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modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
kammbo/klue-roberta-base-klue-sts	kammbo	2025-04-29T01:37:15Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:10501", "loss:CosineSimilarityLoss", "arxiv:1908.10084", "base_model:klue/roberta-base", "base_model:finetune:klue/roberta-base", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-29T01:36:22Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10501 - loss:CosineSimilarityLoss base_model: klue/roberta-base widget: - source_sentence: 유럽발(發) 입국자 검역 강화조치가 시행된 첫 날인 22일 유증상자 152명이 공항 격리시설에서 격리 및 진단검사를 받았다. sentences: - 이 경우 격리 면제된 해외 기업인이 국내 입국 시 임시격리시설(1박 2일)에서 검사를 받은 후 검사결과 음성으로 판정될 경우 최종적으로 격리 면제가 이뤄진다. - 급하게 메일을 보내지 말고 발송 전에 차분하게 2번 확인하도록 해 - 트립닷컴 예약 시 메일을 받으면 어떤 정보가 담겨있어? - source_sentence: 숙소와 체크인 장소가 좀 다르니 참고하세요. sentences: - 에딘버러에 들르실 예정이라면 강추합니다! - 화장실이 상대적으로 좁지만 불편할정도는 아닙니다. - 숙박시설과 체크인 장소가 조금 다릅니다. - source_sentence: 내일 황사 지수 알려줘. sentences: - 늦어봤자 좋을 거 없으니까 회사 상사하고 저녁 약속에는 늦지 마. - 최대 강수 시간대는? 서울 지역. - 올해 17조 5000억원에서 2024년에는 21조까지 확대할 방침이다. - source_sentence: 내년 상반기에는 ‘생산성 혁신을 위한 중장기 전략과제’를 수립하는 동시에 고령자들의 계속고용이 활성화될 수 있도록 기업 고용부담을 완화한 새로운 고령자 일자리 모델도 마련한다. sentences: - 문화체육관광부가 코로나19 극복을 위한 응원 로고를 국민들이 무료로 활용할 수 있도록 배포한다. - 현재는 마감일 이후 신고자의 신고에 오류가 있을 경우 정정 요청 및 정정신고가 불가능했으나 앞으로는 신고자에게도 정정 기회를 부여합니다. - 시내와 지하철 사이의 거리는 가까웠습니다. - source_sentence: 와디즈에서 보내는 광고 메일은 받지마 sentences: - 지메일을 쓰면 첨부파일을 몇개까지 보낼 수 있지? - 문 여는 방법도 비교적 쉽고 간단합니다! - 그들은 나에게 맥주 두 캔과 주스 그리고 물을 주었습니다. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on klue/roberta-base results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: Unknown type: unknown metrics: - type: pearson_cosine value: 0.3477070672169138 name: Pearson Cosine - type: spearman_cosine value: 0.35560473197486514 name: Spearman Cosine - type: pearson_cosine value: 0.9609074593444991 name: Pearson Cosine - type: spearman_cosine value: 0.9191116352550575 name: Spearman Cosine --- # SentenceTransformer based on klue/roberta-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [klue/roberta-base](https://huggingface.co/klue/roberta-base). 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: [klue/roberta-base](https://huggingface.co/klue/roberta-base) <!-- at revision 02f94ba5e3fcb7e2a58a390b8639b0fac974a8da --> - Maximum Sequence Length: 512 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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## 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 = [ '와디즈에서 보내는 광고 메일은 받지마', '지메일을 쓰면 첨부파일을 몇개까지 보낼 수 있지?', '문 여는 방법도 비교적 쉽고 간단합니다!', ] 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 #### Semantic Similarity * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.3477 \| \| spearman_cosine \| 0.3556 \| #### Semantic Similarity * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.9609 \| \| spearman_cosine \| 0.9191 \| <!-- ## 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: 10,501 training samples * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| label \| \|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 7 tokens</li><li>mean: 20.09 tokens</li><li>max: 60 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 19.5 tokens</li><li>max: 55 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| label \| \|:-------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------\|:---------------------------------\| \| <code>지난 1990년 핀란드에서 처음 도입한 ‘탄소세’를 온실가스 저감 수단으로 활용하려는 각국의 움직임도 활발하다.</code> \| <code>각국은 또한 온실 가스 배출을 줄이기 위한 수단으로 1990년 핀란드에서 처음 도입된 '탄소 세금'을 적극적으로 사용하려고 하고 있습니다.</code> \| <code>0.42000000000000004</code> \| \| <code>그러므로 역 근처 숙소가 가장 편리하다고 생각합니다.</code> \| <code>그래서 저는 역 근처에 있는 숙소가 가장 편리하다고 생각합니다.</code> \| <code>0.82</code> \| \| <code>도는 그 일환으로 BC카드 매출의 64%는 10억원 이상 매장에서 사용되는 반면 지역화폐는 3억원 미만 매장에서 사용되는 비율이 36.7%라는 근거를 제시했다.</code> \| <code>서울의 15억원 초과 집값 상승률은 12월 3주 0.40%에서 1월 1주 -0.08%로 하락 전환했다.</code> \| <code>0.0</code> \| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `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`: 16 - `per_device_eval_batch_size`: 16 - `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`: 3 - `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} - `tp_size`: 0 - `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`: None - `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 - `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 \| Epoch \| Step \| Training Loss \| spearman_cosine \| \|:------:\|:----:\|:-------------:\|:---------------:\| \| -1 \| -1 \| - \| 0.3556 \| \| 0.0761 \| 50 \| - \| 0.8659 \| \| 0.1522 \| 100 \| - \| 0.8890 \| \| 0.2283 \| 150 \| - \| 0.8969 \| \| 0.3044 \| 200 \| - \| 0.9008 \| \| 0.3805 \| 250 \| - \| 0.9026 \| \| 0.4566 \| 300 \| - \| 0.9055 \| \| 0.5327 \| 350 \| - \| 0.9023 \| \| 0.6088 \| 400 \| - \| 0.9076 \| \| 0.6849 \| 450 \| - \| 0.9019 \| \| 0.7610 \| 500 \| 0.0282 \| 0.9067 \| \| 0.8371 \| 550 \| - \| 0.9060 \| \| 0.9132 \| 600 \| - \| 0.9090 \| \| 0.9893 \| 650 \| - \| 0.9074 \| \| 1.0 \| 657 \| - \| 0.9077 \| \| 1.0654 \| 700 \| - \| 0.9091 \| \| 1.1416 \| 750 \| - \| 0.9120 \| \| 1.2177 \| 800 \| - \| 0.9085 \| \| 1.2938 \| 850 \| - \| 0.9117 \| \| 1.3699 \| 900 \| - \| 0.9137 \| \| 1.4460 \| 950 \| - \| 0.9126 \| \| 1.5221 \| 1000 \| 0.008 \| 0.9137 \| \| 1.5982 \| 1050 \| - \| 0.9148 \| \| 1.6743 \| 1100 \| - \| 0.9155 \| \| 1.7504 \| 1150 \| - \| 0.9134 \| \| 1.8265 \| 1200 \| - \| 0.9141 \| \| 1.9026 \| 1250 \| - \| 0.9142 \| \| 1.9787 \| 1300 \| - \| 0.9155 \| \| 2.0 \| 1314 \| - \| 0.9163 \| \| 2.0548 \| 1350 \| - \| 0.9174 \| \| 2.1309 \| 1400 \| - \| 0.9177 \| \| 2.2070 \| 1450 \| - \| 0.9171 \| \| 2.2831 \| 1500 \| 0.005 \| 0.9191 \| ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## 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", } ``` <!-- ## 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. -->
wangyingjia8/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_wily_ant	wangyingjia8	2025-04-29T01:37:02Z	4	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am whiskered wily ant", "trl", "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	2025-04-22T09:56:38Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_wily_ant tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am whiskered wily ant - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_wily_ant 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="wangyingjia8/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_wily_ant", 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: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
jimmypan/llama381binstruct_summarize_short	jimmypan	2025-04-29T01:36:57Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:NousResearch/Meta-Llama-3.1-8B-Instruct", "base_model:finetune:NousResearch/Meta-Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-04-29T01:36:39Z	--- base_model: NousResearch/Meta-Llama-3.1-8B-Instruct library_name: transformers model_name: llama381binstruct_summarize_short tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for llama381binstruct_summarize_short This model is a fine-tuned version of [NousResearch/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3.1-8B-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="jimmypan/llama381binstruct_summarize_short", 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/moonshade9-amazon/huggingface/runs/piexpda8) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.1 - 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}} } ```
