res.json") ``` After running, the obtained result is as follows: ```bash {'res': {'image': 'medal_table_en.png', 'query': 'Recognize the content of this table and output it in markdown format', 'result': '\| Rank \| Country/Region \| Gold \| Silver \| Bronze \| Total Medals \|\n\|---\|---\|---\|---\|---\|---\|\n\| 1 \| China (CHN) \| 48 \| 22 \| 30 \| 100 \|\n\| 2 \| United States (USA) \| 36 \| 39 \| 37 \| 112 \|\n\| 3 \| Russia (RUS) \| 24 \| 13 \| 23 \| 60 \|\n\| 4 \| Great Britain (GBR) \| 19 \| 13 \| 19 \| 51 \|\n\| 5 \| Germany (GER) \| 16 \| 11 \| 14 \| 41 \|\n\| 6 \| Australia (AUS) \| 14 \| 15 \| 17 \| 46 \|\n\| 7 \| South Korea (KOR) \| 13 \| 11 \| 8 \| 32 \|\n\| 8 \| Japan (JPN) \| 9 \| 8 \| 8 \| 25 \|\n\| 9 \| Italy (ITA) \| 8 \| 9 \| 10 \| 27 \|\n\| 10 \| France (FRA) \| 7 \| 16 \| 20 \| 43 \|\n\| 11 \| Netherlands (NED) \| 7 \| 5 \| 4 \| 16 \|\n\| 12 \| Ukraine (UKR) \| 7 \| 4 \| 11 \| 22 \|\n\| 13 \| Kenya (KEN) \| 6 \| 4 \| 6 \| 16 \|\n\| 14 \| Spain (ESP) \| 5 \| 11 \| 3 \| 19 \|\n\| 15 \| Jamaica (JAM) \| 5 \| 4 \| 2 \| 11 \|\n'}} ``` The visualized result is as follows: ```bash \| Rank \| Country/Region \| Gold \| Silver \| Bronze \| Total Medals \| \|---\|---\|---\|---\|---\|---\| \| 1 \| China (CHN) \| 48 \| 22 \| 30 \| 100 \| \| 2 \| United States (USA) \| 36 \| 39 \| 37 \| 112 \| \| 3 \| Russia (RUS) \| 24 \| 13 \| 23 \| 60 \| \| 4 \| Great Britain (GBR) \| 19 \| 13 \| 19 \| 51 \| \| 5 \| Germany (GER) \| 16 \| 11 \| 14 \| 41 \| \| 6 \| Australia (AUS) \| 14 \| 15 \| 17 \| 46 \| \| 7 \| South Korea (KOR) \| 13 \| 11 \| 8 \| 32 \| \| 8 \| Japan (JPN) \| 9 \| 8 \| 8 \| 25 \| \| 9 \| Italy (ITA) \| 8 \| 9 \| 10 \| 27 \| \| 10 \| France (FRA) \| 7 \| 16 \| 20 \| 43 \| \| 11 \| Netherlands (NED) \| 7 \| 5 \| 4 \| 16 \| \| 12 \| Ukraine (UKR) \| 7 \| 4 \| 11 \| 22 \| \| 13 \| Kenya (KEN) \| 6 \| 4 \| 6 \| 16 \| \| 14 \| Spain (ESP) \| 5 \| 11 \| 3 \| 19 \| \| 15 \| Jamaica (JAM) \| 5 \| 4 \| 2 \| 11 \| ``` For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/doc_vlm.html#iii-quick-start). ### Pipeline Usage The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenarios. #### doc_understanding The document understanding pipeline is an advanced document processing technology based on Visual-Language Models (VLM), designed to overcome the limitations of traditional document processing. And there is only 1 module in the pipeline: * Document Visual Language Module Run a single command to quickly experience the OCR pipeline: ```bash paddleocr doc_understanding -i "{'image': 'https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png', 'query': 'Recognize the content of this table and output it in markdown format.'}" ``` Results are printed to the terminal: ```bash {'res': {'image': 'medal_table_en.png', 'query': 'Recognize the content of this table and output it in markdown format', 'result': '\| Rank \| Country/Region \| Gold \| Silver \| Bronze \| Total Medals \|\n\|---\|---\|---\|---\|---\|---\|\n\| 1 \| China (CHN) \| 48 \| 22 \| 30 \| 100 \|\n\| 2 \| United States (USA) \| 36 \| 39 \| 37 \| 112 \|\n\| 3 \| Russia (RUS) \| 24 \| 13 \| 23 \| 60 \|\n\| 4 \| Great Britain (GBR) \| 19 \| 13 \| 19 \| 51 \|\n\| 5 \| Germany (GER) \| 16 \| 11 \| 14 \| 41 \|\n\| 6 \| Australia (AUS) \| 14 \| 15 \| 17 \| 46 \|\n\| 7 \| South Korea (KOR) \| 13 \| 11 \| 8 \| 32 \|\n\| 8 \| Japan (JPN) \| 9 \| 8 \| 8 \| 25 \|\n\| 9 \| Italy (ITA) \| 8 \| 9 \| 10 \| 27 \|\n\| 10 \| France (FRA) \| 7 \| 16 \| 20 \| 43 \|\n\| 11 \| Netherlands (NED) \| 7 \| 5 \| 4 \| 16 \|\n\| 12 \| Ukraine (UKR) \| 7 \| 4 \| 11 \| 22 \|\n\| 13 \| Kenya (KEN) \| 6 \| 4 \| 6 \| 16 \|\n\| 14 \| Spain (ESP) \| 5 \| 11 \| 3 \| 19 \|\n\| 15 \| Jamaica (JAM) \| 5 \| 4 \| 2 \| 11 \|\n'}} ``` If save_path is specified, the visualization results will be saved under `save_path`. The visualization output is shown below: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/kFGo9nlHuHs2uyN1voSTg.png) The command-line method is for quick experience. For project integration, also only a few codes are needed as well: ```python from paddleocr import DocUnderstanding pipeline = DocUnderstanding( doc_understanding_model_name="PP-DocBee-7B" ) output = pipeline.predict( { "image": "https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png", "query": "Recognize the content of this table and output it in markdown format." } ) for res in output: res.print() ## Print the structured output of the prediction res.save_to_json("./output/") ``` The default model used in pipeline is `PP-DocBee2-3B`, so you need to specify `doc_understanding_model_name` to `PP-DocBee-7B`. And you can also use the local model file by argument `doc_understanding_model_dir`. For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/pipeline_usage/doc_understanding.html#2-quick-start). ## Links [PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr) [PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html)
original-dr-wong-lu-yang-cctv-Viral-video/New.full.videos.Dr.wong.Viral.Video.Official.Tutorial	original-dr-wong-lu-yang-cctv-Viral-video	2025-08-27T15:25:46Z	0	0	null	[ "region:us" ]	null	2025-08-27T15:25:26Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Nikhil058/Reinforce_Pixelcopter_PLE_V0	Nikhil058	2025-08-27T15:25:07Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-08-27T15:25:03Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce_Pixelcopter_PLE_V0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 29.50 +/- 30.53 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
PaddlePaddle/PP-DocBee2-3B	PaddlePaddle	2025-08-27T15:25:06Z	182	0	PaddleOCR	[ "PaddleOCR", "paddlepaddle", "qwen2_5_vl", "OCR", "PaddlePaddle", "doc_vlm", "image-to-text", "en", "zh", "license:apache-2.0", "region:us" ]	image-to-text	2025-06-06T03:27:39Z	--- license: apache-2.0 library_name: PaddleOCR language: - en - zh pipeline_tag: image-to-text tags: - OCR - PaddlePaddle - PaddleOCR - doc_vlm --- # PP-DocBee2-3B ## Introduction The PaddleOCR team has developed PP-DocBee2-3B, a multimodal large model that significantly enhances Chinese document understanding. Building upon the original PP-DocBee, this new iteration introduces an improved data optimization scheme that boosts data quality. PP-DocBee2 achieves superior performance in Chinese document understanding tasks by leveraging a relatively small dataset of 470,000 synthetic data points, generated through a proprietary data synthesis strategy. Internally, PP-DocBee2 demonstrates an impressive 11.4% improvement over its predecessor, PP-DocBee, in Chinese business scenario metrics. Furthermore, it outperforms other popular open-source and closed-source models of comparable scale in key accuracy metrics. The key accuracy metrics are as follow: \| Model \| Model Storage Size(GB) \| Total Score \| \|-------\|--------------------------\|-----------\| \| PP-DocBee-2B \| 4.2 \| 765 \| \| PP-DocBee-7B \| 15.8 \| - \| \| PP-DocBee2-3B \| 7.6 \| 852 \| Note: The total scores of the above models are test results from an internal evaluation set, where all images have a resolution (height, width) of (1680, 1204), with a total of 1196 data entries, covering scenarios such as financial reports, laws and regulations, scientific and technical papers, manuals, humanities papers, contracts, research reports, etc. There are no plans for public release at the moment. ## Quick Start ### Installation 1. PaddlePaddle Please refer to the following commands to install PaddlePaddle using pip: ```bash # for CUDA11.8 python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ # for CUDA12.6 python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/ # for CPU python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ ``` For details about PaddlePaddle installation, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/en/install/quick). 2. PaddleOCR Install the latest version of the PaddleOCR inference package from PyPI: ```bash python -m pip install paddleocr ``` ### Model Usage You can quickly experience the functionality with a single command: ```bash paddleocr doc_vlm \ --model_name PP-DocBee2-3B \ -i "{'image': 'https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png', 'query': 'Recognize the content of this table and output it in markdown format.'}" ``` You can also integrate the model inference of the document visual-language module into your project. Before running the following code, please download the sample image to your local machine. ```python from paddleocr import DocVLM model = DocVLM(model_name="PP-DocBee2-3B") results = model.predict( input={ "image": "https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png", "query": "Recognize the content of this table and output it in markdown format." }, batch_size=1 ) for res in results: res.print() res.save_to_json(f"./output/res.json") ``` After running, the obtained result is as follows: ```bash {'res': {'image': 'medal_table_en.png', 'query': 'Recognize the content of this table and output it in markdown format', 'result': '\| Rank \| Country/Region \| Gold \| Silver \| Bronze \| Total Medals \|\n\|---\|---\|---\|---\|---\|---\|\n\| 1 \| China (CHN) \| 48 \| 22 \| 30 \| 100 \|\n\| 2 \| United States (USA) \| 36 \| 39 \| 37 \| 112 \|\n\| 3 \| Russia (RUS) \| 24 \| 13 \| 23 \| 60 \|\n\| 4 \| Great Britain (GBR) \| 19 \| 13 \| 19 \| 51 \|\n\| 5 \| Germany (GER) \| 16 \| 11 \| 14 \| 41 \|\n\| 6 \| Australia (AUS) \| 14 \| 15 \| 17 \| 46 \|\n\| 7 \| South Korea (KOR) \| 13 \| 11 \| 8 \| 32 \|\n\| 8 \| Japan (JPN) \| 9 \| 8 \| 8 \| 25 \|\n\| 9 \| Italy (ITA) \| 8 \| 9 \| 10 \| 27 \|\n\| 10 \| France (FRA) \| 7 \| 16 \| 20 \| 43 \|\n\| 11 \| Netherlands (NED) \| 7 \| 5 \| 4 \| 16 \|\n\| 12 \| Ukraine (UKR) \| 7 \| 4 \| 11 \| 22 \|\n\| 13 \| Kenya (KEN) \| 6 \| 4 \| 6 \| 16 \|\n\| 14 \| Spain (ESP) \| 5 \| 11 \| 3 \| 19 \|\n\| 15 \| Jamaica (JAM) \| 5 \| 4 \| 2 \| 11 \|\n'}} ``` The visualized result is as follows: ```bash \| Rank \| Country/Region \| Gold \| Silver \| Bronze \| Total Medals \| \|---\|---\|---\|---\|---\|---\| \| 1 \| China (CHN) \| 48 \| 22 \| 30 \| 100 \| \| 2 \| United States (USA) \| 36 \| 39 \| 37 \| 112 \| \| 3 \| Russia (RUS) \| 24 \| 13 \| 23 \| 60 \| \| 4 \| Great Britain (GBR) \| 19 \| 13 \| 19 \| 51 \| \| 5 \| Germany (GER) \| 16 \| 11 \| 14 \| 41 \| \| 6 \| Australia (AUS) \| 14 \| 15 \| 17 \| 46 \| \| 7 \| South Korea (KOR) \| 13 \| 11 \| 8 \| 32 \| \| 8 \| Japan (JPN) \| 9 \| 8 \| 8 \| 25 \| \| 9 \| Italy (ITA) \| 8 \| 9 \| 10 \| 27 \| \| 10 \| France (FRA) \| 7 \| 16 \| 20 \| 43 \| \| 11 \| Netherlands (NED) \| 7 \| 5 \| 4 \| 16 \| \| 12 \| Ukraine (UKR) \| 7 \| 4 \| 11 \| 22 \| \| 13 \| Kenya (KEN) \| 6 \| 4 \| 6 \| 16 \| \| 14 \| Spain (ESP) \| 5 \| 11 \| 3 \| 19 \| \| 15 \| Jamaica (JAM) \| 5 \| 4 \| 2 \| 11 \| ``` For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/doc_vlm.html#iii-quick-start). ### Pipeline Usage The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenarios. #### doc_understanding The document understanding pipeline is an advanced document processing technology based on Visual-Language Models (VLM), designed to overcome the limitations of traditional document processing. And there is only 1 module in the pipeline: * Document Visual Language Module Run a single command to quickly experience the OCR pipeline: ```bash paddleocr doc_understanding -i "{'image': 'https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png', 'query': 'Recognize the content of this table and output it in markdown format.'}" ``` Results are printed to the terminal: ```json {'res': {'image': 'medal_table_en.png', 'query': 'Recognize the content of this table and output it in markdown format', 'result': '\| Rank \| Country/Region \| Gold \| Silver \| Bronze \| Total Medals \|\n\|---\|---\|---\|---\|---\|---\|\n\| 1 \| China (CHN) \| 48 \| 22 \| 30 \| 100 \|\n\| 2 \| United States (USA) \| 36 \| 39 \| 37 \| 112 \|\n\| 3 \| Russia (RUS) \| 24 \| 13 \| 23 \| 60 \|\n\| 4 \| Great Britain (GBR) \| 19 \| 13 \| 19 \| 51 \|\n\| 5 \| Germany (GER) \| 16 \| 11 \| 14 \| 41 \|\n\| 6 \| Australia (AUS) \| 14 \| 15 \| 17 \| 46 \|\n\| 7 \| South Korea (KOR) \| 13 \| 11 \| 8 \| 32 \|\n\| 8 \| Japan (JPN) \| 9 \| 8 \| 8 \| 25 \|\n\| 9 \| Italy (ITA) \| 8 \| 9 \| 10 \| 27 \|\n\| 10 \| France (FRA) \| 7 \| 16 \| 20 \| 43 \|\n\| 11 \| Netherlands (NED) \| 7 \| 5 \| 4 \| 16 \|\n\| 12 \| Ukraine (UKR) \| 7 \| 4 \| 11 \| 22 \|\n\| 13 \| Kenya (KEN) \| 6 \| 4 \| 6 \| 16 \|\n\| 14 \| Spain (ESP) \| 5 \| 11 \| 3 \| 19 \|\n\| 15 \| Jamaica (JAM) \| 5 \| 4 \| 2 \| 11 \|\n'}} ``` If save_path is specified, the visualization results will be saved under `save_path`. The visualization output is shown below: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/kFGo9nlHuHs2uyN1voSTg.png) The command-line method is for quick experience. For project integration, also only a few codes are needed as well: ```python from paddleocr import DocUnderstanding pipeline = DocUnderstanding( doc_understanding_model_name="PP-DocBee2-3B" ) output = pipeline.predict( { "image": "https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png", "query": "Recognize the content of this table and output it in markdown format." } ) for res in output: res.print() ## Print the structured output of the prediction res.save_to_json("./output/") ``` The default model used in pipeline is `PP-DocBee2-3B`, so you don't have to specify `PP-DocBee2-3B` for the `doc_understanding_model_name argument`, but you can use the local model file by argument `doc_understanding_model_dir`. For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/pipeline_usage/doc_understanding.html#2-quick-start). ## Links [PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr) [PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html)
thorejaya/omega_EbUtNMG	thorejaya	2025-08-27T15:25:03Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-27T15:25:02Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
mang3dd/blockassist-bc-tangled_slithering_alligator_1756306728	mang3dd	2025-08-27T15:24:47Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:24:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
OksanaB/blockassist-bc-huge_ferocious_chameleon_1756308176	OksanaB	2025-08-27T15:24:24Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:23:31Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge ferocious chameleon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
1yuuuna/output	1yuuuna	2025-08-27T15:24:08Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:kisti/korscideberta", "base_model:finetune:kisti/korscideberta", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-27T15:12:15Z	--- library_name: transformers license: mit base_model: kisti/korscideberta tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: output 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. --> # output This model is a fine-tuned version of [kisti/korscideberta](https://huggingface.co/kisti/korscideberta) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1835 - Accuracy: 0.9280 - Precision: 0.9280 - Recall: 0.9280 - F1: 0.9279 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.4034 \| 0.3188 \| 500 \| 0.3371 \| 0.8543 \| 0.8737 \| 0.8543 \| 0.8538 \| \| 0.2338 \| 0.6376 \| 1000 \| 0.1964 \| 0.9159 \| 0.9159 \| 0.9159 \| 0.9159 \| \| 0.2079 \| 0.9563 \| 1500 \| 0.1719 \| 0.9322 \| 0.9323 \| 0.9322 \| 0.9323 \| ### Framework versions - Transformers 4.45.0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.20.3
Jack-Payne1/gemma-3-4b-it-good-doctor-seed3	Jack-Payne1	2025-08-27T15:24:02Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "base_model:unsloth/gemma-3-4b-it", "base_model:finetune:unsloth/gemma-3-4b-it", "endpoints_compatible", "region:us" ]	null	2025-08-27T15:08:34Z	--- base_model: unsloth/gemma-3-4b-it library_name: transformers model_name: gemma-3-4b-it-good-doctor-seed3 tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for gemma-3-4b-it-good-doctor-seed3 This model is a fine-tuned version of [unsloth/gemma-3-4b-it](https://huggingface.co/unsloth/gemma-3-4b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Jack-Payne1/gemma-3-4b-it-good-doctor-seed3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jacktpayne51-macquarie-university/clarifying-em/runs/xi8pi28b) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.4 - Pytorch: 2.7.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
acidjp/blockassist-bc-pesty_extinct_prawn_1756305914	acidjp	2025-08-27T15:23:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pesty extinct prawn", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:23:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pesty extinct prawn --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
2hpsatt/blockassist-bc-huge_deft_eagle_1756307978	2hpsatt	2025-08-27T15:21:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge deft eagle", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:21:00Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - huge deft eagle --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sunrunner79hot1/blockassist-bc-bold_noisy_woodpecker_1756306480	sunrunner79hot1	2025-08-27T15:19:55Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bold noisy woodpecker", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:19:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bold noisy woodpecker --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
bodigardehotma1/blockassist-bc-spotted_mimic_giraffe_1756306328	bodigardehotma1	2025-08-27T15:19:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted mimic giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:19:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted mimic giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756307942	Ferdi3425	2025-08-27T15:19:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:19:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
liukevin666/blockassist-bc-yawning_striped_cassowary_1756307829	liukevin666	2025-08-27T15:19:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:18:08Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
runchat/lora-06654833-d1fd-47fb-ba7d-dd8fb13ca165-w36ous	runchat	2025-08-27T15:18:08Z	0	0	diffusers	[ "diffusers", "flux", "lora", "text-to-image", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-27T15:18:04Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md base_model: black-forest-labs/FLUX.1-dev tags: - flux - lora - diffusers - text-to-image widget: - text: 'a photo of a VICTORIA DETAILS style' output: url: "placeholder.jpg" --- # Flux LoRA: VICTORIA DETAILS This is a LoRA (Low-Rank Adaptation) model for Flux.1-dev fine-tuned on images with the trigger word `VICTORIA DETAILS`. ## Files - `pytorch_lora_weights.safetensors`: Diffusers format (use with diffusers library) - `pytorch_lora_weights_webui.safetensors`: Kohya format (use with AUTOMATIC1111, ComfyUI, etc.) ## Usage ### Diffusers Library ```python from diffusers import FluxPipeline import torch # Load base model pipe = FluxPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16 ) # Load LoRA weights (diffusers format) pipe.load_lora_weights("runchat/lora-06654833-d1fd-47fb-ba7d-dd8fb13ca165-w36ous", weight_name="pytorch_lora_weights.safetensors") pipe = pipe.to("cuda") # Generate image prompt = "a photo of a VICTORIA DETAILS style" image = pipe(prompt, num_inference_steps=50, guidance_scale=3.5).images[0] image.save("output.png") ``` ### WebUI (AUTOMATIC1111, ComfyUI, etc.) Download the `pytorch_lora_weights_webui.safetensors` file and place it in your WebUI's LoRA directory. Use the trigger word `VICTORIA DETAILS` in your prompts. ## Training Details - Base model: black-forest-labs/FLUX.1-dev - Training steps: 500 - Learning rate: 0.001 - Batch size: 2 - LoRA rank: 16 - Trigger word: `VICTORIA DETAILS` ## License This model is trained on Flux.1-dev and inherits its non-commercial license. Please see the [license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md) for usage restrictions.