magvtv/rada-nlp	magvtv	2025-04-29T01:36:05Z	46	1	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-15T08:45:50Z	--- library_name: transformers tags: - generated_from_trainer metrics: - rouge model-index: - name: rada-nlp 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. --> # rada-nlp This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6418 - Rouge1: 32.2628 - Rouge2: 17.6188 - Rougel: 28.3685 - Rougelsum: 28.3035 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\| \| 1.8877 \| 1.0 \| 4 \| 2.6676 \| 32.1274 \| 17.427 \| 27.543 \| 28.0416 \| \| 1.8556 \| 2.0 \| 8 \| 2.6705 \| 31.2511 \| 16.3095 \| 26.6854 \| 26.8166 \| \| 1.8127 \| 3.0 \| 12 \| 2.6705 \| 31.037 \| 16.0077 \| 26.813 \| 26.6464 \| \| 1.784 \| 4.0 \| 16 \| 2.6686 \| 31.5008 \| 16.2333 \| 26.9957 \| 26.7969 \| \| 1.7672 \| 5.0 \| 20 \| 2.6711 \| 31.2118 \| 15.9968 \| 26.9476 \| 26.9864 \| \| 1.7407 \| 6.0 \| 24 \| 2.6716 \| 31.4189 \| 15.9951 \| 26.8681 \| 26.7424 \| \| 1.742 \| 7.0 \| 28 \| 2.6701 \| 30.9705 \| 16.0005 \| 26.5473 \| 26.8081 \| \| 1.7356 \| 8.0 \| 32 \| 2.6687 \| 31.906 \| 17.254 \| 27.7267 \| 27.6687 \| \| 1.7271 \| 9.0 \| 36 \| 2.6654 \| 31.8302 \| 17.1851 \| 27.4294 \| 27.4945 \| \| 1.7224 \| 10.0 \| 40 \| 2.6606 \| 31.5091 \| 17.1353 \| 27.8425 \| 27.5751 \| \| 1.7207 \| 11.0 \| 44 \| 2.6575 \| 31.6189 \| 17.3582 \| 27.5163 \| 27.519 \| \| 1.7404 \| 12.0 \| 48 \| 2.6539 \| 32.0071 \| 17.1878 \| 27.6051 \| 27.7916 \| \| 1.7213 \| 13.0 \| 52 \| 2.6504 \| 32.6314 \| 17.5002 \| 28.0328 \| 28.0245 \| \| 1.7606 \| 14.0 \| 56 \| 2.6472 \| 32.5161 \| 17.4726 \| 28.16 \| 28.4421 \| \| 1.7839 \| 15.0 \| 60 \| 2.6444 \| 32.3599 \| 17.9836 \| 27.9445 \| 28.0023 \| \| 1.812 \| 16.0 \| 64 \| 2.6418 \| 32.2628 \| 17.6188 \| 28.3685 \| 28.3035 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
li55555/zephyr_spin_iter2	li55555	2025-04-29T01:33:02Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T01:29:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. (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]
Zack-Z/gemma3_27bi_cotsft_rs0_3_5cut_ru_gem3_e2	Zack-Z	2025-04-29T01:30:25Z	0	0	transformers	[ "transformers", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-27b-it", "base_model:finetune:unsloth/gemma-3-27b-it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T23:58:31Z	--- base_model: unsloth/gemma-3-27b-it tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Zack-Z - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-27b-it This gemma3 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)
mhr2004/roberta-large-stsb-lr2e-05-bs32	mhr2004	2025-04-29T01:27:13Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-large", "base_model:finetune:FacebookAI/roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-29T01:11:06Z	--- library_name: transformers license: mit base_model: roberta-large tags: - generated_from_trainer model-index: - name: roberta-large-stsb-lr2e-05-bs32 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. --> # roberta-large-stsb-lr2e-05-bs32 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0166 - Pearson: 0.9185 - Spearman: 0.9187 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Pearson \| Spearman \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:--------:\| \| 0.0509 \| 1.0 \| 180 \| 0.0232 \| 0.8807 \| 0.8813 \| \| 0.0327 \| 2.0 \| 360 \| 0.0201 \| 0.9042 \| 0.9041 \| \| 0.0263 \| 3.0 \| 540 \| 0.0165 \| 0.9119 \| 0.9097 \| \| 0.0216 \| 4.0 \| 720 \| 0.0223 \| 0.9162 \| 0.9153 \| \| 0.0206 \| 5.0 \| 900 \| 0.0143 \| 0.9188 \| 0.9175 \| \| 0.0183 \| 6.0 \| 1080 \| 0.0186 \| 0.9180 \| 0.9164 \| \| 0.0161 \| 7.0 \| 1260 \| 0.0151 \| 0.9220 \| 0.9203 \| \| 0.0137 \| 8.0 \| 1440 \| 0.0141 \| 0.9203 \| 0.9189 \| \| 0.0124 \| 9.0 \| 1620 \| 0.0179 \| 0.9218 \| 0.9200 \| \| 0.0112 \| 10.0 \| 1800 \| 0.0144 \| 0.9215 \| 0.9214 \| \| 0.0113 \| 11.0 \| 1980 \| 0.0150 \| 0.9218 \| 0.9198 \| \| 0.0093 \| 12.0 \| 2160 \| 0.0144 \| 0.9181 \| 0.9171 \| \| 0.0089 \| 13.0 \| 2340 \| 0.0166 \| 0.9185 \| 0.9187 \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.6.0+cu124 - Datasets 3.3.2 - Tokenizers 0.21.1
Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q6_K-GGUF	Lucy-in-the-Sky	2025-04-29T01:26:34Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition", "base_model:quantized:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-29T01:25:08Z	--- base_model: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q6_K-GGUF This model was converted to GGUF format from [`cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition`](https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q6_K-GGUF --hf-file dolphin-mistral-24b-venice-edition-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q6_K-GGUF --hf-file dolphin-mistral-24b-venice-edition-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q6_K-GGUF --hf-file dolphin-mistral-24b-venice-edition-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q6_K-GGUF --hf-file dolphin-mistral-24b-venice-edition-q6_k.gguf -c 2048 ```
greenwich157/Qwen2.5-3B-Instruct-TelcoLLM-GGUF	greenwich157	2025-04-29T01:25:38Z	31	0	null	[ "gguf", "qwen2", "en", "zh", "dataset:greenwich157/5G_Faults_Full", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:quantized:Qwen/Qwen2.5-3B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-27T02:31:19Z	--- license: apache-2.0 datasets: - greenwich157/5G_Faults_Full language: - en - zh base_model: - Qwen/Qwen2.5-3B-Instruct --- 5G mobile network faults suitable for engineer evaluation, based on synthetic dataset
Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q6_K-GGUF	Lucy-in-the-Sky	2025-04-29T01:24:56Z	7	0	transformers	[ "transformers", "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-02-20T21:14:16Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara pipeline_tag: text-generation base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - chat - llama-cpp - gguf-my-repo library_name: transformers --- # Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q6_K-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q6_K-GGUF --hf-file qwen2.5-1.5b-instruct-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q6_K-GGUF --hf-file qwen2.5-1.5b-instruct-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q6_K-GGUF --hf-file qwen2.5-1.5b-instruct-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Lucy-in-the-Sky/Qwen2.5-1.5B-Instruct-Q6_K-GGUF --hf-file qwen2.5-1.5b-instruct-q6_k.gguf -c 2048 ```
infogep/8559b4d9-7871-4dbb-ac96-1b77aaa15f2f	infogep	2025-04-29T01:21:57Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3.5-mini-instruct", "base_model:adapter:unsloth/Phi-3.5-mini-instruct", "license:mit", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T01:15:53Z	--- library_name: peft license: mit base_model: unsloth/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: 8559b4d9-7871-4dbb-ac96-1b77aaa15f2f 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/Phi-3.5-mini-instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 67114b4672ccfa56_train_data.json ds_type: json format: custom path: /workspace/input_data/67114b4672ccfa56_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: infogep/8559b4d9-7871-4dbb-ac96-1b77aaa15f2f hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/67114b4672ccfa56_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 4365af0f-8b36-406d-b2f7-4d21c6c582bd wandb_project: s56-30 wandb_run: your_name wandb_runid: 4365af0f-8b36-406d-b2f7-4d21c6c582bd warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 8559b4d9-7871-4dbb-ac96-1b77aaa15f2f This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 9.1212 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 7.4729 \| 0.1201 \| 200 \| 9.1212 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