yeok/sft-Qwen2-5-3B-Instruct-user_bias-200000	yeok	2025-08-27T15:17:11Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/Qwen2.5-3B-Instruct", "base_model:finetune:unsloth/Qwen2.5-3B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-27T15:17:05Z	--- base_model: unsloth/Qwen2.5-3B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: yeok - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-3B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756307730	Ferdi3425	2025-08-27T15:15:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:15:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lakelee/RLB_MLP_BC_v3.20250827.22_5	lakelee	2025-08-27T15:15:25Z	0	0	transformers	[ "transformers", "safetensors", "mlp_swiglu", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2025-08-27T13:43:00Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: RLB_MLP_BC_v3.20250827.22_5 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. --> # RLB_MLP_BC_v3.20250827.22_5 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch_fused with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 20.0 ### Training results ### Framework versions - Transformers 4.55.4 - Pytorch 2.8.0+cu128 - Tokenizers 0.21.4
SVBilenko/dqn-SpaceInvadersNoFrameskip-v4	SVBilenko	2025-08-27T15:15:18Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-08-27T15:14:49Z	--- 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: 625.00 +/- 264.58 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 SVBilenko -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 SVBilenko -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 SVBilenko ``` ## 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', 0.0001), ('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'} ```
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756307553	ggozzy	2025-08-27T15:13:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:13:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
xinnn32/blockassist-bc-meek_winged_caterpillar_1756307565	xinnn32	2025-08-27T15:13:21Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:13:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - meek winged caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756307497	Ferdi3425	2025-08-27T15:12:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:12:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
alestrami/AutoNerf-8B_init	alestrami	2025-08-27T15:10:03Z	0	0	null	[ "safetensors", "en", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "region:us" ]	null	2025-08-27T09:29:15Z	--- language: - en base_model: - meta-llama/Llama-3.1-8B ---
liukevin666/blockassist-bc-yawning_striped_cassowary_1756307171	liukevin666	2025-08-27T15:07:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:07:09Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hnv2520/LNG_Qwen2.5VL_32B_150st_4b	hnv2520	2025-08-27T15:07:23Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-to-text	2025-08-27T14:25:38Z	--- base_model: unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_5_vl license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: hnv2520 - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit This qwen2_5_vl 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)
unitova/blockassist-bc-zealous_sneaky_raven_1756305398	unitova	2025-08-27T15:07:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:07:11Z	--- 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).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756307057	ggozzy	2025-08-27T15:05:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:05:19Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ChenWu98/statement_deepseek_v1.5_sft_cluster_weighted_alpha2.0_split_1	ChenWu98	2025-08-27T15:05:18Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:deepseek-ai/DeepSeek-Prover-V1.5-SFT", "base_model:finetune:deepseek-ai/DeepSeek-Prover-V1.5-SFT", "endpoints_compatible", "region:us" ]	null	2025-08-27T14:55:56Z	--- base_model: deepseek-ai/DeepSeek-Prover-V1.5-SFT library_name: transformers model_name: statement_deepseek_v1.5_sft_cluster_weighted_alpha2.0_split_1 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for statement_deepseek_v1.5_sft_cluster_weighted_alpha2.0_split_1 This model is a fine-tuned version of [deepseek-ai/DeepSeek-Prover-V1.5-SFT](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V1.5-SFT). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/chenwu/huggingface/runs/8fukhhjm) This model was trained with SFT. ### Framework versions - TRL: 0.19.1 - Transformers: 4.51.1 - Pytorch: 2.7.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
whaoyang/gemma-3-4b-novision-quant-rk3588-1.2.1	whaoyang	2025-08-27T15:05:16Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "conversational", "base_model:gghfez/gemma-3-4b-novision", "base_model:finetune:gghfez/gemma-3-4b-novision", "license:gemma", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-27T14:45:02Z	--- license: gemma base_model: - gghfez/gemma-3-4b-novision - google/gemma-3-4b-it library_name: transformers --- This version of gemma-3-4b-novision has been converted to run on the RK3588 NPU using w8a8 quantization and with the [quant_dataset.json](https://huggingface.co/whaoyang/gemma-3-4b-novision-quant-rk3588-1.2.1/blob/main/quant_dataset.json) file from this repo as the value for the dataset param of the `rkllm.build()` command. This converted rkllm model differs from my other `gemma-3-4b-novision-rk3588-1.2.1` model in that `rkllm.build()` was run with param `dataset=quant_dataset.json` instead of param `dataset=None` in the other model. The `quant_dataset.json` file was generated with Rockchip's [generate_data_quant.py](https://github.com/airockchip/rknn-llm/blob/release-v1.2.1/examples/DeepSeek-R1-Distill-Qwen-1.5B_Demo/export/generate_data_quant.py) script with the following arguments: * max_new_tokens = 448 * top_k = 64 * temperature = 0.7 * repetition_penalty = 1.0 * apply_chat_template = True This model has been optimized with the following LoRA: NA This model supports a max context length of 16384. Compatible with RKLLM version: 1.2.1 ## Recommended `rkllm` parameters This model runs well in limited testing with the following `rkllm` library paremeters: * `n_keep` = -1 * `top_k` = 64 * `top_p` = 0.95 * `temperature` = 0.7 * `repeat_penalty` = 1.0 * `frequency_penalty` = 1.0 * `presence_penalty` = 0.0 * `mirostat` = 0 * `mirostat_tau` = 5.0 * `mirostat_eta` = 0.1 It is recommended to also apply a specific chat template using the following Python methods(that hook the rkllm library): ``` # System prompt taken from https://docs.unsloth.ai/basics/gemma-3-how-to-run-and-fine-tune#official-recommended-inference-settings system_prompt = "<bos><start_of_turn>user\nHello!<end_of_turn>\n<start_of_turn>model\nHey there!<end_of_turn>\n<start_of_turn>user\nWhat is 1+1?<end_of_turn>\n<start_of_turn>model\n" prompt_prefix = "<start_of_turn>user\n" prompt_postfix = "<end_of_turn>\n<start_of_turn>model\n" rkllm_lib.rkllm_set_chat_template( llm_handle, ctypes.c_char_p(system_prompt.encode('utf-8')), ctypes.c_char_p(prompt_prefix.encode('utf-8')), ctypes.c_char_p(prompt_postfix.encode('utf-8'))) ``` ## 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
Dejiat/blockassist-bc-savage_unseen_bobcat_1756307014	Dejiat	2025-08-27T15:03:59Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:03:56Z	--- 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).
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1756305455	helmutsukocok	2025-08-27T15:02:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:02:42Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - loud scavenging kangaroo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756306806	ggozzy	2025-08-27T15:01:17Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T15:01:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
leonMW/DeepSeek-R1-Distill-Qwen-7B-GSPO-Basic	leonMW	2025-08-27T15:01:00Z	237	1	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "grpo", "trl", "conversational", "arxiv:2402.03300", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-09T19:03:32Z	--- base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B library_name: transformers model_name: DeepSeek-R1-Distill-Qwen-7B-GSPO-Basic tags: - generated_from_trainer - grpo - trl licence: license --- # Model Card for DeepSeek-R1-Distill-Qwen-7B-GSPO-Basic This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="leonMW/DeepSeek-R1-Distill-Qwen-7B-GSPO-Basic", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/leonwenderoth-tu-darmstadt/huggingface/runs/9vbuyz3z) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.0 - Pytorch: 2.6.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/smollm2-1.7b-orca-GGUF	mradermacher	2025-08-27T15:00:58Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:ketchup123/smollm2-1.7b-orca", "base_model:quantized:ketchup123/smollm2-1.7b-orca", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-27T14:35:44Z	--- base_model: ketchup123/smollm2-1.7b-orca 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/ketchup123/smollm2-1.7b-orca <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#smollm2-1.7b-orca-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/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q2_K.gguf) \| Q2_K \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q3_K_S.gguf) \| Q3_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q3_K_M.gguf) \| Q3_K_M \| 1.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q3_K_L.gguf) \| Q3_K_L \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.IQ4_XS.gguf) \| IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q4_K_S.gguf) \| Q4_K_S \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q4_K_M.gguf) \| Q4_K_M \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q5_K_S.gguf) \| Q5_K_S \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q5_K_M.gguf) \| Q5_K_M \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q6_K.gguf) \| Q6_K \| 1.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q8_0.gguf) \| Q8_0 \| 1.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.f16.gguf) \| f16 \| 3.5 \| 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 -->
Cholan1986/my_policy	Cholan1986	2025-08-27T15:00:47Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "act", "dataset:Cholan1986/record-redcube", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-27T14:59:10Z	--- datasets: Cholan1986/record-redcube library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - lerobot - robotics - act --- # Model Card for act <!-- Provide a quick summary of what the model is/does. --> [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
koloni/blockassist-bc-deadly_graceful_stingray_1756305136	koloni	2025-08-27T14:59:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:59:11Z	--- 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).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756306658	Ferdi3425	2025-08-27T14:58:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:58:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hflqf88888/SWIRL_MATH	hflqf88888	2025-08-27T14:57:57Z	0	0	null	[ "safetensors", "dataset:hflqf88888/SWIRL_MATH_data", "base_model:Qwen/Qwen2.5-Coder-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-3B-Instruct", "license:cc-by-4.0", "region:us" ]	null	2025-08-25T03:04:43Z	--- license: cc-by-4.0 datasets: - hflqf88888/SWIRL_MATH_data base_model: - Qwen/Qwen2.5-Coder-3B-Instruct --- The instantiation of SWIRL's dual-agent architecture in math reasoning. The Teacher provides a concise outline of the problem-solving approach, while the Student generates the final solution by following this guidance. This separation of roles enables structured reasoning and improves overall solution quality. For more details, please refer to our [project repository](https://github.com/Lqf-HFNJU/SWIRL).
liukevin666/blockassist-bc-yawning_striped_cassowary_1756306503	liukevin666	2025-08-27T14:56:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:55:55Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
xinnn32/blockassist-bc-meek_winged_caterpillar_1756306469	xinnn32	2025-08-27T14:55:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:54:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - meek winged caterpillar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
boahancock/blockassist-bc-iridescent_rapid_toad_1756306118	boahancock	2025-08-27T14:54:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "iridescent rapid toad", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:50:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - iridescent rapid toad --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Member42/blockassist-bc-pensive_agile_macaque_1756306369	Member42	2025-08-27T14:54:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pensive agile macaque", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:54:05Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pensive agile macaque --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756306330	Ferdi3425	2025-08-27T14:52:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:52:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mang3dd/blockassist-bc-tangled_slithering_alligator_1756304704	mang3dd	2025-08-27T14:51:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:51:34Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dejiat/blockassist-bc-savage_unseen_bobcat_1756306221	Dejiat	2025-08-27T14:50:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:50:45Z	--- 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).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756306207	Ferdi3425	2025-08-27T14:50:38Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:50:35Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
WillLedd/q-FrozenLake-v1-4x4-noSlippery	WillLedd	2025-08-27T14:49:48Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-08-27T14:49:42Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="WillLedd/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
lautan/blockassist-bc-gentle_patterned_goat_1756304356	lautan	2025-08-27T14:47:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle patterned goat", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:47:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle patterned goat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
raj4536/blockassist-bc-thick_amphibious_meerkat_1756305951	raj4536	2025-08-27T14:46:50Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thick amphibious meerkat", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:46:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thick amphibious meerkat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756305902	Ferdi3425	2025-08-27T14:45:36Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:45:25Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
byzadgan/blockassist-bc-omnivorous_bold_wombat_1756303985	byzadgan	2025-08-27T14:45:16Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "omnivorous bold wombat", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:45:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - omnivorous bold wombat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
elliepreed/bgpt-french-english	elliepreed	2025-08-27T14:44:16Z	0	0	null	[ "pytorch", "safetensors", "gpt2", "fr", "en", "region:us" ]	null	2025-08-27T14:16:16Z	--- language: - fr - en --- library_name: transformers tags: - gpt2 - causal-lm - bilingual - sentencepiece - french - english pipeline_tag: text-generation datasets: - climb-mao/babylm-fra - elliepreed/l2-corpus-10m license: other # change to "apache-2.0" or "mit" if that's correct model-index: - name: BGPT (French+English) – 128k steps results: [] --- # BGPT – French + English (GPT-2 style) Small bilingual GPT-2–style language model trained on French and English with SentencePiece tokenizers. This model is trained on both French 🇫🇷 and English 🇬🇧, but it does not come with a single `AutoTokenizer`. Instead, we provide two SentencePiece tokenizers: - `tokenizers/french.model` - `tokenizers/english.model` You can load either depending on the language you want to work with. # Load the model from transformers import AutoModelForCausalLM import torch model_id = "elliepreed/bgpt-french-english" device = "cuda" if torch.cuda.is_available() else "cpu" model = AutoModelForCausalLM.from_pretrained(model_id).to(device).eval() # Load both tokenizers import sentencepiece as spm from huggingface_hub import hf_hub_download fr_path = hf_hub_download(model_id, "tokenizers/french.model") en_path = hf_hub_download(model_id, "tokenizers/english.model") sp_fr = spm.SentencePieceProcessor(model_file=fr_path) sp_en = spm.SentencePieceProcessor(model_file=en_path) # Example: French generation prompt = "Paris est" ids = sp_fr.encode(prompt, out_type=int) + [sp_fr.eos_id()] input_ids = torch.tensor([ids], device=device) out = model.generate( input_ids, max_new_tokens=40, do_sample=True, top_p=0.95, temperature=0.9, eos_token_id=sp_fr.eos_id(), pad_token_id=sp_fr.pad_id(), ) print("FR:", sp_fr.decode(out[0].tolist()[len(ids):]))
Member42/blockassist-bc-pensive_agile_macaque_1756305754	Member42	2025-08-27T14:44:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pensive agile macaque", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:43:56Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pensive agile macaque --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
danieall/blockassist-bc-humming_gilded_rabbit_1756303951	danieall	2025-08-27T14:43:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "humming gilded rabbit", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:43:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - humming gilded rabbit --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mahir05/ppo-LunarLander-v2	mahir05	2025-08-27T14:43:41Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-08-27T14:43:23Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 270.84 +/- 18.89 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756305640	Ferdi3425	2025-08-27T14:41:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:41:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - amphibious deadly otter --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sakamotoz/blockassist-bc-silent_shaggy_rabbit_1756303971	sakamotoz	2025-08-27T14:40:53Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silent shaggy rabbit", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:40:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - silent shaggy rabbit --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
demomern/custom_Qwen2.5-1.5B-Instruct	demomern	2025-08-27T14:39:43Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-18T08:07:28Z	--- 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]
Kimz1/act-so100-policy-0827-5	Kimz1	2025-08-27T14:38:41Z	0	0	lerobot	[ "lerobot", "safetensors", "act", "robotics", "dataset:Kimz1/so100-teleop-record-0826-1", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-27T14:38:16Z	--- datasets: Kimz1/so100-teleop-record-0826-1 library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - act - robotics - lerobot --- # Model Card for act <!-- Provide a quick summary of what the model is/does. --> [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python lerobot/scripts/train.py \ --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
alessiodevoto/exp_att_stats_meta-llama_Llama-3.1-8B-Instruct_kmfoda_booksum_100_1000_4	alessiodevoto	2025-08-27T14:36:29Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-08-27T14:36:25Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756305311	ggozzy	2025-08-27T14:36:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:36:13Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
acidjp/blockassist-bc-pesty_extinct_prawn_1756302970	acidjp	2025-08-27T14:35:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pesty extinct prawn", "arxiv:2504.07091", "region:us" ]	null	2025-08-27T14:35:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pesty extinct prawn --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
SelmaNajih001/ModelloGRPOMinstral	SelmaNajih001	2025-08-27T14:34:54Z	0	0	transformers	[ "transformers", "safetensors", "text-generation", "en", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1", "endpoints_compatible", "region:us" ]	text-generation	2025-08-27T11:37:25Z	--- library_name: transformers language: - en base_model: - mistralai/Mistral-7B-Instruct-v0.1 pipeline_tag: text-generation --- # 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]

2fast9furious/fast_stupidity

2fast9furious

2025-08-27T15:56:25Z

0

null

[ "license:cc-by-nd-4.0", "region:us" ]

null

2025-08-27T14:59:58Z

--- license: cc-by-nd-4.0 --- Model type: Transformer Task: text generation Languages: English, chinese

OksanaB/blockassist-bc-huge_ferocious_chameleon_1756309941

OksanaB

2025-08-27T15:53:59Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:53:03Z

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

Sarath3321/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-singing_flapping_narwhal

Sarath3321

2025-08-27T15:53:14Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am singing_flapping_narwhal", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-27T12:13:21Z

--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am singing_flapping_narwhal --- # 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]

granenko/taxi_model

granenko

2025-08-27T15:49:13Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2025-08-27T15:49:08Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi_model results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="granenko/taxi_model", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

18-Milica-y-Angel-David-debut-video-Clips/18.ver.video.milica.y.angel.david.debut.filtrado.clip.viral.completo

18-Milica-y-Angel-David-debut-video-Clips

2025-08-27T15:48:43Z

0

null

[ "region:us" ]

null

2025-08-27T15:46:00Z

<a href="http://landht.com/full-video/?v=milica-angel" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a> <a href="http://landht.com/full-video/?v=milica-angel" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 Viral 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a> <a href="http://landht.com/full-video/?v=milica-angel"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsgd" /></a>

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756309687

Ferdi3425

2025-08-27T15:48:37Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:48:33Z

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

tejals22/blockassist-bc-pale_energetic_monkey_1756300068

tejals22

2025-08-27T15:47:56Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pale energetic monkey", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:47:48Z

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

nightmedia/QiMing-Janus-Rhapsody-dwq6-mlx

nightmedia

2025-08-27T15:47:13Z

0

mlx

[ "mlx", "safetensors", "qwen3", "qwen", "unsloth", "qiming", "qiming-holos", "bagua", "decision-making", "strategic-analysis", "cognitive-architecture", "chat", "lora", "philosophy-driven-ai", "text-generation", "conversational", "zh", "en", "base_model:aifeifei798/QiMing-Janus-Rhapsody", "base_model:adapter:aifeifei798/QiMing-Janus-Rhapsody", "license:apache-2.0", "6-bit", "region:us" ]

text-generation

2025-08-27T14:52:17Z

--- license: apache-2.0 language: - zh - en tags: - qwen - qwen3 - unsloth - qiming - qiming-holos - bagua - decision-making - strategic-analysis - cognitive-architecture - chat - lora - philosophy-driven-ai - mlx pipeline_tag: text-generation base_model: aifeifei798/QiMing-Janus-Rhapsody library_name: mlx --- # QiMing-Janus-Rhapsody-dwq6-mlx This model [QiMing-Janus-Rhapsody-dwq6-mlx](https://huggingface.co/QiMing-Janus-Rhapsody-dwq6-mlx) was converted to MLX format from [aifeifei798/QiMing-Janus-Rhapsody](https://huggingface.co/aifeifei798/QiMing-Janus-Rhapsody) using mlx-lm version **0.26.4**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("QiMing-Janus-Rhapsody-dwq6-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```

MarryMy/blockassist-bc-stocky_energetic_porcupine_1756305804

MarryMy

2025-08-27T15:47:03Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stocky energetic porcupine", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:46:55Z

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

Bheri/labse-en-sa-v1

Bheri

2025-08-27T15:44:12Z

0

1

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:257886", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:sentence-transformers/LaBSE", "base_model:finetune:sentence-transformers/LaBSE", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-08-27T15:43:01Z