marialvsantiago/fb5d6bfb-6a71-4d7a-93bf-7e0f852fae50	marialvsantiago	2025-04-29T01:21:33Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3.5-mini-instruct", "base_model:adapter:unsloth/Phi-3.5-mini-instruct", "license:mit", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T01:17:14Z	--- library_name: peft license: mit base_model: unsloth/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: fb5d6bfb-6a71-4d7a-93bf-7e0f852fae50 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Phi-3.5-mini-instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 67114b4672ccfa56_train_data.json ds_type: json format: custom path: /workspace/input_data/67114b4672ccfa56_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: marialvsantiago/fb5d6bfb-6a71-4d7a-93bf-7e0f852fae50 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/67114b4672ccfa56_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 4365af0f-8b36-406d-b2f7-4d21c6c582bd wandb_project: s56-33 wandb_run: your_name wandb_runid: 4365af0f-8b36-406d-b2f7-4d21c6c582bd warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # fb5d6bfb-6a71-4d7a-93bf-7e0f852fae50 This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 9.0095 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 7.4489 \| 0.1201 \| 200 \| 9.0095 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q4_K_M-GGUF	Lucy-in-the-Sky	2025-04-29T01:18:00Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition", "base_model:quantized:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-29T01:16:49Z	--- base_model: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q4_K_M-GGUF This model was converted to GGUF format from [`cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition`](https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q4_K_M-GGUF --hf-file dolphin-mistral-24b-venice-edition-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q4_K_M-GGUF --hf-file dolphin-mistral-24b-venice-edition-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q4_K_M-GGUF --hf-file dolphin-mistral-24b-venice-edition-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Lucy-in-the-Sky/Dolphin-Mistral-24B-Venice-Edition-Q4_K_M-GGUF --hf-file dolphin-mistral-24b-venice-edition-q4_k_m.gguf -c 2048 ```
jayellho/whisper-large-v3-turbo-imda-part4-bs32-grad4-dl4-4h200-splbat-16cpus-optiLR-1600maxst-perstwkrs	jayellho	2025-04-29T01:16:44Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:data_loading_script", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-29T00:38:32Z	--- library_name: transformers tags: - generated_from_trainer datasets: - data_loading_script model-index: - name: whisper-large-v3-turbo-imda-part4-bs32-grad4-dl4-4h200-splbat-16cpus-optiLR-1600maxst-perstwkrs results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-large-v3-turbo-imda-part4-bs32-grad4-dl4-4h200-splbat-16cpus-optiLR-1600maxst-perstwkrs This model was trained from scratch on the data_loading_script 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: 1.0417199205400627e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - total_eval_batch_size: 128 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 160 - training_steps: 1600 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.4.1+cu121 - Datasets 3.5.0 - Tokenizers 0.21.1
peterwa/Qwen2.5-7B-instruct-GRPO-GSM8K	peterwa	2025-04-29T01:16:35Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T01:09:16Z	--- 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. 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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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vmpsergio/27ddc76e-6f2a-404d-8369-0ec4c2735092	vmpsergio	2025-04-29T01:16:10Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:defog/sqlcoder-7b-2", "base_model:adapter:defog/sqlcoder-7b-2", "license:cc-by-sa-4.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T00:39:10Z	--- library_name: peft license: cc-by-sa-4.0 base_model: defog/sqlcoder-7b-2 tags: - axolotl - generated_from_trainer model-index: - name: 27ddc76e-6f2a-404d-8369-0ec4c2735092 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: defog/sqlcoder-7b-2 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 09fd8de16e0ef037_train_data.json ds_type: json format: custom path: /workspace/input_data/09fd8de16e0ef037_train_data.json type: field_input: Patient field_instruction: Description field_output: Doctor format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vmpsergio/27ddc76e-6f2a-404d-8369-0ec4c2735092 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/09fd8de16e0ef037_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: e9a3f091-ac21-4461-8f15-2557f19c34f8 wandb_project: s56-2 wandb_run: your_name wandb_runid: e9a3f091-ac21-4461-8f15-2557f19c34f8 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 27ddc76e-6f2a-404d-8369-0ec4c2735092 This model is a fine-tuned version of [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5952 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.0341 \| 0.0066 \| 200 \| 2.5952 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nHTDayQrFAXhHAY/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-timid_fierce_ladybug	nHTDayQrFAXhHAY	2025-04-29T01:15:51Z	2	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am timid fierce ladybug", "trl", "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	2025-04-22T15:41:05Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-timid_fierce_ladybug tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am timid fierce ladybug - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-timid_fierce_ladybug 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="nHTDayQrFAXhHAY/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-timid_fierce_ladybug", 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: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
joelm/llama-3.1-8b-ai-to-pg-finetune-GGUF	joelm	2025-04-29T01:15:24Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-29T01:14:23Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: joelm - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
tFQbekUPTuNgAxFkR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_wiry_butterfly	tFQbekUPTuNgAxFkR	2025-04-29T01:11:45Z	6	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am lightfooted wiry butterfly", "trl", "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	2025-04-22T11:52:29Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_wiry_butterfly tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am lightfooted wiry butterfly - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_wiry_butterfly 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="tFQbekUPTuNgAxFkR/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-lightfooted_wiry_butterfly", 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: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
Cozmicalz/Irix-12B-Model_Stock-mlx-4Bit	Cozmicalz	2025-04-29T01:10:22Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "mlx", "mlx-my-repo", "conversational", "base_model:DreadPoor/Irix-12B-Model_Stock", "base_model:quantized:DreadPoor/Irix-12B-Model_Stock", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	2025-04-29T01:09:56Z	--- base_model: DreadPoor/Irix-12B-Model_Stock library_name: transformers tags: - mergekit - merge - mlx - mlx-my-repo --- # Cozmicalz/Irix-12B-Model_Stock-mlx-4Bit The Model [Cozmicalz/Irix-12B-Model_Stock-mlx-4Bit](https://huggingface.co/Cozmicalz/Irix-12B-Model_Stock-mlx-4Bit) was converted to MLX format from [DreadPoor/Irix-12B-Model_Stock](https://huggingface.co/DreadPoor/Irix-12B-Model_Stock) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("Cozmicalz/Irix-12B-Model_Stock-mlx-4Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
phospho-app/nebo1337-GetTheRubberNextG2-mkz2etcus0	phospho-app	2025-04-29T01:03:39Z	0	0	null	[ "safetensors", "phosphobot", "act", "region:us" ]	null	2025-04-28T23:54:23Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [nebo1337/GetTheRubberNextG2](https://huggingface.co/datasets/nebo1337/GetTheRubberNextG2) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 8000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
BSC-NLP4BIA/BIOMAT-AnatNER-MTL	BSC-NLP4BIA	2025-04-29T01:01:01Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-04-29T01:00:21Z	--- 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]