--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:257886 - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/LaBSE widget: - source_sentence: 'Karwa Chauth is a festival celebrated by Hindu women of Northern and Western India on the fourth day after Purnima in the month of Kartika. ' sentences: - 'तस्याः युग्मभ्रातुः वंशानुगत-राजकुमारस्य जाक् इत्यस्य निमेषद्वयात् प्राक् सा अजायत। ' - '"तथापि, Internet Explorer नोपयोक्तव्यम् । यतो हि तत् सम्यक् डिस्प्ले न करोति ।"' - 'कर्वा-चौथ् इति उत्सवः उत्तर-पश्चिम-भारतस्य हिन्दु-महिलाभिः कार्तिकमासे पूर्णिमायाः अनन्तरं चतुर्थदिने आचर्यते। ' - source_sentence: '"""And if any man will hurt them, fire proceedeth out of their mouth, and devoureth their enemies: and if any man will hurt them, he must in this manner be killed."""' sentences: - '"C तथा C++ उभयोः मध्येऽपि, इदं समानं मार्गं इम्प्लिमेण्ट् कर्तुमनुसरति ।"' - यदि केचित् तौ हिंसितुं चेष्टन्ते तर्हि तयो र्वदनाभ्याम् अग्नि र्निर्गत्य तयोः शत्रून् भस्मीकरिष्यति। यः कश्चित् तौ हिंसितुं चेष्टते तेनैवमेव विनष्टव्यं। - यवक्रीत उवाच नायं शक्यस्त्वया बड़े महानोघस्तपोधन। अशक्याद् विनिवर्तस्व शक्यमर्थं समारभ॥ - source_sentence: 'It tarnishes in air to produce a whitish oxidized layer on the surface. ' sentences: - उपस्थितानां रत्नानां श्रेष्ठानामर्घहारिणाम्। नादृश्यत परः पारो नापरस्तत्र भारत॥ - 'इदं वायौ कलङ्कितं भवति, येन तले श्वेतवर्णीयं आक्सिडैस्ड्-आस्तरणं निर्मीयते। ' - आचार्येणाभ्यनुज्ञातश्चतुर्णामेकमाश्रमम्। आविमोक्षाच्छरीरस्य सोऽवतिष्ठेद् यथाविधि॥ - source_sentence: 'If you''re planning to fund part or all of your child''s higher education, it''s best to start saving early on. ' sentences: - समयं वाजिमेधस्य विदित्वा पुरुषर्षभः। यथोक्तो धर्मपुत्रेण प्रव्रजन् स्वपुरी प्रति॥ - 'यदि भवान् भवतः सन्ततेः उच्चशिक्षायाः कृते, आंशिकं वा सम्पूर्णं वा शुल्कं दातुम् इच्छति तर्हि तदर्थं पूर्वमेव धनसञ्चयस्य आरम्भः क्षेमकरः भवेत्। ' - '"""तदनन्तरं तेषां सप्तकंसधारिणां सप्तदूतानाम् एक आगत्य मां सम्भाष्यावदत्, अत्रागच्छ, मेदिन्या नरपतयो यया वेश्यया सार्द्धं व्यभिचारकर्म्म कृतवन्तः,"""' - source_sentence: In spite of these, Dhananjaya made Drona's son carless by cutting off the out-stretched bow of his foe with three shafts, killing his driver with a razor like shaft and making away with his banner with three and his four horses with four other shafts. sentences: - तथापि तं प्रस्फुरदात्तकार्मुकं त्रिभिः शरैर्यन्तृशिरः क्षुरेणा हयांश्चतुर्भिश्च पुनस्त्रिभिर्ध्वज धनंजयो द्रौणिरथादपातयत्॥ - एकवारं पूरितं चेत् एतां प्रक्रियां undo कर्तुं न शक्नुमः । - क्रीडां तथा कूर्दनं विना शिक्षा अपूर्णा अस्ति । pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - src2trg_accuracy - trg2src_accuracy - mean_accuracy model-index: - name: SentenceTransformer based on sentence-transformers/LaBSE results: - task: type: translation name: Translation dataset: name: eval en sa type: eval-en-sa metrics: - type: src2trg_accuracy value: 0.944 name: Src2Trg Accuracy - type: trg2src_accuracy value: 0.947 name: Trg2Src Accuracy - type: mean_accuracy value: 0.9455 name: Mean Accuracy --- # SentenceTransformer based on sentence-transformers/LaBSE This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE)  - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity    ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) (3): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ "In spite of these, Dhananjaya made Drona's son carless by cutting off the out-stretched bow of his foe with three shafts, killing his driver with a razor like shaft and making away with his banner with three and his four horses with four other shafts.", 'तथापि तं प्रस्फुरदात्तकार्मुकं त्रिभिः शरैर्यन्तृशिरः क्षुरेणा हयांश्चतुर्भिश्च पुनस्त्रिभिर्ध्वज धनंजयो द्रौणिरथादपातयत्॥', 'क्रीडां तथा कूर्दनं विना शिक्षा अपूर्णा अस्ति ।', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Translation * Dataset: `eval-en-sa` * Evaluated with [<code>TranslationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator) | Metric | Value | |:------------------|:-----------| | src2trg_accuracy | 0.944 | | trg2src_accuracy | 0.947 | | **mean_accuracy** | **0.9455** |   ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 257,886 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 6 tokens</li><li>mean: 31.6 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 40.18 tokens</li><li>max: 128 tokens</li></ul> | * Samples: | sentence_0 | sentence_1 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>It normally connects to port 80 on a computer. </code> | <code>इदं सामान्यतः एकस्मिन् सङ्गणके पोर्ट् ८० इत्यनेन सम्पर्कं साधयति। </code> | | <code>He who gives to a Brahmana a good bed perfumed with fragrant scents, covered with an excellent sheet, and pillows, gets without any effort on his part a beautiful wife, belonging to a respectable family and of agreeable manners.</code> | <code>सुगन्धचित्रास्तरणोपधानं दद्यान्नरो यः शयनं द्विजाय। रूपान्वितां पक्षवती मनोज्ञां भार्यामयत्नोपगतां लभेत् सः।</code> | | <code>By mid-1665, with the fortress at Purandar besieged and near capture, Shivaji was forced to come to terms with Jai Singh. </code> | <code>१६६५ तमवर्षस्य मध्यभागे यावत् पुरन्दरस्थस्य दुर्गस्य परिवेष्टनं कृत्वा, ग्रहणस्य समीपे, शिवाजी जयसिङ्घेन सह सन्धानं कर्तुं बाध्यः अभवत्। </code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `num_train_epochs`: 15 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 15 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | eval-en-sa_mean_accuracy | |:-------:|:------:|:-------------:|:------------------------:| | 0.0310 | 500 | 0.4289 | - | | 0.0620 | 1000 | 0.182 | - | | 0.0931 | 1500 | 0.1405 | - | | 0.1241 | 2000 | 0.1097 | - | | 0.1551 | 2500 | 0.0911 | - | | 0.1861 | 3000 | 0.0791 | - | | 0.2171 | 3500 | 0.0725 | - | | 0.2482 | 4000 | 0.067 | - | | 0.2792 | 4500 | 0.0594 | - | | 0.3102 | 5000 | 0.0629 | - | | 0.3412 | 5500 | 0.0535 | - | | 0.3723 | 6000 | 0.0512 | - | | 0.4033 | 6500 | 0.0456 | - | | 0.4343 | 7000 | 0.0462 | - | | 0.4653 | 7500 | 0.043 | - | | 0.4963 | 8000 | 0.0425 | - | | 0.5274 | 8500 | 0.0412 | - | | 0.5584 | 9000 | 0.0418 | - | | 0.5894 | 9500 | 0.0415 | - | | 0.6204 | 10000 | 0.0409 | - | | 0.6514 | 10500 | 0.04 | - | | 0.6825 | 11000 | 0.032 | - | | 0.7135 | 11500 | 0.0323 | - | | 0.7445 | 12000 | 0.0325 | - | | 0.7755 | 12500 | 0.0355 | - | | 0.8066 | 13000 | 0.0285 | - | | 0.8376 | 13500 | 0.0281 | - | | 0.8686 | 14000 | 0.0289 | - | | 0.8996 | 14500 | 0.033 | - | | 0.9306 | 15000 | 0.0336 | - | | 0.9617 | 15500 | 0.0335 | - | | 0.9927 | 16000 | 0.0278 | - | | 1.0 | 16118 | - | 0.913 | | 1.0237 | 16500 | 0.0312 | - | | 1.0547 | 17000 | 0.0294 | - | | 1.0857 | 17500 | 0.0288 | - | | 1.1168 | 18000 | 0.0287 | - | | 1.1478 | 18500 | 0.0245 | - | | 1.1788 | 19000 | 0.0243 | - | | 1.2098 | 19500 | 0.022 | - | | 1.2408 | 20000 | 0.0266 | - | | 1.2719 | 20500 | 0.0224 | - | | 1.3029 | 21000 | 0.0283 | - | | 1.3339 | 21500 | 0.02 | - | | 1.3649 | 22000 | 0.0212 | - | | 1.3960 | 22500 | 0.0197 | - | | 1.4270 | 23000 | 0.0174 | - | | 1.4580 | 23500 | 0.0179 | - | | 1.4890 | 24000 | 0.0187 | - | | 1.5200 | 24500 | 0.0191 | - | | 1.5511 | 25000 | 0.0151 | - | | 1.5821 | 25500 | 0.0161 | - | | 1.6131 | 26000 | 0.0182 | - | | 1.6441 | 26500 | 0.0155 | - | | 1.6751 | 27000 | 0.013 | - | | 1.7062 | 27500 | 0.0119 | - | | 1.7372 | 28000 | 0.0119 | - | | 1.7682 | 28500 | 0.0133 | - | | 1.7992 | 29000 | 0.0113 | - | | 1.8303 | 29500 | 0.011 | - | | 1.8613 | 30000 | 0.0133 | - | | 1.8923 | 30500 | 0.0114 | - | | 1.9233 | 31000 | 0.0139 | - | | 1.9543 | 31500 | 0.0131 | - | | 1.9854 | 32000 | 0.0115 | - | | 2.0 | 32236 | - | 0.9345 | | 2.0164 | 32500 | 0.01 | - | | 2.0474 | 33000 | 0.01 | - | | 2.0784 | 33500 | 0.0091 | - | | 2.1094 | 34000 | 0.0131 | - | | 2.1405 | 34500 | 0.0096 | - | | 2.1715 | 35000 | 0.0095 | - | | 2.2025 | 35500 | 0.0103 | - | | 2.2335 | 36000 | 0.0101 | - | | 2.2645 | 36500 | 0.0102 | - | | 2.2956 | 37000 | 0.0102 | - | | 2.3266 | 37500 | 0.0085 | - | | 2.3576 | 38000 | 0.0087 | - | | 2.3886 | 38500 | 0.0103 | - | | 2.4197 | 39000 | 0.0058 | - | | 2.4507 | 39500 | 0.0086 | - | | 2.4817 | 40000 | 0.0088 | - | | 2.5127 | 40500 | 0.0088 | - | | 2.5437 | 41000 | 0.007 | - | | 2.5748 | 41500 | 0.0082 | - | | 2.6058 | 42000 | 0.0069 | - | | 2.6368 | 42500 | 0.0071 | - | | 2.6678 | 43000 | 0.0058 | - | | 2.6988 | 43500 | 0.0075 | - | | 2.7299 | 44000 | 0.0064 | - | | 2.7609 | 44500 | 0.0053 | - | | 2.7919 | 45000 | 0.0055 | - | | 2.8229 | 45500 | 0.0061 | - | | 2.8540 | 46000 | 0.0059 | - | | 2.8850 | 46500 | 0.0062 | - | | 2.9160 | 47000 | 0.0046 | - | | 2.9470 | 47500 | 0.0064 | - | | 2.9780 | 48000 | 0.0053 | - | | 3.0 | 48354 | - | 0.941 | | 3.0091 | 48500 | 0.0048 | - | | 3.0401 | 49000 | 0.0059 | - | | 3.0711 | 49500 | 0.005 | - | | 3.1021 | 50000 | 0.005 | 0.9415 | | 3.1331 | 50500 | 0.0046 | - | | 3.1642 | 51000 | 0.005 | - | | 3.1952 | 51500 | 0.0051 | - | | 3.2262 | 52000 | 0.0041 | - | | 3.2572 | 52500 | 0.0052 | - | | 3.2882 | 53000 | 0.0052 | - | | 3.3193 | 53500 | 0.0053 | - | | 3.3503 | 54000 | 0.0041 | - | | 3.3813 | 54500 | 0.0042 | - | | 3.4123 | 55000 | 0.0026 | - | | 3.4434 | 55500 | 0.0045 | - | | 3.4744 | 56000 | 0.0045 | - | | 3.5054 | 56500 | 0.0054 | - | | 3.5364 | 57000 | 0.0055 | - | | 3.5674 | 57500 | 0.0046 | - | | 3.5985 | 58000 | 0.0045 | - | | 3.6295 | 58500 | 0.0041 | - | | 3.6605 | 59000 | 0.0037 | - | | 3.6915 | 59500 | 0.003 | - | | 3.7225 | 60000 | 0.0039 | - | | 3.7536 | 60500 | 0.0027 | - | | 3.7846 | 61000 | 0.0041 | - | | 3.8156 | 61500 | 0.003 | - | | 3.8466 | 62000 | 0.0027 | - | | 3.8777 | 62500 | 0.0039 | - | | 3.9087 | 63000 | 0.0038 | - | | 3.9397 | 63500 | 0.0029 | - | | 3.9707 | 64000 | 0.0037 | - | | 4.0 | 64472 | - | 0.9365 | | 4.0017 | 64500 | 0.0023 | - | | 4.0328 | 65000 | 0.0034 | - | | 4.0638 | 65500 | 0.0033 | - | | 4.0948 | 66000 | 0.0033 | - | | 4.1258 | 66500 | 0.004 | - | | 4.1568 | 67000 | 0.0026 | - | | 4.1879 | 67500 | 0.0026 | - | | 4.2189 | 68000 | 0.0025 | - | | 4.2499 | 68500 | 0.0037 | - | | 4.2809 | 69000 | 0.0041 | - | | 4.3119 | 69500 | 0.0031 | - | | 4.3430 | 70000 | 0.0025 | - | | 4.3740 | 70500 | 0.0025 | - | | 4.4050 | 71000 | 0.0022 | - | | 4.4360 | 71500 | 0.0016 | - | | 4.4671 | 72000 | 0.003 | - | | 4.4981 | 72500 | 0.0029 | - | | 4.5291 | 73000 | 0.003 | - | | 4.5601 | 73500 | 0.0025 | - | | 4.5911 | 74000 | 0.0027 | - | | 4.6222 | 74500 | 0.0028 | - | | 4.6532 | 75000 | 0.003 | - | | 4.6842 | 75500 | 0.002 | - | | 4.7152 | 76000 | 0.0028 | - | | 4.7462 | 76500 | 0.0016 | - | | 4.7773 | 77000 | 0.0022 | - | | 4.8083 | 77500 | 0.0019 | - | | 4.8393 | 78000 | 0.0019 | - | | 4.8703 | 78500 | 0.0026 | - | | 4.9014 | 79000 | 0.0023 | - | | 4.9324 | 79500 | 0.0016 | - | | 4.9634 | 80000 | 0.0019 | - | | 4.9944 | 80500 | 0.0018 | - | | 5.0 | 80590 | - | 0.937 | | 5.0254 | 81000 | 0.0028 | - | | 5.0565 | 81500 | 0.0019 | - | | 5.0875 | 82000 | 0.0024 | - | | 5.1185 | 82500 | 0.0016 | - | | 5.1495 | 83000 | 0.0015 | - | | 5.1805 | 83500 | 0.0017 | - | | 5.2116 | 84000 | 0.0016 | - | | 5.2426 | 84500 | 0.0026 | - | | 5.2736 | 85000 | 0.0029 | - | | 5.3046 | 85500 | 0.0027 | - | | 5.3356 | 86000 | 0.002 | - | | 5.3667 | 86500 | 0.002 | - | | 5.3977 | 87000 | 0.0021 | - | | 5.4287 | 87500 | 0.0011 | - | | 5.4597 | 88000 | 0.0016 | - | | 5.4908 | 88500 | 0.0019 | - | | 5.5218 | 89000 | 0.0027 | - | | 5.5528 | 89500 | 0.0012 | - | | 5.5838 | 90000 | 0.0012 | - | | 5.6148 | 90500 | 0.0016 | - | | 5.6459 | 91000 | 0.0019 | - | | 5.6769 | 91500 | 0.0016 | - | | 5.7079 | 92000 | 0.0027 | - | | 5.7389 | 92500 | 0.0013 | - | | 5.7699 | 93000 | 0.0013 | - | | 5.8010 | 93500 | 0.0015 | - | | 5.8320 | 94000 | 0.0016 | - | | 5.8630 | 94500 | 0.002 | - | | 5.8940 | 95000 | 0.001 | - | | 5.9251 | 95500 | 0.0014 | - | | 5.9561 | 96000 | 0.0021 | - | | 5.9871 | 96500 | 0.0022 | - | | 6.0 | 96708 | - | 0.933 | | 6.0181 | 97000 | 0.0016 | - | | 6.0491 | 97500 | 0.0015 | - | | 6.0802 | 98000 | 0.0011 | - | | 6.1112 | 98500 | 0.0016 | - | | 6.1422 | 99000 | 0.001 | - | | 6.1732 | 99500 | 0.0013 | - | | 6.2042 | 100000 | 0.0015 | 0.9365 | | 6.2353 | 100500 | 0.0017 | - | | 6.2663 | 101000 | 0.0015 | - | | 6.2973 | 101500 | 0.0016 | - | | 6.3283 | 102000 | 0.001 | - | | 6.3593 | 102500 | 0.0013 | - | | 6.3904 | 103000 | 0.0013 | - | | 6.4214 | 103500 | 0.0011 | - | | 6.4524 | 104000 | 0.0007 | - | | 6.4834 | 104500 | 0.0013 | - | | 6.5145 | 105000 | 0.0011 | - | | 6.5455 | 105500 | 0.0011 | - | | 6.5765 | 106000 | 0.0015 | - | | 6.6075 | 106500 | 0.002 | - | | 6.6385 | 107000 | 0.0011 | - | | 6.6696 | 107500 | 0.0013 | - | | 6.7006 | 108000 | 0.0017 | - | | 6.7316 | 108500 | 0.0008 | - | | 6.7626 | 109000 | 0.0011 | - | | 6.7936 | 109500 | 0.0008 | - | | 6.8247 | 110000 | 0.0009 | - | | 6.8557 | 110500 | 0.0014 | - | | 6.8867 | 111000 | 0.0014 | - | | 6.9177 | 111500 | 0.0014 | - | | 6.9488 | 112000 | 0.0014 | - | | 6.9798 | 112500 | 0.0013 | - | | 7.0 | 112826 | - | 0.9390 | | 7.0108 | 113000 | 0.0011 | - | | 7.0418 | 113500 | 0.0013 | - | | 7.0728 | 114000 | 0.0012 | - | | 7.1039 | 114500 | 0.001 | - | | 7.1349 | 115000 | 0.0016 | - | | 7.1659 | 115500 | 0.0009 | - | | 7.1969 | 116000 | 0.0009 | - | | 7.2279 | 116500 | 0.0007 | - | | 7.2590 | 117000 | 0.0008 | - | | 7.2900 | 117500 | 0.0014 | - | | 7.3210 | 118000 | 0.0012 | - | | 7.3520 | 118500 | 0.0007 | - | | 7.3831 | 119000 | 0.001 | - | | 7.4141 | 119500 | 0.001 | - | | 7.4451 | 120000 | 0.0007 | - | | 7.4761 | 120500 | 0.0008 | - | | 7.5071 | 121000 | 0.0009 | - | | 7.5382 | 121500 | 0.0009 | - | | 7.5692 | 122000 | 0.001 | - | | 7.6002 | 122500 | 0.0009 | - | | 7.6312 | 123000 | 0.0007 | - | | 7.6622 | 123500 | 0.0009 | - | | 7.6933 | 124000 | 0.0007 | - | | 7.7243 | 124500 | 0.0012 | - | | 7.7553 | 125000 | 0.001 | - | | 7.7863 | 125500 | 0.0005 | - | | 7.8173 | 126000 | 0.0005 | - | | 7.8484 | 126500 | 0.0008 | - | | 7.8794 | 127000 | 0.0014 | - | | 7.9104 | 127500 | 0.0014 | - | | 7.9414 | 128000 | 0.0009 | - | | 7.9725 | 128500 | 0.0008 | - | | 8.0 | 128944 | - | 0.94 | | 8.0035 | 129000 | 0.0013 | - | | 8.0345 | 129500 | 0.0007 | - | | 8.0655 | 130000 | 0.0007 | - | | 8.0965 | 130500 | 0.0008 | - | | 8.1276 | 131000 | 0.0009 | - | | 8.1586 | 131500 | 0.0009 | - | | 8.1896 | 132000 | 0.0007 | - | | 8.2206 | 132500 | 0.0008 | - | | 8.2516 | 133000 | 0.0008 | - | | 8.2827 | 133500 | 0.0006 | - | | 8.3137 | 134000 | 0.0008 | - | | 8.3447 | 134500 | 0.001 | - | | 8.3757 | 135000 | 0.0006 | - | | 8.4068 | 135500 | 0.0007 | - | | 8.4378 | 136000 | 0.0007 | - | | 8.4688 | 136500 | 0.0009 | - | | 8.4998 | 137000 | 0.0008 | - | | 8.5308 | 137500 | 0.0006 | - | | 8.5619 | 138000 | 0.0008 | - | | 8.5929 | 138500 | 0.0007 | - | | 8.6239 | 139000 | 0.0008 | - | | 8.6549 | 139500 | 0.0006 | - | | 8.6859 | 140000 | 0.0005 | - | | 8.7170 | 140500 | 0.0006 | - | | 8.7480 | 141000 | 0.0006 | - | | 8.7790 | 141500 | 0.0006 | - | | 8.8100 | 142000 | 0.0005 | - | | 8.8410 | 142500 | 0.0006 | - | | 8.8721 | 143000 | 0.0005 | - | | 8.9031 | 143500 | 0.0006 | - | | 8.9341 | 144000 | 0.0009 | - | | 8.9651 | 144500 | 0.0007 | - | | 8.9962 | 145000 | 0.0007 | - | | 9.0 | 145062 | - | 0.938 | | 9.0272 | 145500 | 0.0007 | - | | 9.0582 | 146000 | 0.0007 | - | | 9.0892 | 146500 | 0.0007 | - | | 9.1202 | 147000 | 0.0007 | - | | 9.1513 | 147500 | 0.0005 | - | | 9.1823 | 148000 | 0.0005 | - | | 9.2133 | 148500 | 0.0005 | - | | 9.2443 | 149000 | 0.0007 | - | | 9.2753 | 149500 | 0.0006 | - | | 9.3064 | 150000 | 0.0005 | 0.938 | | 9.3374 | 150500 | 0.0005 | - | | 9.3684 | 151000 | 0.0004 | - | | 9.3994 | 151500 | 0.0007 | - | | 9.4305 | 152000 | 0.0006 | - | | 9.4615 | 152500 | 0.0006 | - | | 9.4925 | 153000 | 0.0012 | - | | 9.5235 | 153500 | 0.0015 | - | | 9.5545 | 154000 | 0.0006 | - | | 9.5856 | 154500 | 0.0004 | - | | 9.6166 | 155000 | 0.0004 | - | | 9.6476 | 155500 | 0.0007 | - | | 9.6786 | 156000 | 0.0005 | - | | 9.7096 | 156500 | 0.0006 | - | | 9.7407 | 157000 | 0.0004 | - | | 9.7717 | 157500 | 0.0004 | - | | 9.8027 | 158000 | 0.0006 | - | | 9.8337 | 158500 | 0.0004 | - | | 9.8647 | 159000 | 0.0005 | - | | 9.8958 | 159500 | 0.0005 | - | | 9.9268 | 160000 | 0.0004 | - | | 9.9578 | 160500 | 0.0007 | - | | 9.9888 | 161000 | 0.0008 | - | | 10.0 | 161180 | - | 0.9405 | | 10.0199 | 161500 | 0.0009 | - | | 10.0509 | 162000 | 0.0007 | - | | 10.0819 | 162500 | 0.0007 | - | | 10.1129 | 163000 | 0.0007 | - | | 10.1439 | 163500 | 0.0005 | - | | 10.1750 | 164000 | 0.0005 | - | | 10.2060 | 164500 | 0.0004 | - | | 10.2370 | 165000 | 0.0006 | - | | 10.2680 | 165500 | 0.0006 | - | | 10.2990 | 166000 | 0.0005 | - | | 10.3301 | 166500 | 0.0005 | - | | 10.3611 | 167000 | 0.0006 | - | | 10.3921 | 167500 | 0.0006 | - | | 10.4231 | 168000 | 0.0003 | - | | 10.4542 | 168500 | 0.0005 | - | | 10.4852 | 169000 | 0.001 | - | | 10.5162 | 169500 | 0.0007 | - | | 10.5472 | 170000 | 0.0003 | - | | 10.5782 | 170500 | 0.0005 | - | | 10.6093 | 171000 | 0.0003 | - | | 10.6403 | 171500 | 0.0004 | - | | 10.6713 | 172000 | 0.0006 | - | | 10.7023 | 172500 | 0.0006 | - | | 10.7333 | 173000 | 0.0005 | - | | 10.7644 | 173500 | 0.0004 | - | | 10.7954 | 174000 | 0.0003 | - | | 10.8264 | 174500 | 0.0007 | - | | 10.8574 | 175000 | 0.0005 | - | | 10.8884 | 175500 | 0.0003 | - | | 10.9195 | 176000 | 0.0006 | - | | 10.9505 | 176500 | 0.001 | - | | 10.9815 | 177000 | 0.0007 | - | | 11.0 | 177298 | - | 0.9345 | | 11.0125 | 177500 | 0.0003 | - | | 11.0436 | 178000 | 0.0003 | - | | 11.0746 | 178500 | 0.0005 | - | | 11.1056 | 179000 | 0.0005 | - | | 11.1366 | 179500 | 0.0007 | - | | 11.1676 | 180000 | 0.0008 | - | | 11.1987 | 180500 | 0.0004 | - | | 11.2297 | 181000 | 0.0006 | - | | 11.2607 | 181500 | 0.0006 | - | | 11.2917 | 182000 | 0.0009 | - | | 11.3227 | 182500 | 0.0005 | - | | 11.3538 | 183000 | 0.0004 | - | | 11.3848 | 183500 | 0.0004 | - | | 11.4158 | 184000 | 0.0005 | - | | 11.4468 | 184500 | 0.0003 | - | | 11.4779 | 185000 | 0.0002 | - | | 11.5089 | 185500 | 0.0003 | - | | 11.5399 | 186000 | 0.0007 | - | | 11.5709 | 186500 | 0.0003 | - | | 11.6019 | 187000 | 0.0003 | - | | 11.6330 | 187500 | 0.0004 | - | | 11.6640 | 188000 | 0.0007 | - | | 11.6950 | 188500 | 0.0003 | - | | 11.7260 | 189000 | 0.0003 | - | | 11.7570 | 189500 | 0.0004 | - | | 11.7881 | 190000 | 0.0004 | - | | 11.8191 | 190500 | 0.0003 | - | | 11.8501 | 191000 | 0.0003 | - | | 11.8811 | 191500 | 0.0003 | - | | 11.9121 | 192000 | 0.0002 | - | | 11.9432 | 192500 | 0.0008 | - | | 11.9742 | 193000 | 0.0004 | - | | 12.0 | 193416 | - | 0.944 | | 12.0052 | 193500 | 0.0005 | - | | 12.0362 | 194000 | 0.0002 | - | | 12.0673 | 194500 | 0.0003 | - | | 12.0983 | 195000 | 0.0004 | - | | 12.1293 | 195500 | 0.0005 | - | | 12.1603 | 196000 | 0.0004 | - | | 12.1913 | 196500 | 0.0002 | - | | 12.2224 | 197000 | 0.0002 | - | | 12.2534 | 197500 | 0.0003 | - | | 12.2844 | 198000 | 0.0003 | - | | 12.3154 | 198500 | 0.0005 | - | | 12.3464 | 199000 | 0.0004 | - | | 12.3775 | 199500 | 0.0004 | - | | 12.4085 | 200000 | 0.0003 | 0.9435 | | 12.4395 | 200500 | 0.0003 | - | | 12.4705 | 201000 | 0.0004 | - | | 12.5016 | 201500 | 0.0009 | - | | 12.5326 | 202000 | 0.0005 | - | | 12.5636 | 202500 | 0.0003 | - | | 12.5946 | 203000 | 0.0003 | - | | 12.6256 | 203500 | 0.0002 | - | | 12.6567 | 204000 | 0.0003 | - | | 12.6877 | 204500 | 0.0002 | - | | 12.7187 | 205000 | 0.0005 | - | | 12.7497 | 205500 | 0.0003 | - | | 12.7807 | 206000 | 0.0004 | - | | 12.8118 | 206500 | 0.0003 | - | | 12.8428 | 207000 | 0.0003 | - | | 12.8738 | 207500 | 0.0003 | - | | 12.9048 | 208000 | 0.0003 | - | | 12.9358 | 208500 | 0.0006 | - | | 12.9669 | 209000 | 0.0004 | - | | 12.9979 | 209500 | 0.0004 | - | | 13.0 | 209534 | - | 0.9455 | </details> ### Framework Versions - Python: 3.10.17 - Sentence Transformers: 4.1.0 - Transformers: 4.46.3 - PyTorch: 2.2.0+cu121 - Accelerate: 1.1.1 - Datasets: 2.18.0 - Tokenizers: 0.20.3 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

Dejiat/blockassist-bc-savage_unseen_bobcat_1756309339

Dejiat

2025-08-27T15:42:44Z

0

null

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

null

2025-08-27T15:42:41Z

--- 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).

kelantan-doctor-video-part-4-video/dr.wong.lu.yang.cctv.kelantan.doctor.video.part.4.video.link

kelantan-doctor-video-part-4-video

2025-08-27T15:40:12Z

0

null

[ "region:us" ]

null

2025-08-27T15:39:58Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

quantumxnode/blockassist-bc-dormant_peckish_seahorse_1756307656

quantumxnode

2025-08-27T15:39:59Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:39:55Z

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

vangard703/output_stage2

vangard703

2025-08-27T15:39:15Z

0

transformers

[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]

image-to-text

2025-08-27T15:33:26Z

--- 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]

xinnn32/blockassist-bc-meek_winged_caterpillar_1756309083

xinnn32

2025-08-27T15:38:41Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:38:31Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - meek winged caterpillar --- # 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_1756309043

Dejiat

2025-08-27T15:37:47Z

0

null

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

null

2025-08-27T15:37:45Z

--- 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).

dr-wong-lu-yang-cctv-Viral-video-Clip/New.full.videos.Dr.wong.Viral.Video.Official.Tutorial

dr-wong-lu-yang-cctv-Viral-video-Clip

2025-08-27T15:37:10Z

0

null

[ "region:us" ]

null

2025-08-27T15:35:20Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/mdfprj9k?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

hakimjustbao/blockassist-bc-raging_subtle_wasp_1756307278

hakimjustbao

2025-08-27T15:36:30Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:36:26Z

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

mirlasith/blockassist-bc-bold_shiny_rat_1756308822

mirlasith

2025-08-27T15:34:30Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bold shiny rat", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:34:22Z

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

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756308761

Ferdi3425

2025-08-27T15:33:10Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:33:06Z

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

GroomerG/blockassist-bc-vicious_pawing_badger_1756307320

GroomerG

2025-08-27T15:33:04Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "vicious pawing badger", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:33:01Z

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

OksanaB/blockassist-bc-huge_ferocious_chameleon_1756308652

OksanaB

2025-08-27T15:32:26Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:31:31Z

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

xinnn32/blockassist-bc-meek_winged_caterpillar_1756308707

xinnn32

2025-08-27T15:32:23Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:32:16Z

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

GlitChwoLf9/blockassist-bc-graceful_lazy_reindeer_1756307073

GlitChwoLf9

2025-08-27T15:32:11Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "graceful lazy reindeer", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:31:38Z

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

koloni/blockassist-bc-deadly_graceful_stingray_1756307121

koloni

2025-08-27T15:31:52Z

0

null

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

null

2025-08-27T15:31:48Z

--- 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).

dr-wong-lu-yang-cctv-Viral-videos/New.full.videos.Dr.wong.Viral.Video.Official.Tutorial

dr-wong-lu-yang-cctv-Viral-videos

2025-08-27T15:31:32Z

0

null

[ "region:us" ]

null

2025-08-27T15:31:04Z

<animated-image data-catalyst=""><a href="https://fubotv24.com/Leaked/?v=video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

insanesaga/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison

insanesaga

2025-08-27T15:30:44Z

35

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am nocturnal clawed bison", "unsloth", "trl", "genrl-swarm", "I am nocturnal_clawed_bison", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-05-08T01:05:47Z

--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am nocturnal clawed bison - unsloth - trl - genrl-swarm - I am nocturnal_clawed_bison licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="insanesaga/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nocturnal_clawed_bison", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

original-dr-wong-lu-yang-cctv-Viral-video/full.videos.Dr.wong.Viral.Video.Official.Tutorial

original-dr-wong-lu-yang-cctv-Viral-video

2025-08-27T15:30:25Z

0

null

[ "region:us" ]

null

2025-08-27T15:30:10Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

alok0777/blockassist-bc-masked_pensive_lemur_1756308581

alok0777

2025-08-27T15:30:24Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "masked pensive lemur", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:30:13Z

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

2hpsatt/blockassist-bc-huge_deft_eagle_1756308569

2hpsatt

2025-08-27T15:30:24Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge deft eagle", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:30:13Z

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

liukevin666/blockassist-bc-yawning_striped_cassowary_1756308493

liukevin666

2025-08-27T15:29:19Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:29:12Z

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

kugu/model_negotitation

kugu

2025-08-27T15:28:10Z

0

transformers

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

text-generation

2025-08-25T05:30:44Z

--- 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]

lurepaper/LURE_5.3

lurepaper

2025-08-27T15:28:10Z

0

null

[ "safetensors", "qwen3", "en", "base_model:Qwen/Qwen3-32B", "base_model:finetune:Qwen/Qwen3-32B", "license:apache-2.0", "region:us" ]

null

2025-08-27T13:04:46Z

--- license: apache-2.0 language: - en base_model: - Qwen/Qwen3-32B --- # LURE 5.3 This is the LURE model for Lua 5.3. ## Usage: ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "lurepaper/LURE_5.3" model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto") tokenizer = AutoTokenizer.from_pretrained(model_id) prompt = "You are a Lua programming language expert. Please generate generate concise Lua code that produces the Lua 5.3 opcode OP_ADD. Use a print function call at the end to show the execution result of the opcode." messages = [ {"role": "user", "content": prompt}, ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=32768, ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("Thinking content:") print(thinking_content) print("Generated LuaGadget:") print(content) ```

mradermacher/TinyLlama-Sakha-Instruct-GGUF

mradermacher

2025-08-27T15:26:52Z

0

transformers

[ "transformers", "gguf", "sah", "base_model:lab-ii/TinyLlama-Sakha-Instruct", "base_model:quantized:lab-ii/TinyLlama-Sakha-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-27T14:55:35Z