bmFVHfwBm0ktSackD3/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-waddling_colorful_ostrich	bmFVHfwBm0ktSackD3	2025-04-29T00:59:34Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am waddling colorful ostrich", "trl", "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	2025-04-22T13:27:03Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-waddling_colorful_ostrich tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am waddling colorful ostrich - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-waddling_colorful_ostrich 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="bmFVHfwBm0ktSackD3/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-waddling_colorful_ostrich", 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: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
VishnuT/llama3-qlora-phase2.2-adapter	VishnuT	2025-04-29T00:56:22Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-3B", "base_model:adapter:meta-llama/Llama-3.2-3B", "region:us" ]	null	2025-04-29T00:49:10Z	--- base_model: meta-llama/Llama-3.2-3B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
joelm/llama-3.1-8b-ai-to-pg-finetune-16bit	joelm	2025-04-29T00:55:54Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T00:54:37Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: joelm - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
fQNrIdeWOYvDBCMqov/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grassy_mammalian_macaque	fQNrIdeWOYvDBCMqov	2025-04-29T00:55:10Z	3	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am grassy mammalian macaque", "trl", "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	2025-04-22T11:38:09Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grassy_mammalian_macaque tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am grassy mammalian macaque - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grassy_mammalian_macaque 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="fQNrIdeWOYvDBCMqov/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grassy_mammalian_macaque", 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: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
MikeRoz/TheDrummer_Fallen-Gemma3-27B-v1-6.0bpw-h8-exl2	MikeRoz	2025-04-29T00:55:08Z	0	0	null	[ "safetensors", "gemma3_text", "exl2", "license:other", "6-bit", "region:us" ]	null	2025-04-28T23:29:21Z	--- license: other base_model: TheDrummer/Fallen-Gemma3-27b-v1 base_model_relation: quantized tags: - exl2 --- This model was quantized using commit 3a90264 of the dev branch of exllamav2. The Gemma 3 8k context bug looks to be thoroughly squashed as of this commit. To use this model, please either build your own copy of exllamav2 from the dev branch, or wait for the forthcoming v0.2.9 release. The original model can be found [here](https://huggingface.co/TheDrummer/Fallen-Gemma3-27B-v1). # Join our Discord! https://discord.gg/Nbv9pQ88Xb ## Nearly 5000 members of helpful, LLM enthusiasts! A hub for players and makers alike! --- [BeaverAI](https://huggingface.co/BeaverAI) proudly presents... # Fallen Gemma3 27B v1 👺 ![image/gif](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/9oyZxzpfhmmNr21S1P_iJ.gif) ## Special Thanks - Thank you to each and everyone who donated and subscribed in [Patreon](https://www.patreon.com/TheDrummer) and [Ko-Fi](https://ko-fi.com/thedrummer) to make our venture a little bit easier. - I'm also recently unemployed. I am a Software Developer with 8 years of experience in Web, API, AI, and adapting to new tech and requirements. If you're hiring, feel free to reach out to me however. ## Usage - Use Gemma Chat Template ## Description Fallen Gemma3 27B v1 is an evil tune of Gemma 3 27B but it is not a complete decensor. Evil tunes knock out the positivity and may enjoy torturing you and humanity. Vision still works and it has something to say about the crap you feed it. ## Links - Original: https://huggingface.co/TheDrummer/Fallen-Gemma3-27B-v1 - GGUF: https://huggingface.co/TheDrummer/Fallen-Gemma3-27B-v1-GGUF - iMatrix (recommended): https://huggingface.co/bartowski/TheDrummer_Fallen-Gemma3-27B-v1-GGUF `config-v1c`
pandorakevin/pandorakevi	pandorakevin	2025-04-29T00:54:33Z	0	0	null	[ "license:bsd-3-clause-clear", "region:us" ]	null	2025-04-29T00:54:33Z	--- license: bsd-3-clause-clear ---
thomasjthe/SmolLM2-FT-MyDataset	thomasjthe	2025-04-29T00:53:32Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "smol-course", "module_1", "trl", "sft", "conversational", "base_model:HuggingFaceTB/SmolLM2-135M", "base_model:finetune:HuggingFaceTB/SmolLM2-135M", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T00:52:41Z	--- base_model: HuggingFaceTB/SmolLM2-135M library_name: transformers model_name: SmolLM2-FT-MyDataset tags: - generated_from_trainer - smol-course - module_1 - trl - sft licence: license --- # Model Card for SmolLM2-FT-MyDataset This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M). 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="thomasjthe/SmolLM2-FT-MyDataset", 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/thomashe42-university-of-melbourne/huggingface/runs/z86k0ddc) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.1 - 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}} } ```
kokovova/c172c020-3d0e-4a0c-a72f-0af785cff78b	kokovova	2025-04-29T00:53:22Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:defog/sqlcoder-7b-2", "base_model:adapter:defog/sqlcoder-7b-2", "license:cc-by-sa-4.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T00:40:12Z	--- library_name: peft license: cc-by-sa-4.0 base_model: defog/sqlcoder-7b-2 tags: - axolotl - generated_from_trainer model-index: - name: c172c020-3d0e-4a0c-a72f-0af785cff78b 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: defog/sqlcoder-7b-2 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 09fd8de16e0ef037_train_data.json ds_type: json format: custom path: /workspace/input_data/09fd8de16e0ef037_train_data.json type: field_input: Patient field_instruction: Description field_output: Doctor format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: kokovova/c172c020-3d0e-4a0c-a72f-0af785cff78b hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/09fd8de16e0ef037_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: e9a3f091-ac21-4461-8f15-2557f19c34f8 wandb_project: s56-4 wandb_run: your_name wandb_runid: e9a3f091-ac21-4461-8f15-2557f19c34f8 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # c172c020-3d0e-4a0c-a72f-0af785cff78b This model is a fine-tuned version of [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6110 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.063 \| 0.0066 \| 200 \| 2.6110 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
BTOREYES/albertreyes	BTOREYES	2025-04-29T00:51:06Z	0	0	null	[ "license:other", "region:us" ]	null	2025-04-29T00:10:34Z	--- 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 ---
Ej9m6yillwiPBWTyMI1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-furry_mimic_mule	Ej9m6yillwiPBWTyMI1	2025-04-29T00:51:02Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am furry mimic mule", "trl", "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	2025-04-22T13:52:28Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-furry_mimic_mule tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am furry mimic mule - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-furry_mimic_mule 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="Ej9m6yillwiPBWTyMI1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-furry_mimic_mule", 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: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
MoyYuan/DeductiveReasoning-forward-explicit	MoyYuan	2025-04-29T00:49:24Z	0	0	null	[ "pytorch", "bert", "en", "dataset:MoyYuan/DeductiveReasoning", "license:mit", "region:us" ]	null	2025-04-29T00:43:28Z	--- license: mit datasets: - MoyYuan/DeductiveReasoning language: - en --- Please refer to https://huggingface.co/datasets/MoyYuan/DeductiveReasoning for README information.