--- base_model: lab-ii/TinyLlama-Sakha-Instruct language: - sah library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About         static quants of https://huggingface.co/lab-ii/TinyLlama-Sakha-Instruct  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#TinyLlama-Sakha-Instruct-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/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q2_K.gguf) | Q2_K | 0.6 | | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q3_K_S.gguf) | Q3_K_S | 0.6 | | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q3_K_M.gguf) | Q3_K_M | 0.7 | lower quality | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q3_K_L.gguf) | Q3_K_L | 0.7 | | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.IQ4_XS.gguf) | IQ4_XS | 0.8 | | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q4_K_S.gguf) | Q4_K_S | 0.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q4_K_M.gguf) | Q4_K_M | 0.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q5_K_S.gguf) | Q5_K_S | 0.9 | | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q5_K_M.gguf) | Q5_K_M | 0.9 | | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q6_K.gguf) | Q6_K | 1.1 | very good quality | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.Q8_0.gguf) | Q8_0 | 1.3 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/TinyLlama-Sakha-Instruct-GGUF/resolve/main/TinyLlama-Sakha-Instruct.f16.gguf) | f16 | 2.4 | 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.

bytedance-research/USO

bytedance-research

2025-08-27T15:26:44Z

0

2

transformers

[ "transformers", "image-generation", "subject-personalization", "style-transfer", "Diffusion-Transformer", "image-to-image", "en", "arxiv:2508.18966", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:apache-2.0", "endpoints_compatible", "region:us" ]

image-to-image

2025-08-27T08:35:39Z

--- license: apache-2.0 language: - en base_model: - black-forest-labs/FLUX.1-dev library_name: transformers pipeline_tag: image-to-image tags: - image-generation - subject-personalization - style-transfer - Diffusion-Transformer --- <img src="assets/uso.webp" width="100"/> <h3 align="center"> Unified Style and Subject-Driven Generation via Disentangled and Reward Learning </h3> <a href="https://github.com/bytedance/USO"><img alt="Build" src="https://img.shields.io/github/stars/bytedance/USO"></a> <a href="https://bytedance.github.io/USO/"><img alt="Build" src="https://img.shields.io/badge/Project%20Page-USO-blue"></a> <a href="https://arxiv.org/abs/2508.18966"><img alt="Build" src="https://img.shields.io/badge/Tech%20Report-USO-b31b1b.svg"></a> <a href="https://huggingface.co/bytedance-research/USO"><img src="https://img.shields.io/static/v1?label=%F0%9F%A4%97%20Hugging%20Face&message=Model&color=green"></a> ![teaser of USO](./assets/teaser.webp) ## 📖 Introduction Existing literature typically treats style-driven and subject-driven generation as two disjoint tasks: the former prioritizes stylistic similarity, whereas the latter insists on subject consistency, resulting in an apparent antagonism. We argue that both objectives can be unified under a single framework because they ultimately concern the disentanglement and re-composition of “content” and “style”, a long-standing theme in style-driven research. To this end, we present USO, a Unified framework for Style driven and subject-driven GeneratiOn. First, we construct a large-scale triplet dataset consisting of content images, style images, and their corresponding stylized content images. Second, we introduce a disentangled learning scheme that simultaneously aligns style features and disentangles content from style through two complementary objectives, style-alignment training and content–style disentanglement training. Third, we incorporate a style reward-learning paradigm to further enhance the model’s performance. ## ⚡️ Quick Start ### 🔧 Requirements and Installation Install the requirements ```bash ## create a virtual environment with python >= 3.10 <= 3.12, like python -m venv uso_env source uso_env/bin/activate ## or conda create -n uso_env python=3.10 -y conda activate uso_env ## then install the requirements by you need pip install -r requirements.txt # legacy installation command ``` Then download checkpoints in one of the following ways: - **Suppose you already have some of the checkpoints** ```bash # 1. download USO official checkpoints pip install huggingface_hub huggingface-cli download bytedance-research/USO --local-dir <YOU_SAVE_DIR> --local-dir-use-symlinks False # 2. Then set the environment variable for FLUX.1 base model export AE="YOUR_AE_PATH" export FLUX_DEV="YOUR_FLUX_DEV_PATH" export T5="YOUR_T5_PATH" export CLIP="YOUR_CLIP_PATH" # or export HF_HOME="YOUR_HF_HOME" # 3. Then set the environment variable for USO export LORA="<YOU_SAVE_DIR>/uso_flux_v1.0/dit_lora.safetensors" export PROJECTION_MODEL="<YOU_SAVE_DIR>/uso_flux_v1.0/projector.safetensors" ``` - Directly run the inference scripts, the checkpoints will be downloaded automatically by the `hf_hub_download` function in the code. ### ✍️ Inference Start from the examples below to explore and spark your creativity. ✨ ```bash # the first image is a content reference, and the rest are style references. # for subject-driven generation python inference.py --prompt "The man in flower shops carefully match bouquets, conveying beautiful emotions and blessings with flowers. " --image_paths "assets/gradio_examples/identity1.jpg" --width 1024 --height 1024 # for style-driven generation # please keep the first image path empty python inference.py --prompt "A cat sleeping on a chair." --image_paths "" "assets/gradio_examples/style1.webp" --width 1024 --height 1024 # for ip-style generation python inference.py --prompt "The woman gave an impassioned speech on the podium." --image_paths "assets/gradio_examples/identity2.webp" "assets/gradio_examples/style2.webp" --width 1024 --height 1024 # for multi-style generation # please keep the first image path empty python inference.py --prompt "A handsome man." --image_paths "" "assets/gradio_examples/style3.webp" "assets/gradio_examples/style4.webp" --width 1024 --height 1024 ``` ## 📄 Disclaimer We open-source this project for academic research. The vast majority of images used in this project are either generated or from open-source datasets. If you have any concerns, please contact us, and we will promptly remove any inappropriate content. Our project is released under the Apache 2.0 License. If you apply to other base models, please ensure that you comply with the original licensing terms. This research aims to advance the field of generative AI. Users are free to create images using this tool, provided they comply with local laws and exercise responsible usage. The developers are not liable for any misuse of the tool by users. ## Citation We also appreciate it if you could give a star ⭐ to our [Github repository](https://github.com/bytedance/USO). Thanks a lot! If you find this project useful for your research, please consider citing our paper: ```bibtex @article{wu2025uso, title={USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning}, author={Shaojin Wu and Mengqi Huang and Yufeng Cheng and Wenxu Wu and Jiahe Tian and Yiming Luo and Fei Ding and Qian He}, year={2025}, eprint={2508.18966}, archivePrefix={arXiv}, primaryClass={cs.CV}, } ```

dr-wong-lu-yang-cctv-Viral-videoss/New.full.videos.Dr.wong.Viral.Video.Official.Tutorial

dr-wong-lu-yang-cctv-Viral-videoss

2025-08-27T15:26:12Z

0

null

[ "region:us" ]

null

2025-08-27T15:25:42Z

<animated-image data-catalyst=""><a href="https://fubotv24.com/Leaked/?v=video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

PaddlePaddle/PP-DocBee-7B

PaddlePaddle

2025-08-27T15:25:46Z

15

1

PaddleOCR

[ "PaddleOCR", "paddlepaddle", "qwen2_vl", "OCR", "PaddlePaddle", "doc_vlm", "image-to-text", "en", "zh", "license:apache-2.0", "region:us" ]

image-to-text

2025-06-06T04:10:39Z

--- license: apache-2.0 library_name: PaddleOCR language: - en - zh pipeline_tag: image-to-text tags: - OCR - PaddlePaddle - PaddleOCR - doc_vlm --- # PP-DocBee-7B ## Introduction The PaddleOCR team has developed PP-DocBee-7B, a multimodal large model focusing on document understanding, and it performs excellently in Chinese document understanding tasks. The model is fine-tuned and optimized using nearly 5 million multimodal datasets for document understanding, including general VQA, OCR, charts, text-rich documents, mathematics and complex reasoning, synthetic data, and pure text data, with different training data ratios set. On several authoritative English document understanding evaluation lists in academia, PP-DocBee has basically achieved SOTA for models of the same parameter scale. In terms of internal business Chinese scenario indicators, PP-DocBee also outperforms the current popular open-source and closed-source models. The key accuracy metrics are as follow: | Model | Model Storage Size(GB) | Total Score | |-------|--------------------------|-----------| | PP-DocBee-2B | 4.2 | 765 | | **PP-DocBee-7B** | 15.8 | - | **Note**: The total scores of the above models are test results from an internal evaluation set, where all images have a resolution (height, width) of (1680, 1204), with a total of 1196 data entries, covering scenarios such as financial reports, laws and regulations, scientific and technical papers, manuals, humanities papers, contracts, research reports, etc. There are no plans for public release at the moment. ## Quick Start ### Installation 1. PaddlePaddle Please refer to the following commands to install PaddlePaddle using pip: ```bash # for CUDA11.8 python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ # for CUDA12.6 python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/ # for CPU python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ ``` For details about PaddlePaddle installation, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/en/install/quick). 2. PaddleOCR Install the latest version of the PaddleOCR inference package from PyPI: ```bash python -m pip install paddleocr ``` ### Model Usage You can quickly experience the functionality with a single command: ```bash paddleocr doc_vlm \ --model_name PP-DocBee-7B \ -i "{'image': 'https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png', 'query': 'Recognize the content of this table and output it in markdown format.'}" ``` You can also integrate the model inference of the document visual-language module into your project. Before running the following code, please download the sample image to your local machine. ```python from paddleocr import DocVLM model = DocVLM(model_name="PP-DocBee-7B") results = model.predict( input={ "image": "https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png", "query": "Recognize the content of this table and output it in markdown format." }, batch_size=1 ) for res in results: res.print() res.save_to_json(f"./output/res.json") ``` After running, the obtained result is as follows: ```bash {'res': {'image': 'medal_table_en.png', 'query': 'Recognize the content of this table and output it in markdown format', 'result': '| Rank | Country/Region | Gold | Silver | Bronze | Total Medals |\n|---|---|---|---|---|---|\n| 1 | China (CHN) | 48 | 22 | 30 | 100 |\n| 2 | United States (USA) | 36 | 39 | 37 | 112 |\n| 3 | Russia (RUS) | 24 | 13 | 23 | 60 |\n| 4 | Great Britain (GBR) | 19 | 13 | 19 | 51 |\n| 5 | Germany (GER) | 16 | 11 | 14 | 41 |\n| 6 | Australia (AUS) | 14 | 15 | 17 | 46 |\n| 7 | South Korea (KOR) | 13 | 11 | 8 | 32 |\n| 8 | Japan (JPN) | 9 | 8 | 8 | 25 |\n| 9 | Italy (ITA) | 8 | 9 | 10 | 27 |\n| 10 | France (FRA) | 7 | 16 | 20 | 43 |\n| 11 | Netherlands (NED) | 7 | 5 | 4 | 16 |\n| 12 | Ukraine (UKR) | 7 | 4 | 11 | 22 |\n| 13 | Kenya (KEN) | 6 | 4 | 6 | 16 |\n| 14 | Spain (ESP) | 5 | 11 | 3 | 19 |\n| 15 | Jamaica (JAM) | 5 | 4 | 2 | 11 |\n'}} ``` The visualized result is as follows: ```bash | Rank | Country/Region | Gold | Silver | Bronze | Total Medals | |---|---|---|---|---|---| | 1 | China (CHN) | 48 | 22 | 30 | 100 | | 2 | United States (USA) | 36 | 39 | 37 | 112 | | 3 | Russia (RUS) | 24 | 13 | 23 | 60 | | 4 | Great Britain (GBR) | 19 | 13 | 19 | 51 | | 5 | Germany (GER) | 16 | 11 | 14 | 41 | | 6 | Australia (AUS) | 14 | 15 | 17 | 46 | | 7 | South Korea (KOR) | 13 | 11 | 8 | 32 | | 8 | Japan (JPN) | 9 | 8 | 8 | 25 | | 9 | Italy (ITA) | 8 | 9 | 10 | 27 | | 10 | France (FRA) | 7 | 16 | 20 | 43 | | 11 | Netherlands (NED) | 7 | 5 | 4 | 16 | | 12 | Ukraine (UKR) | 7 | 4 | 11 | 22 | | 13 | Kenya (KEN) | 6 | 4 | 6 | 16 | | 14 | Spain (ESP) | 5 | 11 | 3 | 19 | | 15 | Jamaica (JAM) | 5 | 4 | 2 | 11 | ``` For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/doc_vlm.html#iii-quick-start). ### Pipeline Usage The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenarios. #### doc_understanding The document understanding pipeline is an advanced document processing technology based on Visual-Language Models (VLM), designed to overcome the limitations of traditional document processing. And there is only 1 module in the pipeline: * Document Visual Language Module Run a single command to quickly experience the OCR pipeline: ```bash paddleocr doc_understanding -i "{'image': 'https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png', 'query': 'Recognize the content of this table and output it in markdown format.'}" ``` Results are printed to the terminal: ```bash {'res': {'image': 'medal_table_en.png', 'query': 'Recognize the content of this table and output it in markdown format', 'result': '| Rank | Country/Region | Gold | Silver | Bronze | Total Medals |\n|---|---|---|---|---|---|\n| 1 | China (CHN) | 48 | 22 | 30 | 100 |\n| 2 | United States (USA) | 36 | 39 | 37 | 112 |\n| 3 | Russia (RUS) | 24 | 13 | 23 | 60 |\n| 4 | Great Britain (GBR) | 19 | 13 | 19 | 51 |\n| 5 | Germany (GER) | 16 | 11 | 14 | 41 |\n| 6 | Australia (AUS) | 14 | 15 | 17 | 46 |\n| 7 | South Korea (KOR) | 13 | 11 | 8 | 32 |\n| 8 | Japan (JPN) | 9 | 8 | 8 | 25 |\n| 9 | Italy (ITA) | 8 | 9 | 10 | 27 |\n| 10 | France (FRA) | 7 | 16 | 20 | 43 |\n| 11 | Netherlands (NED) | 7 | 5 | 4 | 16 |\n| 12 | Ukraine (UKR) | 7 | 4 | 11 | 22 |\n| 13 | Kenya (KEN) | 6 | 4 | 6 | 16 |\n| 14 | Spain (ESP) | 5 | 11 | 3 | 19 |\n| 15 | Jamaica (JAM) | 5 | 4 | 2 | 11 |\n'}} ``` If save_path is specified, the visualization results will be saved under `save_path`. The visualization output is shown below: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/kFGo9nlHuHs2uyN1voSTg.png) The command-line method is for quick experience. For project integration, also only a few codes are needed as well: ```python from paddleocr import DocUnderstanding pipeline = DocUnderstanding( doc_understanding_model_name="PP-DocBee-7B" ) output = pipeline.predict( { "image": "https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png", "query": "Recognize the content of this table and output it in markdown format." } ) for res in output: res.print() ## Print the structured output of the prediction res.save_to_json("./output/") ``` The default model used in pipeline is `PP-DocBee2-3B`, so you need to specify `doc_understanding_model_name` to `PP-DocBee-7B`. And you can also use the local model file by argument `doc_understanding_model_dir`. For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/pipeline_usage/doc_understanding.html#2-quick-start). ## Links [PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr) [PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html)

original-dr-wong-lu-yang-cctv-Viral-video/New.full.videos.Dr.wong.Viral.Video.Official.Tutorial

original-dr-wong-lu-yang-cctv-Viral-video

2025-08-27T15:25:46Z

0

null

[ "region:us" ]

null

2025-08-27T15:25:26Z

<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>

Nikhil058/Reinforce_Pixelcopter_PLE_V0

Nikhil058

2025-08-27T15:25:07Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2025-08-27T15:25:03Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce_Pixelcopter_PLE_V0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 29.50 +/- 30.53 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