Asif-Sheriff/QAC3	Asif-Sheriff	2025-04-29T00:47:36Z	17	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-bert/bert-large-uncased", "base_model:finetune:google-bert/bert-large-uncased", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-11T13:47:36Z	--- library_name: transformers license: apache-2.0 base_model: bert-large-uncased tags: - generated_from_trainer model-index: - name: QAC3 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. --> # QAC3 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.51.1 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
MikeRoz/allura-org_Gemma-3-Glitter-27B-6.0bpw-h8-exl2	MikeRoz	2025-04-29T00:45:49Z	0	0	exllamav2	[ "exllamav2", "safetensors", "gemma3", "exl2", "base_model:allura-org/Gemma-3-Glitter-27B", "base_model:quantized:allura-org/Gemma-3-Glitter-27B", "6-bit", "region:us" ]	null	2025-04-28T19:14:11Z	--- base_model: allura-org/Gemma-3-Glitter-27B base_model_relation: quantized library_name: exllamav2 tags: - exl2 --- This model was quantized using commit 3a90264 of the dev branch of exllamav2. The 8k context bug looks to be thoroughly squashed as of this commit. To use this model, please either build your own copy of exllamav2 from the dev branch, or wait for the forthcoming v0.2.9 release. The original model can be found [here](https://huggingface.co/allura-org/Gemma-3-Glitter-27B). # ✨G3 Glitter 27B✨ <figure> <img src="https://huggingface.co/ToastyPigeon/Gemma-3-Glitter-27B/resolve/main/ComfyUI_02512_.png" width="600"> </figure> A creative writing model based on Gemma 3 27B. [Columbidae/gemma-3-27b-half](https://huggingface.co/Columbidae/gemma-3-27b-half), a 50/50 merge of 27B IT and 27B PT, was used as the base model. (This was done because of the success of [Starshine](https://huggingface.co/ToastyPigeon/Gemma-3-Starshine-12B), a 50/50 IT and PT merge.) The inclusion of PT model does weaken the instruct, but it also weakens the censorship/hesitancy to participate in certain fictional stories. The prose also becomes more natural with less of the IT model included. This model does better with short and to-the-point prompts. Long, detailed system prompts will often confuse it. (Tested with 1000-2000 token system prompts to lackluster results compared to 100-500 token prompts). ## Instruct Format Uses Gemma2/3 instruct and context. Like Glitter 12b, this works well with `temp = 1, top-nsigma = 1.5`. ``` <start_of_turn>user {User messages; can also put sysprompt here to use the built-in g3 training}<end_of_turn> <start_of_turn>model {model response}<end_of_turn> ```
MoyYuan/DeductiveReasoning-forward	MoyYuan	2025-04-29T00:44:48Z	0	0	null	[ "pytorch", "bert", "en", "dataset:MoyYuan/DeductiveReasoning", "license:mit", "region:us" ]	null	2025-04-29T00:21:06Z	--- license: mit datasets: - MoyYuan/DeductiveReasoning language: - en --- Please refer to https://huggingface.co/datasets/MoyYuan/DeductiveReasoning for README information.
Rkdon11/deberta-v3-large-osint-cybersecurity-ner	Rkdon11	2025-04-29T00:41:09Z	0	0	transformers	[ "transformers", "safetensors", "deberta-v2", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-04-29T00:39:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
cristiandouglas777/Projet2	cristiandouglas777	2025-04-29T00:39:25Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-29T00:39:25Z	--- license: apache-2.0 ---
raraujo/peft-granite-lora-a100	raraujo	2025-04-29T00:34:42Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:ibm-granite/granite-3b-code-instruct-2k", "base_model:adapter:ibm-granite/granite-3b-code-instruct-2k", "license:apache-2.0", "region:us" ]	null	2025-04-28T23:41:55Z	--- library_name: peft license: apache-2.0 base_model: ibm-granite/granite-3b-code-instruct-2k tags: - generated_from_trainer model-index: - name: peft-granite-lora-a100 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. --> # peft-granite-lora-a100 This model is a fine-tuned version of [ibm-granite/granite-3b-code-instruct-2k](https://huggingface.co/ibm-granite/granite-3b-code-instruct-2k) 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: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 30 - training_steps: 1000 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
takedakoji00/Llama-3.1-8B-Instruct-custom-qg-7th_val_val_edit_distance_1000epoch_empty_removed	takedakoji00	2025-04-29T00:28:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T03:34:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mlfoundations-dev/d1_science_long_paragraphs_3k	mlfoundations-dev	2025-04-29T00:27:54Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T19:56:32Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_science_long_paragraphs_3k 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. --> # d1_science_long_paragraphs_3k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_science_long_paragraphs_3k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
KYUNGYONG/EEVE-Korean-Instruct-7B-v2.0-Preview-mlx-4Bit	KYUNGYONG	2025-04-29T00:25:59Z	0	0	mlx	[ "mlx", "safetensors", "qwen2", "generated_from_trainer", "mlx-my-repo", "base_model:yanolja/EEVE-Korean-Instruct-7B-v2.0-Preview", "base_model:quantized:yanolja/EEVE-Korean-Instruct-7B-v2.0-Preview", "license:apache-2.0", "4-bit", "region:us" ]	null	2025-04-29T00:25:41Z	--- license: apache-2.0 tags: - generated_from_trainer - mlx - mlx-my-repo base_model: yanolja/EEVE-Korean-Instruct-7B-v2.0-Preview model-index: - name: yanolja/EEVE-Korean-Instruct-7B-v2.0-Preview results: [] --- # KYUNGYONG/EEVE-Korean-Instruct-7B-v2.0-Preview-mlx-4Bit The Model [KYUNGYONG/EEVE-Korean-Instruct-7B-v2.0-Preview-mlx-4Bit](https://huggingface.co/KYUNGYONG/EEVE-Korean-Instruct-7B-v2.0-Preview-mlx-4Bit) was converted to MLX format from [yanolja/EEVE-Korean-Instruct-7B-v2.0-Preview](https://huggingface.co/yanolja/EEVE-Korean-Instruct-7B-v2.0-Preview) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("KYUNGYONG/EEVE-Korean-Instruct-7B-v2.0-Preview-mlx-4Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
spow12/ChatWaifu_32B_reasoning	spow12	2025-04-29T00:23:05Z	52	2	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "nsfw", "Visual novel", "roleplay", "mergekit", "merge", "conversational", "en", "ja", "dataset:HuggingFaceTB/smoltalk", "dataset:microsoft/orca-agentinstruct-1M-v1", "dataset:Gryphe/Sonnet3.5-SlimOrcaDedupCleaned", "dataset:facebook/natural_reasoning", "dataset:Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted", "dataset:Aratako/Synthetic-JP-EN-Coding-Dataset-801k", "dataset:Aratako/Magpie-Tanuki-8B-97k", "dataset:SkunkworksAI/reasoning-0.01", "dataset:anthracite-org/stheno-filtered-v1.1", "dataset:Aratako/Synthetic-JP-EN-Translation-Dataset-Magpie-Nemotron-4-20k", "dataset:open-r1/OpenR1-Math-220k", "dataset:Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted", "dataset:Nopm/Opus_WritingStruct", "dataset:gretelai/synthetic_text_to_sql", "dataset:kalomaze/Opus_Instruct_3k", "dataset:PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT", "dataset:SicariusSicariiStuff/Bluemoon_Top50MB_Sorted_Fixed", "dataset:roleplay4fun/aesir-v1.1", "dataset:Aratako/Rosebleu-1on1-Dialogues-RP_v2", "base_model:Qwen/QwQ-32B", "base_model:merge:Qwen/QwQ-32B", "base_model:rinna/qwq-bakeneko-32b", "base_model:merge:rinna/qwq-bakeneko-32b", "base_model:trashpanda-org/QwQ-32B-Snowdrop-v0", "base_model:merge:trashpanda-org/QwQ-32B-Snowdrop-v0", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-04T04:48:17Z	--- language: - en - ja license: cc-by-nc-4.0 library_name: transformers tags: - nsfw - Visual novel - roleplay - mergekit - merge base_model: - trashpanda-org/QwQ-32B-Snowdrop-v0 - rinna/qwq-bakeneko-32b - Qwen/QwQ-32B datasets: - HuggingFaceTB/smoltalk - microsoft/orca-agentinstruct-1M-v1 - Gryphe/Sonnet3.5-SlimOrcaDedupCleaned - facebook/natural_reasoning - Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted - Aratako/Synthetic-JP-EN-Coding-Dataset-801k - Aratako/Magpie-Tanuki-8B-97k - SkunkworksAI/reasoning-0.01 - anthracite-org/stheno-filtered-v1.1 - Aratako/Synthetic-JP-EN-Translation-Dataset-Magpie-Nemotron-4-20k - open-r1/OpenR1-Math-220k - Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted - Nopm/Opus_WritingStruct - gretelai/synthetic_text_to_sql - kalomaze/Opus_Instruct_3k - PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT - SicariusSicariiStuff/Bluemoon_Top50MB_Sorted_Fixed - roleplay4fun/aesir-v1.1 - Aratako/Rosebleu-1on1-Dialogues-RP_v2 pipeline_tag: text-generation --- # Model Card for Model ID ![image](https://huggingface.co/spow12/ChatWaifu_22B_v2.0_preview/resolve/main/cover_2.png) Merged model using [mergekit](https://github.com/arcee-ai/mergekit/tree/main/mergekit) This model aim to make a agent system with keeping given our waifu persona. ## Merge Format ```yaml models: - model: trashpanda-org/QwQ-32B-Snowdrop-v0 - model: Qwen/QwQ-32B_sft(private) merge_method: model_stock base_model: Qwen/QwQ-32B dtype: bfloat16 tokenizer_source: base ``` ## Model Details ### Model Description - Developed by: spow12(yw_nam) - Shared by : spow12(yw_nam) - Model type: CausalLM - Language(s) (NLP): japanese, english - Finetuned from model : [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) ### Chat Format ``` <\|im_start\|>system This is the system prompt.<\|im_end\|> <\|im_start\|>user Instructions placed here.<\|im_end\|> <\|im_start\|>assistant The model's response will be here.<\|im_end\|> ``` ## Reasoning mode If you want to turn on the reasoning mode, incorporate below sentence in system message or instruction. ``` Before answer, organize thoughts your thought inside <think> and </think> tags after that, answer in a concise manner. ``` ## Dataset SFT (585K) - Riddle Joker(Prviate) - Café Stella and the Reaper's Butterflies(Private) - Senren＊Banka(Private) - HuggingFaceTB/smoltalk - microsoft/orca-agentinstruct-1M-v1 - Gryphe/Sonnet3.5-SlimOrcaDedupCleaned - facebook/natural_reasoning - Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted - Aratako/Synthetic-JP-EN-Coding-Dataset-801k - Aratako/Magpie-Tanuki-8B-97k - SkunkworksAI/reasoning-0.01 - anthracite-org/stheno-filtered-v1.1 - Aratako/Synthetic-JP-EN-Translation-Dataset-Magpie-Nemotron-4-20k - open-r1/OpenR1-Math-220k - Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted - Nopm/Opus_WritingStruct - gretelai/synthetic_text_to_sql - kalomaze/Opus_Instruct_3k - PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT - SicariusSicariiStuff/Bluemoon_Top50MB_Sorted_Fixed - roleplay4fun/aesir-v1.1 - Aratako/Rosebleu-1on1-Dialogues-RP_v2 ## Use & Credit This model is currently available for non-commercial & Research purpose only. Also, since I'm not detailed in licensing, I hope you use it responsibly. By sharing this model, I hope to contribute to the research efforts of our community (the open-source community and Waifu Lovers). ## Citation ```bibtex @misc {ChatWaifu_32B_reasoning, author = { YoungWoo Nam }, title = { spow12/ChatWaifu_32B_reasoning }, year = 2025, url = { https://huggingface.co/spow12/ChatWaifu_32B_reasoning }, publisher = { Hugging Face } } ```
kostiantynk-outlook/dbc44e50-3e7f-48ec-80aa-b7f594546b67	kostiantynk-outlook	2025-04-29T00:22:40Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:oopsung/llama2-7b-n-ox-test-v1", "base_model:adapter:oopsung/llama2-7b-n-ox-test-v1", "region:us" ]	null	2025-04-29T00:22:06Z	--- library_name: peft tags: - generated_from_trainer base_model: oopsung/llama2-7b-n-ox-test-v1 model-index: - name: kostiantynk-outlook/dbc44e50-3e7f-48ec-80aa-b7f594546b67 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. --> # kostiantynk-outlook/dbc44e50-3e7f-48ec-80aa-b7f594546b67 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5471 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
redlessone/PanDerm	redlessone	2025-04-29T00:19:36Z	0	0	null	[ "medical", "medical AI", "SSL", "foundation_model", "multimodal", "skin_cancer", "license:cc-by-nc-nd-4.0", "region:us" ]	null	2025-04-29T00:18:06Z	--- license: cc-by-nc-nd-4.0 tags: - medical - medical AI - SSL - foundation_model - multimodal - skin_cancer ---
theaivaultqueen/charlaeexum	theaivaultqueen	2025-04-29T00:15:37Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-28T23:38:41Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Charlae --- # Charlaeexum <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Charlae` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Charlae", "lora_weights": "https://huggingface.co/theaivaultqueen/charlaeexum/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('theaivaultqueen/charlaeexum', weight_name='lora.safetensors') image = pipeline('Charlae').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/theaivaultqueen/charlaeexum/discussions) to add images that show off what you’ve made with this LoRA.