PaddlePaddle/PP-DocBee2-3B

PaddlePaddle

2025-08-27T15:25:06Z

182

0

PaddleOCR

[ "PaddleOCR", "paddlepaddle", "qwen2_5_vl", "OCR", "PaddlePaddle", "doc_vlm", "image-to-text", "en", "zh", "license:apache-2.0", "region:us" ]

image-to-text

2025-06-06T03:27:39Z

--- license: apache-2.0 library_name: PaddleOCR language: - en - zh pipeline_tag: image-to-text tags: - OCR - PaddlePaddle - PaddleOCR - doc_vlm --- # PP-DocBee2-3B ## Introduction The PaddleOCR team has developed PP-DocBee2-3B, a multimodal large model that significantly enhances Chinese document understanding. Building upon the original PP-DocBee, this new iteration introduces an improved data optimization scheme that boosts data quality. PP-DocBee2 achieves superior performance in Chinese document understanding tasks by leveraging a relatively small dataset of 470,000 synthetic data points, generated through a proprietary data synthesis strategy. Internally, PP-DocBee2 demonstrates an impressive 11.4% improvement over its predecessor, PP-DocBee, in Chinese business scenario metrics. Furthermore, it outperforms other popular open-source and closed-source models of comparable scale in key accuracy metrics. The key accuracy metrics are as follow: | Model | Model Storage Size(GB) | Total Score | |-------|--------------------------|-----------| | PP-DocBee-2B | 4.2 | 765 | | PP-DocBee-7B | 15.8 | - | | **PP-DocBee2-3B** | 7.6 | 852 | **Note**: The total scores of the above models are test results from an internal evaluation set, where all images have a resolution (height, width) of (1680, 1204), with a total of 1196 data entries, covering scenarios such as financial reports, laws and regulations, scientific and technical papers, manuals, humanities papers, contracts, research reports, etc. There are no plans for public release at the moment. ## Quick Start ### Installation 1. PaddlePaddle Please refer to the following commands to install PaddlePaddle using pip: ```bash # for CUDA11.8 python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ # for CUDA12.6 python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/ # for CPU python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ ``` For details about PaddlePaddle installation, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/en/install/quick). 2. PaddleOCR Install the latest version of the PaddleOCR inference package from PyPI: ```bash python -m pip install paddleocr ``` ### Model Usage You can quickly experience the functionality with a single command: ```bash paddleocr doc_vlm \ --model_name PP-DocBee2-3B \ -i "{'image': 'https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png', 'query': 'Recognize the content of this table and output it in markdown format.'}" ``` You can also integrate the model inference of the document visual-language module into your project. Before running the following code, please download the sample image to your local machine. ```python from paddleocr import DocVLM model = DocVLM(model_name="PP-DocBee2-3B") results = model.predict( input={ "image": "https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png", "query": "Recognize the content of this table and output it in markdown format." }, batch_size=1 ) for res in results: res.print() res.save_to_json(f"./output/res.json") ``` After running, the obtained result is as follows: ```bash {'res': {'image': 'medal_table_en.png', 'query': 'Recognize the content of this table and output it in markdown format', 'result': '| Rank | Country/Region | Gold | Silver | Bronze | Total Medals |\n|---|---|---|---|---|---|\n| 1 | China (CHN) | 48 | 22 | 30 | 100 |\n| 2 | United States (USA) | 36 | 39 | 37 | 112 |\n| 3 | Russia (RUS) | 24 | 13 | 23 | 60 |\n| 4 | Great Britain (GBR) | 19 | 13 | 19 | 51 |\n| 5 | Germany (GER) | 16 | 11 | 14 | 41 |\n| 6 | Australia (AUS) | 14 | 15 | 17 | 46 |\n| 7 | South Korea (KOR) | 13 | 11 | 8 | 32 |\n| 8 | Japan (JPN) | 9 | 8 | 8 | 25 |\n| 9 | Italy (ITA) | 8 | 9 | 10 | 27 |\n| 10 | France (FRA) | 7 | 16 | 20 | 43 |\n| 11 | Netherlands (NED) | 7 | 5 | 4 | 16 |\n| 12 | Ukraine (UKR) | 7 | 4 | 11 | 22 |\n| 13 | Kenya (KEN) | 6 | 4 | 6 | 16 |\n| 14 | Spain (ESP) | 5 | 11 | 3 | 19 |\n| 15 | Jamaica (JAM) | 5 | 4 | 2 | 11 |\n'}} ``` The visualized result is as follows: ```bash | Rank | Country/Region | Gold | Silver | Bronze | Total Medals | |---|---|---|---|---|---| | 1 | China (CHN) | 48 | 22 | 30 | 100 | | 2 | United States (USA) | 36 | 39 | 37 | 112 | | 3 | Russia (RUS) | 24 | 13 | 23 | 60 | | 4 | Great Britain (GBR) | 19 | 13 | 19 | 51 | | 5 | Germany (GER) | 16 | 11 | 14 | 41 | | 6 | Australia (AUS) | 14 | 15 | 17 | 46 | | 7 | South Korea (KOR) | 13 | 11 | 8 | 32 | | 8 | Japan (JPN) | 9 | 8 | 8 | 25 | | 9 | Italy (ITA) | 8 | 9 | 10 | 27 | | 10 | France (FRA) | 7 | 16 | 20 | 43 | | 11 | Netherlands (NED) | 7 | 5 | 4 | 16 | | 12 | Ukraine (UKR) | 7 | 4 | 11 | 22 | | 13 | Kenya (KEN) | 6 | 4 | 6 | 16 | | 14 | Spain (ESP) | 5 | 11 | 3 | 19 | | 15 | Jamaica (JAM) | 5 | 4 | 2 | 11 | ``` For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/doc_vlm.html#iii-quick-start). ### Pipeline Usage The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenarios. #### doc_understanding The document understanding pipeline is an advanced document processing technology based on Visual-Language Models (VLM), designed to overcome the limitations of traditional document processing. And there is only 1 module in the pipeline: * Document Visual Language Module Run a single command to quickly experience the OCR pipeline: ```bash paddleocr doc_understanding -i "{'image': 'https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png', 'query': 'Recognize the content of this table and output it in markdown format.'}" ``` Results are printed to the terminal: ```json {'res': {'image': 'medal_table_en.png', 'query': 'Recognize the content of this table and output it in markdown format', 'result': '| Rank | Country/Region | Gold | Silver | Bronze | Total Medals |\n|---|---|---|---|---|---|\n| 1 | China (CHN) | 48 | 22 | 30 | 100 |\n| 2 | United States (USA) | 36 | 39 | 37 | 112 |\n| 3 | Russia (RUS) | 24 | 13 | 23 | 60 |\n| 4 | Great Britain (GBR) | 19 | 13 | 19 | 51 |\n| 5 | Germany (GER) | 16 | 11 | 14 | 41 |\n| 6 | Australia (AUS) | 14 | 15 | 17 | 46 |\n| 7 | South Korea (KOR) | 13 | 11 | 8 | 32 |\n| 8 | Japan (JPN) | 9 | 8 | 8 | 25 |\n| 9 | Italy (ITA) | 8 | 9 | 10 | 27 |\n| 10 | France (FRA) | 7 | 16 | 20 | 43 |\n| 11 | Netherlands (NED) | 7 | 5 | 4 | 16 |\n| 12 | Ukraine (UKR) | 7 | 4 | 11 | 22 |\n| 13 | Kenya (KEN) | 6 | 4 | 6 | 16 |\n| 14 | Spain (ESP) | 5 | 11 | 3 | 19 |\n| 15 | Jamaica (JAM) | 5 | 4 | 2 | 11 |\n'}} ``` If save_path is specified, the visualization results will be saved under `save_path`. The visualization output is shown below: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/kFGo9nlHuHs2uyN1voSTg.png) The command-line method is for quick experience. For project integration, also only a few codes are needed as well: ```python from paddleocr import DocUnderstanding pipeline = DocUnderstanding( doc_understanding_model_name="PP-DocBee2-3B" ) output = pipeline.predict( { "image": "https://cdn-uploads.huggingface.co/production/uploads/684acf07de103b2d44c85531/l5xpHbfLn75dKInhQZ84I.png", "query": "Recognize the content of this table and output it in markdown format." } ) for res in output: res.print() ## Print the structured output of the prediction res.save_to_json("./output/") ``` The default model used in pipeline is `PP-DocBee2-3B`, so you don't have to specify `PP-DocBee2-3B` for the `doc_understanding_model_name argument`, but you can use the local model file by argument `doc_understanding_model_dir`. For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/pipeline_usage/doc_understanding.html#2-quick-start). ## Links [PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr) [PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html)

thorejaya/omega_EbUtNMG

thorejaya

2025-08-27T15:25:03Z

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

2025-08-27T15:25:02Z

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

mang3dd/blockassist-bc-tangled_slithering_alligator_1756306728

mang3dd

2025-08-27T15:24:47Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:24:43Z

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

OksanaB/blockassist-bc-huge_ferocious_chameleon_1756308176

OksanaB

2025-08-27T15:24:24Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge ferocious chameleon", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:23:31Z

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

1yuuuna/output

1yuuuna

2025-08-27T15:24:08Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:kisti/korscideberta", "base_model:finetune:kisti/korscideberta", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-27T15:12:15Z

--- library_name: transformers license: mit base_model: kisti/korscideberta tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: output results: [] ---  # output This model is a fine-tuned version of [kisti/korscideberta](https://huggingface.co/kisti/korscideberta) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1835 - Accuracy: 0.9280 - Precision: 0.9280 - Recall: 0.9280 - F1: 0.9279 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.4034 | 0.3188 | 500 | 0.3371 | 0.8543 | 0.8737 | 0.8543 | 0.8538 | | 0.2338 | 0.6376 | 1000 | 0.1964 | 0.9159 | 0.9159 | 0.9159 | 0.9159 | | 0.2079 | 0.9563 | 1500 | 0.1719 | 0.9322 | 0.9323 | 0.9322 | 0.9323 | ### Framework versions - Transformers 4.45.0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.20.3

Jack-Payne1/gemma-3-4b-it-good-doctor-seed3

Jack-Payne1

2025-08-27T15:24:02Z

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "base_model:unsloth/gemma-3-4b-it", "base_model:finetune:unsloth/gemma-3-4b-it", "endpoints_compatible", "region:us" ]

null

2025-08-27T15:08:34Z

--- base_model: unsloth/gemma-3-4b-it library_name: transformers model_name: gemma-3-4b-it-good-doctor-seed3 tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for gemma-3-4b-it-good-doctor-seed3 This model is a fine-tuned version of [unsloth/gemma-3-4b-it](https://huggingface.co/unsloth/gemma-3-4b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Jack-Payne1/gemma-3-4b-it-good-doctor-seed3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jacktpayne51-macquarie-university/clarifying-em/runs/xi8pi28b) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.4 - Pytorch: 2.7.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

acidjp/blockassist-bc-pesty_extinct_prawn_1756305914

acidjp

2025-08-27T15:23:29Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pesty extinct prawn", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:23:25Z

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

2hpsatt/blockassist-bc-huge_deft_eagle_1756307978

2hpsatt

2025-08-27T15:21:09Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "huge deft eagle", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:21:00Z

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

sunrunner79hot1/blockassist-bc-bold_noisy_woodpecker_1756306480

sunrunner79hot1

2025-08-27T15:19:55Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bold noisy woodpecker", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:19:52Z

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

bodigardehotma1/blockassist-bc-spotted_mimic_giraffe_1756306328

bodigardehotma1

2025-08-27T15:19:53Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted mimic giraffe", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:19:43Z

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

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756307942

Ferdi3425

2025-08-27T15:19:30Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:19:26Z

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

liukevin666/blockassist-bc-yawning_striped_cassowary_1756307829

liukevin666

2025-08-27T15:19:21Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:18:08Z

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

runchat/lora-06654833-d1fd-47fb-ba7d-dd8fb13ca165-w36ous

runchat

2025-08-27T15:18:08Z

0

diffusers

[ "diffusers", "flux", "lora", "text-to-image", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-08-27T15:18:04Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md base_model: black-forest-labs/FLUX.1-dev tags: - flux - lora - diffusers - text-to-image widget: - text: 'a photo of a VICTORIA DETAILS style' output: url: "placeholder.jpg" --- # Flux LoRA: VICTORIA DETAILS This is a LoRA (Low-Rank Adaptation) model for Flux.1-dev fine-tuned on images with the trigger word `VICTORIA DETAILS`. ## Files - `pytorch_lora_weights.safetensors`: Diffusers format (use with diffusers library) - `pytorch_lora_weights_webui.safetensors`: Kohya format (use with AUTOMATIC1111, ComfyUI, etc.) ## Usage ### Diffusers Library ```python from diffusers import FluxPipeline import torch # Load base model pipe = FluxPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16 ) # Load LoRA weights (diffusers format) pipe.load_lora_weights("runchat/lora-06654833-d1fd-47fb-ba7d-dd8fb13ca165-w36ous", weight_name="pytorch_lora_weights.safetensors") pipe = pipe.to("cuda") # Generate image prompt = "a photo of a VICTORIA DETAILS style" image = pipe(prompt, num_inference_steps=50, guidance_scale=3.5).images[0] image.save("output.png") ``` ### WebUI (AUTOMATIC1111, ComfyUI, etc.) Download the `pytorch_lora_weights_webui.safetensors` file and place it in your WebUI's LoRA directory. Use the trigger word `VICTORIA DETAILS` in your prompts. ## Training Details - Base model: black-forest-labs/FLUX.1-dev - Training steps: 500 - Learning rate: 0.001 - Batch size: 2 - LoRA rank: 16 - Trigger word: `VICTORIA DETAILS` ## License This model is trained on Flux.1-dev and inherits its non-commercial license. Please see the [license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md) for usage restrictions.

yeok/sft-Qwen2-5-3B-Instruct-user_bias-200000

yeok

2025-08-27T15:17:11Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/Qwen2.5-3B-Instruct", "base_model:finetune:unsloth/Qwen2.5-3B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-08-27T15:17:05Z

--- base_model: unsloth/Qwen2.5-3B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** yeok - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756307730

Ferdi3425

2025-08-27T15:15:58Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:15:54Z

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

lakelee/RLB_MLP_BC_v3.20250827.22_5

lakelee

2025-08-27T15:15:25Z

0

transformers

[ "transformers", "safetensors", "mlp_swiglu", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2025-08-27T13:43:00Z

--- library_name: transformers tags: - generated_from_trainer model-index: - name: RLB_MLP_BC_v3.20250827.22_5 results: [] ---  # RLB_MLP_BC_v3.20250827.22_5 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch_fused with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 20.0 ### Training results ### Framework versions - Transformers 4.55.4 - Pytorch 2.8.0+cu128 - Tokenizers 0.21.4

SVBilenko/dqn-SpaceInvadersNoFrameskip-v4

SVBilenko

2025-08-27T15:15:18Z

0

stable-baselines3

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

reinforcement-learning

2025-08-27T15:14:49Z

--- 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: 625.00 +/- 264.58 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 SVBilenko -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 SVBilenko -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 SVBilenko ``` ## 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', 0.0001), ('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'} ```

ggozzy/blockassist-bc-stubby_yapping_mandrill_1756307553

ggozzy

2025-08-27T15:13:41Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:13:35Z

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

xinnn32/blockassist-bc-meek_winged_caterpillar_1756307565

xinnn32

2025-08-27T15:13:21Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:13:14Z

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

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756307497

Ferdi3425

2025-08-27T15:12:16Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:12:04Z

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

alestrami/AutoNerf-8B_init

alestrami

2025-08-27T15:10:03Z

0

null

[ "safetensors", "en", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "region:us" ]

null

2025-08-27T09:29:15Z

--- language: - en base_model: - meta-llama/Llama-3.1-8B ---

liukevin666/blockassist-bc-yawning_striped_cassowary_1756307171

liukevin666

2025-08-27T15:07:43Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:07:09Z

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

hnv2520/LNG_Qwen2.5VL_32B_150st_4b

hnv2520

2025-08-27T15:07:23Z

0

transformers

[ "transformers", "safetensors", "qwen2_5_vl", "image-to-text", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

image-to-text

2025-08-27T14:25:38Z

--- base_model: unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_5_vl license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** hnv2520 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2.5-VL-32B-Instruct-unsloth-bnb-4bit This qwen2_5_vl 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)

unitova/blockassist-bc-zealous_sneaky_raven_1756305398

unitova

2025-08-27T15:07:15Z

0

null

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

null

2025-08-27T15:07:11Z

--- 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).