mradermacher/MiniusLight-24B-v2.1-i1-GGUF	mradermacher	2025-04-29T00:13:54Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:DoppelReflEx/MiniusLight-24B-v2.1", "base_model:quantized:DoppelReflEx/MiniusLight-24B-v2.1", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-28T20:56:12Z	--- base_model: DoppelReflEx/MiniusLight-24B-v2.1 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/DoppelReflEx/MiniusLight-24B-v2.1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 5.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 5.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 7.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 7.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 8.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-Q2_K.gguf) \| i1-Q2_K \| 9.0 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 9.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 10.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 10.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 10.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 10.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 11.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 12.5 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 12.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-Q4_0.gguf) \| i1-Q4_0 \| 13.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 13.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-Q4_1.gguf) \| i1-Q4_1 \| 15.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 16.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MiniusLight-24B-v2.1-i1-GGUF/resolve/main/MiniusLight-24B-v2.1.i1-Q6_K.gguf) \| i1-Q6_K \| 19.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
King-Cane/Dans-PersonalityEngine-V1.2.0-24b-Q4_K_M-GGUF	King-Cane	2025-04-29T00:13:45Z	0	0	transformers	[ "transformers", "gguf", "general-purpose", "roleplay", "storywriting", "chemistry", "biology", "code", "climate", "axolotl", "text-generation-inference", "finetune", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:PocketDoc/Dans-MemoryCore-CoreCurriculum-Small", "dataset:AquaV/US-Army-Survival-Sharegpt", "dataset:AquaV/Multi-Environment-Operations-Sharegpt", "dataset:AquaV/Resistance-Sharegpt", "dataset:AquaV/Interrogation-Sharegpt", "dataset:AquaV/Chemical-Biological-Safety-Applications-Sharegpt", "dataset:AquaV/Energetic-Materials-Sharegpt", "dataset:PocketDoc/Dans-Mathmaxx", "dataset:PocketDoc/Dans-Mathmaxx-Numina-CoT", "dataset:PJMixers/Math-Multiturn-1K-ShareGPT", "dataset:PocketDoc/Dans-Benchmaxx-COT", "dataset:PocketDoc/Dans-Codemaxx-LeetCode", "dataset:PocketDoc/Dans-Codemaxx-CodeFeedback-Conversations", "dataset:PocketDoc/Dans-Codemaxx-CodeFeedback-SingleTurn", "dataset:PocketDoc/Dans-Codemaxx-Bigcode-SelfInstruct", "dataset:PocketDoc/Dans-Taskmaxx", "dataset:PocketDoc/Dans-Taskmaxx-DataPrepper", "dataset:PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked", "dataset:PocketDoc/Dans-Taskmaxx-TableGPT", "dataset:PocketDoc/Dans-Taskmaxx-SciRIFF", "dataset:PocketDoc/Dans-Taskmaxx-Edit", "dataset:PocketDoc/Dans-Toolmaxx-Agent", "dataset:PocketDoc/Dans-Toolmaxx-ShellCommands", "dataset:PocketDoc/Dans-Toolmaxx-Functions-Toolbench", "dataset:PocketDoc/Dans-Toolmaxx-Functions-ToolACE", "dataset:PocketDoc/Dans-ASCIIMaxx-Wordart", "dataset:PocketDoc/Dans-Prosemaxx-Gutenberg", "dataset:PocketDoc/Dans-Prosemaxx-Cowriter-3-XL", "dataset:PocketDoc/Dans-Prosemaxx-Adventure", "dataset:PocketDoc/Dans-Failuremaxx-Adventure-3", "dataset:PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2", "dataset:PocketDoc/Dans-Prosemaxx-InstructWriter-Continue-2", "dataset:PocketDoc/Dans-Assistantmaxx-Sharegpt", "dataset:PocketDoc/Dans-Assistantmaxx-OpenAssistant2", "dataset:PocketDoc/Dans-Assistantmaxx-Opus-Merge", "dataset:PocketDoc/Dans-Assistantmaxx-sonnetorca-subset", "dataset:PocketDoc/Dans-Assistantmaxx-sonnetorca-subset-2", "dataset:PocketDoc/Dans-Assistantmaxx-NoRobots", "dataset:PocketDoc/Dans-Assistantmaxx-Synthia", "dataset:PocketDoc/Dans-Assistantmaxx-ASL", "dataset:PocketDoc/Dans-Assistantmaxx-PersonaLLM-Opus", "dataset:PocketDoc/Dans-Assistantmaxx-UnnaturalInstructions-GPT4", "dataset:PocketDoc/Dans-Assistantmaxx-LongAlign", "dataset:PocketDoc/Dans-Assistantmaxx-EvolKit", "dataset:PocketDoc/Dans-Assistantmaxx-Camel-GPT4", "dataset:PocketDoc/Dans-Assistantmaxx-OpenLeecher-Instruct", "dataset:PocketDoc/Dans-Assistantmaxx-Tulu3-IF", "dataset:PocketDoc/Dans-Systemmaxx", "dataset:PocketDoc/Dans-Logicmaxx-Skunkworks", "dataset:PocketDoc/Dans-Logicmaxx-FI-VeriMed", "dataset:PocketDoc/Dans-Logicmaxx-SAT-AP", "dataset:PocketDoc/Dans-Logicmaxx-Magpie-Ultra", "dataset:PJMixers/grimulkan_theory-of-mind-ShareGPT", "dataset:PJMixers/grimulkan_physical-reasoning-ShareGPT", "dataset:PocketDoc/Dans-Personamaxx", "dataset:PocketDoc/Dans-Personamaxx-Rainy", "dataset:PocketDoc/Dans-Personamaxx-C1", "dataset:PocketDoc/Dans-Personamaxx-VN", "base_model:PocketDoc/Dans-PersonalityEngine-V1.2.0-24b", "base_model:quantized:PocketDoc/Dans-PersonalityEngine-V1.2.0-24b", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T00:12:32Z	--- base_model: PocketDoc/Dans-PersonalityEngine-V1.2.0-24b datasets: - PocketDoc/Dans-MemoryCore-CoreCurriculum-Small - AquaV/US-Army-Survival-Sharegpt - AquaV/Multi-Environment-Operations-Sharegpt - AquaV/Resistance-Sharegpt - AquaV/Interrogation-Sharegpt - AquaV/Chemical-Biological-Safety-Applications-Sharegpt - AquaV/Energetic-Materials-Sharegpt - PocketDoc/Dans-Mathmaxx - PocketDoc/Dans-Mathmaxx-Numina-CoT - PJMixers/Math-Multiturn-1K-ShareGPT - PocketDoc/Dans-Benchmaxx-COT - PocketDoc/Dans-Codemaxx-LeetCode - PocketDoc/Dans-Codemaxx-CodeFeedback-Conversations - PocketDoc/Dans-Codemaxx-CodeFeedback-SingleTurn - PocketDoc/Dans-Codemaxx-Bigcode-SelfInstruct - PocketDoc/Dans-Taskmaxx - PocketDoc/Dans-Taskmaxx-DataPrepper - PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked - PocketDoc/Dans-Taskmaxx-TableGPT - PocketDoc/Dans-Taskmaxx-SciRIFF - PocketDoc/Dans-Taskmaxx-Edit - PocketDoc/Dans-Toolmaxx-Agent - PocketDoc/Dans-Toolmaxx-ShellCommands - PocketDoc/Dans-Toolmaxx-Functions-Toolbench - PocketDoc/Dans-Toolmaxx-Functions-ToolACE - PocketDoc/Dans-ASCIIMaxx-Wordart - PocketDoc/Dans-Prosemaxx-Gutenberg - PocketDoc/Dans-Prosemaxx-Cowriter-3-XL - PocketDoc/Dans-Prosemaxx-Adventure - PocketDoc/Dans-Failuremaxx-Adventure-3 - PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2 - PocketDoc/Dans-Prosemaxx-InstructWriter-Continue-2 - PocketDoc/Dans-Assistantmaxx-Sharegpt - PocketDoc/Dans-Assistantmaxx-OpenAssistant2 - PocketDoc/Dans-Assistantmaxx-Opus-Merge - PocketDoc/Dans-Assistantmaxx-sonnetorca-subset - PocketDoc/Dans-Assistantmaxx-sonnetorca-subset-2 - PocketDoc/Dans-Assistantmaxx-NoRobots - PocketDoc/Dans-Assistantmaxx-Synthia - PocketDoc/Dans-Assistantmaxx-ASL - PocketDoc/Dans-Assistantmaxx-PersonaLLM-Opus - PocketDoc/Dans-Assistantmaxx-UnnaturalInstructions-GPT4 - PocketDoc/Dans-Assistantmaxx-LongAlign - PocketDoc/Dans-Assistantmaxx-EvolKit - PocketDoc/Dans-Assistantmaxx-Camel-GPT4 - PocketDoc/Dans-Assistantmaxx-OpenLeecher-Instruct - PocketDoc/Dans-Assistantmaxx-Tulu3-IF - PocketDoc/Dans-Systemmaxx - PocketDoc/Dans-Logicmaxx-Skunkworks - PocketDoc/Dans-Logicmaxx-FI-VeriMed - PocketDoc/Dans-Logicmaxx-SAT-AP - PocketDoc/Dans-Logicmaxx-Magpie-Ultra - PJMixers/grimulkan_theory-of-mind-ShareGPT - PJMixers/grimulkan_physical-reasoning-ShareGPT - PocketDoc/Dans-Personamaxx - PocketDoc/Dans-Personamaxx-Rainy - PocketDoc/Dans-Personamaxx-C1 - PocketDoc/Dans-Personamaxx-VN language: - en library_name: transformers license: apache-2.0 pipeline_tag: text-generation tags: - general-purpose - roleplay - storywriting - chemistry - biology - code - climate - axolotl - text-generation-inference - finetune - llama-cpp - gguf-my-repo thumbnail: https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.2.0-24b/resolve/main/resources/pe24.png --- # King-Cane/Dans-PersonalityEngine-V1.2.0-24b-Q4_K_M-GGUF This model was converted to GGUF format from [`PocketDoc/Dans-PersonalityEngine-V1.2.0-24b`](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.2.0-24b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.2.0-24b) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo King-Cane/Dans-PersonalityEngine-V1.2.0-24b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.2.0-24b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo King-Cane/Dans-PersonalityEngine-V1.2.0-24b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.2.0-24b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo King-Cane/Dans-PersonalityEngine-V1.2.0-24b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.2.0-24b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo King-Cane/Dans-PersonalityEngine-V1.2.0-24b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.2.0-24b-q4_k_m.gguf -c 2048 ```