ggozzy/blockassist-bc-stubby_yapping_mandrill_1756307057

ggozzy

2025-08-27T15:05:26Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:05:19Z

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

ChenWu98/statement_deepseek_v1.5_sft_cluster_weighted_alpha2.0_split_1

ChenWu98

2025-08-27T15:05:18Z

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:deepseek-ai/DeepSeek-Prover-V1.5-SFT", "base_model:finetune:deepseek-ai/DeepSeek-Prover-V1.5-SFT", "endpoints_compatible", "region:us" ]

null

2025-08-27T14:55:56Z

--- base_model: deepseek-ai/DeepSeek-Prover-V1.5-SFT library_name: transformers model_name: statement_deepseek_v1.5_sft_cluster_weighted_alpha2.0_split_1 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for statement_deepseek_v1.5_sft_cluster_weighted_alpha2.0_split_1 This model is a fine-tuned version of [deepseek-ai/DeepSeek-Prover-V1.5-SFT](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V1.5-SFT). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/chenwu/huggingface/runs/8fukhhjm) This model was trained with SFT. ### Framework versions - TRL: 0.19.1 - Transformers: 4.51.1 - Pytorch: 2.7.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

whaoyang/gemma-3-4b-novision-quant-rk3588-1.2.1

whaoyang

2025-08-27T15:05:16Z

0

transformers

[ "transformers", "safetensors", "gemma3_text", "text-generation", "conversational", "base_model:gghfez/gemma-3-4b-novision", "base_model:finetune:gghfez/gemma-3-4b-novision", "license:gemma", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-27T14:45:02Z

--- license: gemma base_model: - gghfez/gemma-3-4b-novision - google/gemma-3-4b-it library_name: transformers --- This version of gemma-3-4b-novision has been converted to run on the RK3588 NPU using w8a8 quantization and with the [quant_dataset.json](https://huggingface.co/whaoyang/gemma-3-4b-novision-quant-rk3588-1.2.1/blob/main/quant_dataset.json) file from this repo as the value for the dataset param of the `rkllm.build()` command. This converted rkllm model differs from my other `gemma-3-4b-novision-rk3588-1.2.1` model in that `rkllm.build()` was run with param `dataset=quant_dataset.json` instead of param `dataset=None` in the other model. The `quant_dataset.json` file was generated with Rockchip's [generate_data_quant.py](https://github.com/airockchip/rknn-llm/blob/release-v1.2.1/examples/DeepSeek-R1-Distill-Qwen-1.5B_Demo/export/generate_data_quant.py) script with the following arguments: * max_new_tokens = 448 * top_k = 64 * temperature = 0.7 * repetition_penalty = 1.0 * apply_chat_template = True This model has been optimized with the following LoRA: NA This model supports a max context length of 16384. Compatible with RKLLM version: 1.2.1 ## Recommended `rkllm` parameters This model runs well in limited testing with the following `rkllm` library paremeters: * `n_keep` = -1 * `top_k` = 64 * `top_p` = 0.95 * `temperature` = 0.7 * `repeat_penalty` = 1.0 * `frequency_penalty` = 1.0 * `presence_penalty` = 0.0 * `mirostat` = 0 * `mirostat_tau` = 5.0 * `mirostat_eta` = 0.1 It is recommended to also apply a specific chat template using the following Python methods(that hook the rkllm library): ``` # System prompt taken from https://docs.unsloth.ai/basics/gemma-3-how-to-run-and-fine-tune#official-recommended-inference-settings system_prompt = "<bos><start_of_turn>user\nHello!<end_of_turn>\n<start_of_turn>model\nHey there!<end_of_turn>\n<start_of_turn>user\nWhat is 1+1?<end_of_turn>\n<start_of_turn>model\n" prompt_prefix = "<start_of_turn>user\n" prompt_postfix = "<end_of_turn>\n<start_of_turn>model\n" rkllm_lib.rkllm_set_chat_template( llm_handle, ctypes.c_char_p(system_prompt.encode('utf-8')), ctypes.c_char_p(prompt_prefix.encode('utf-8')), ctypes.c_char_p(prompt_postfix.encode('utf-8'))) ``` ## 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

Dejiat/blockassist-bc-savage_unseen_bobcat_1756307014

Dejiat

2025-08-27T15:03:59Z

0

null

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

null

2025-08-27T15:03:56Z

--- 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).

helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1756305455

helmutsukocok

2025-08-27T15:02:46Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:02:42Z

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

ggozzy/blockassist-bc-stubby_yapping_mandrill_1756306806

ggozzy

2025-08-27T15:01:17Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T15:01:11Z

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

leonMW/DeepSeek-R1-Distill-Qwen-7B-GSPO-Basic

leonMW

2025-08-27T15:01:00Z

237

1

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "grpo", "trl", "conversational", "arxiv:2402.03300", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-09T19:03:32Z

--- base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B library_name: transformers model_name: DeepSeek-R1-Distill-Qwen-7B-GSPO-Basic tags: - generated_from_trainer - grpo - trl licence: license --- # Model Card for DeepSeek-R1-Distill-Qwen-7B-GSPO-Basic This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="leonMW/DeepSeek-R1-Distill-Qwen-7B-GSPO-Basic", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/leonwenderoth-tu-darmstadt/huggingface/runs/9vbuyz3z) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.0 - Pytorch: 2.6.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

mradermacher/smollm2-1.7b-orca-GGUF

mradermacher

2025-08-27T15:00:58Z

0

transformers

[ "transformers", "gguf", "en", "base_model:ketchup123/smollm2-1.7b-orca", "base_model:quantized:ketchup123/smollm2-1.7b-orca", "endpoints_compatible", "region:us", "conversational" ]

null

2025-08-27T14:35:44Z

--- base_model: ketchup123/smollm2-1.7b-orca language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About         static quants of https://huggingface.co/ketchup123/smollm2-1.7b-orca  ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#smollm2-1.7b-orca-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/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q2_K.gguf) | Q2_K | 0.8 | | | [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q3_K_S.gguf) | Q3_K_S | 0.9 | | | [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality | | [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q3_K_L.gguf) | Q3_K_L | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.IQ4_XS.gguf) | IQ4_XS | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q4_K_S.gguf) | Q4_K_S | 1.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q5_K_S.gguf) | Q5_K_S | 1.3 | | | [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q5_K_M.gguf) | Q5_K_M | 1.3 | | | [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q6_K.gguf) | Q6_K | 1.5 | very good quality | | [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.Q8_0.gguf) | Q8_0 | 1.9 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/smollm2-1.7b-orca-GGUF/resolve/main/smollm2-1.7b-orca.f16.gguf) | f16 | 3.5 | 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.

Cholan1986/my_policy

Cholan1986

2025-08-27T15:00:47Z

0

lerobot

[ "lerobot", "safetensors", "robotics", "act", "dataset:Cholan1986/record-redcube", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]

robotics

2025-08-27T14:59:10Z

--- datasets: Cholan1986/record-redcube library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - lerobot - robotics - act --- # Model Card for act  [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python -m lerobot.scripts.train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash python -m lerobot.record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - **License:** apache-2.0

koloni/blockassist-bc-deadly_graceful_stingray_1756305136

koloni

2025-08-27T14:59:15Z

0

null

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

null

2025-08-27T14:59:11Z

--- 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).

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756306658

Ferdi3425

2025-08-27T14:58:09Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:58:05Z

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

hflqf88888/SWIRL_MATH

hflqf88888

2025-08-27T14:57:57Z

0

null

[ "safetensors", "dataset:hflqf88888/SWIRL_MATH_data", "base_model:Qwen/Qwen2.5-Coder-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-3B-Instruct", "license:cc-by-4.0", "region:us" ]

null

2025-08-25T03:04:43Z

--- license: cc-by-4.0 datasets: - hflqf88888/SWIRL_MATH_data base_model: - Qwen/Qwen2.5-Coder-3B-Instruct --- The instantiation of SWIRL's dual-agent architecture in math reasoning. The *Teacher* provides a concise outline of the problem-solving approach, while the *Student* generates the final solution by following this guidance. This separation of roles enables structured reasoning and improves overall solution quality. For more details, please refer to our [project repository](https://github.com/Lqf-HFNJU/SWIRL).

liukevin666/blockassist-bc-yawning_striped_cassowary_1756306503

liukevin666

2025-08-27T14:56:01Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:55:55Z

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

xinnn32/blockassist-bc-meek_winged_caterpillar_1756306469

xinnn32

2025-08-27T14:55:06Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "meek winged caterpillar", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:54:58Z

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

boahancock/blockassist-bc-iridescent_rapid_toad_1756306118

boahancock

2025-08-27T14:54:43Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "iridescent rapid toad", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:50:32Z

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

Member42/blockassist-bc-pensive_agile_macaque_1756306369

Member42

2025-08-27T14:54:12Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pensive agile macaque", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:54:05Z

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

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756306330

Ferdi3425

2025-08-27T14:52:44Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:52:33Z

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

mang3dd/blockassist-bc-tangled_slithering_alligator_1756304704

mang3dd

2025-08-27T14:51:38Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:51:34Z

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

Dejiat/blockassist-bc-savage_unseen_bobcat_1756306221

Dejiat

2025-08-27T14:50:48Z

0

null

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

null

2025-08-27T14:50:45Z

--- 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).

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756306207

Ferdi3425

2025-08-27T14:50:38Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:50:35Z

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

WillLedd/q-FrozenLake-v1-4x4-noSlippery

WillLedd

2025-08-27T14:49:48Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2025-08-27T14:49:42Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="WillLedd/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

lautan/blockassist-bc-gentle_patterned_goat_1756304356

lautan

2025-08-27T14:47:28Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle patterned goat", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:47:24Z

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

raj4536/blockassist-bc-thick_amphibious_meerkat_1756305951

raj4536

2025-08-27T14:46:50Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thick amphibious meerkat", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:46:36Z

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

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756305902

Ferdi3425

2025-08-27T14:45:36Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:45:25Z

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

byzadgan/blockassist-bc-omnivorous_bold_wombat_1756303985

byzadgan

2025-08-27T14:45:16Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "omnivorous bold wombat", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:45:13Z

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

elliepreed/bgpt-french-english

elliepreed

2025-08-27T14:44:16Z

0

null

[ "pytorch", "safetensors", "gpt2", "fr", "en", "region:us" ]

null

2025-08-27T14:16:16Z

--- language: - fr - en --- library_name: transformers tags: - gpt2 - causal-lm - bilingual - sentencepiece - french - english pipeline_tag: text-generation datasets: - climb-mao/babylm-fra - elliepreed/l2-corpus-10m license: other # change to "apache-2.0" or "mit" if that's correct model-index: - name: BGPT (French+English) – 128k steps results: [] --- # BGPT – French + English (GPT-2 style) Small bilingual GPT-2–style language model trained on **French** and **English** with **SentencePiece** tokenizers. This model is trained on **both French 🇫🇷 and English 🇬🇧**, but it does not come with a single `AutoTokenizer`. Instead, we provide **two SentencePiece tokenizers**: - `tokenizers/french.model` - `tokenizers/english.model` You can load either depending on the language you want to work with. # Load the model from transformers import AutoModelForCausalLM import torch model_id = "elliepreed/bgpt-french-english" device = "cuda" if torch.cuda.is_available() else "cpu" model = AutoModelForCausalLM.from_pretrained(model_id).to(device).eval() # Load both tokenizers import sentencepiece as spm from huggingface_hub import hf_hub_download fr_path = hf_hub_download(model_id, "tokenizers/french.model") en_path = hf_hub_download(model_id, "tokenizers/english.model") sp_fr = spm.SentencePieceProcessor(model_file=fr_path) sp_en = spm.SentencePieceProcessor(model_file=en_path) # Example: French generation prompt = "Paris est" ids = sp_fr.encode(prompt, out_type=int) + [sp_fr.eos_id()] input_ids = torch.tensor([ids], device=device) out = model.generate( input_ids, max_new_tokens=40, do_sample=True, top_p=0.95, temperature=0.9, eos_token_id=sp_fr.eos_id(), pad_token_id=sp_fr.pad_id(), ) print("FR:", sp_fr.decode(out[0].tolist()[len(ids):]))

Member42/blockassist-bc-pensive_agile_macaque_1756305754

Member42

2025-08-27T14:44:03Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pensive agile macaque", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:43:56Z

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

danieall/blockassist-bc-humming_gilded_rabbit_1756303951

danieall

2025-08-27T14:43:53Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "humming gilded rabbit", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:43:50Z

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

mahir05/ppo-LunarLander-v2

mahir05

2025-08-27T14:43:41Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2025-08-27T14:43:23Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 270.84 +/- 18.89 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756305640

Ferdi3425

2025-08-27T14:41:08Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "amphibious deadly otter", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:41:04Z

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

sakamotoz/blockassist-bc-silent_shaggy_rabbit_1756303971

sakamotoz

2025-08-27T14:40:53Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silent shaggy rabbit", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:40:40Z

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

demomern/custom_Qwen2.5-1.5B-Instruct

demomern

2025-08-27T14:39:43Z

0

transformers

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

null

2025-08-18T08:07:28Z

--- 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]

Kimz1/act-so100-policy-0827-5

Kimz1

2025-08-27T14:38:41Z

0

lerobot

[ "lerobot", "safetensors", "act", "robotics", "dataset:Kimz1/so100-teleop-record-0826-1", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]

robotics

2025-08-27T14:38:16Z

--- datasets: Kimz1/so100-teleop-record-0826-1 library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - act - robotics - lerobot --- # Model Card for act  [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash python lerobot/scripts/train.py \ --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

alessiodevoto/exp_att_stats_meta-llama_Llama-3.1-8B-Instruct_kmfoda_booksum_100_1000_4

alessiodevoto

2025-08-27T14:36:29Z

0

null

[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]

null

2025-08-27T14:36:25Z

--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]

ggozzy/blockassist-bc-stubby_yapping_mandrill_1756305311

ggozzy

2025-08-27T14:36:19Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:36:13Z

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

acidjp/blockassist-bc-pesty_extinct_prawn_1756302970

acidjp

2025-08-27T14:35:44Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pesty extinct prawn", "arxiv:2504.07091", "region:us" ]

null

2025-08-27T14:35:40Z

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

SelmaNajih001/ModelloGRPOMinstral

SelmaNajih001

2025-08-27T14:34:54Z

0

transformers

[ "transformers", "safetensors", "text-generation", "en", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1", "endpoints_compatible", "region:us" ]

text-generation

2025-08-27T11:37:25Z

--- library_name: transformers language: - en base_model: - mistralai/Mistral-7B-Instruct-v0.1 pipeline_tag: text-generation --- # 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]