Amjad00/crossfitgym1	Amjad00	2025-04-29T00:09:48Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-29T00:09:03Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: crossfitgym 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 --- # crossfitgym1 A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `crossfitgym` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
fedovtt/be0b69cb-89a4-4aad-ae49-1404ffd97d83	fedovtt	2025-04-29T00:08:43Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:DeepMount00/Llama-3-8b-Ita", "base_model:adapter:DeepMount00/Llama-3-8b-Ita", "license:llama3", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T23:40:15Z	--- library_name: peft license: llama3 base_model: DeepMount00/Llama-3-8b-Ita tags: - axolotl - generated_from_trainer model-index: - name: be0b69cb-89a4-4aad-ae49-1404ffd97d83 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: DeepMount00/Llama-3-8b-Ita bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 79318d698494eac0_train_data.json ds_type: json format: custom path: /workspace/input_data/79318d698494eac0_train_data.json type: field_instruction: prompt field_output: gold_standard_solution format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: fedovtt/be0b69cb-89a4-4aad-ae49-1404ffd97d83 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/79318d698494eac0_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: <\|eot_id\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 1ec4609f-0146-420b-96e9-6b8f3cb30115 wandb_project: s56-1 wandb_run: your_name wandb_runid: 1ec4609f-0146-420b-96e9-6b8f3cb30115 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # be0b69cb-89a4-4aad-ae49-1404ffd97d83 This model is a fine-tuned version of [DeepMount00/Llama-3-8b-Ita](https://huggingface.co/DeepMount00/Llama-3-8b-Ita) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4307 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.2252 \| 0.0284 \| 200 \| 2.4307 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
vmpsergio/75a78abd-cd41-49dd-9dcf-db98952288b4	vmpsergio	2025-04-29T00:08:30Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:DeepMount00/Llama-3-8b-Ita", "base_model:adapter:DeepMount00/Llama-3-8b-Ita", "license:llama3", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T23:39:59Z	--- library_name: peft license: llama3 base_model: DeepMount00/Llama-3-8b-Ita tags: - axolotl - generated_from_trainer model-index: - name: 75a78abd-cd41-49dd-9dcf-db98952288b4 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: DeepMount00/Llama-3-8b-Ita bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 79318d698494eac0_train_data.json ds_type: json format: custom path: /workspace/input_data/79318d698494eac0_train_data.json type: field_instruction: prompt field_output: gold_standard_solution format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vmpsergio/75a78abd-cd41-49dd-9dcf-db98952288b4 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/79318d698494eac0_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: <\|eot_id\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 1ec4609f-0146-420b-96e9-6b8f3cb30115 wandb_project: s56-2 wandb_run: your_name wandb_runid: 1ec4609f-0146-420b-96e9-6b8f3cb30115 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 75a78abd-cd41-49dd-9dcf-db98952288b4 This model is a fine-tuned version of [DeepMount00/Llama-3-8b-Ita](https://huggingface.co/DeepMount00/Llama-3-8b-Ita) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4291 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.2237 \| 0.0284 \| 200 \| 2.4291 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
iboero16/SAFE-SFT-EXAMPLE	iboero16	2025-04-29T00:07:18Z	0	0	null	[ "llama", "license:apache-2.0", "region:us" ]	null	2025-04-29T00:05:11Z	--- license: apache-2.0 ---
alezz12/FineTune-CodeLLaMA-Debugger	alezz12	2025-04-29T00:00:18Z	0	0	null	[ "region:us" ]	null	2025-04-28T23:58:45Z	# FineTune-CodeLLaMA-Debugger Fine-tuning Code LLaMA to create a context-aware Python code generation and debugging assistant. ## Project Overview This project aims to fine-tune a large language model (LLM) — specifically Code LLaMA — to perform two tasks: - Code Generation Mode: Generate correct Python code from natural language problem descriptions. - Debugging Mode: Take buggy Python code, identify the errors, fix them, and explain the fix in simple words. ## Key Features - Smart Python code writing from prompts (LeetCode-style problems). - Intelligent bug detection and auto-repair. - Clear explanations for every fix — educational for learners. - Simple Command-Line Interface (CLI) to interact with the model. ## Project Structure <pre> data/ # Datasets: coding problems, buggy codes scripts/ # Fine-tuning, evaluation, utilities models/ # Trained models and checkpoints notebooks/ # Experiment notebooks results/ # Evaluation results and reports </pre>
infogeo/1cd51e9e-83d4-47ff-8526-8e66ffd89c2f	infogeo	2025-04-28T23:59:54Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-1.7B-Instruct", "base_model:adapter:unsloth/SmolLM-1.7B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T23:55:32Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-1.7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 1cd51e9e-83d4-47ff-8526-8e66ffd89c2f 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/SmolLM-1.7B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 09440e5d84ab787c_train_data.json ds_type: json format: custom path: /workspace/input_data/09440e5d84ab787c_train_data.json type: field_input: user_prompt field_instruction: system_prompt field_output: prompt format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: infogeo/1cd51e9e-83d4-47ff-8526-8e66ffd89c2f hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/09440e5d84ab787c_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 0a019fdb-0b45-4625-bb8c-9db767620d26 wandb_project: s56-28 wandb_run: your_name wandb_runid: 0a019fdb-0b45-4625-bb8c-9db767620d26 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 1cd51e9e-83d4-47ff-8526-8e66ffd89c2f This model is a fine-tuned version of [unsloth/SmolLM-1.7B-Instruct](https://huggingface.co/unsloth/SmolLM-1.7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2423 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.2296 \| 0.0071 \| 150 \| 0.2423 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
harshbajpai/NYC_Yellow_Taxi_Fare_Prediction	harshbajpai	2025-04-28T23:57:48Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T23:41:25Z	--- license: apache-2.0 ---
dtocre/llama-3.1-8b-Instruct-bnb-4bit-CGR-def2	dtocre	2025-04-28T23:57:21Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-06T10:15:27Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: dtocre - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
onnx-community/Qwen3-1.7B-ONNX	onnx-community	2025-04-28T23:53:30Z	0	0	transformers.js	[ "transformers.js", "onnx", "qwen3", "text-generation", "conversational", "base_model:Qwen/Qwen3-1.7B", "base_model:quantized:Qwen/Qwen3-1.7B", "region:us" ]	text-generation	2025-04-28T23:47:52Z	--- library_name: transformers.js base_model: Qwen/Qwen3-1.7B --- https://huggingface.co/Qwen/Qwen3-1.7B with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
Flo0620/Qwen2_5_7B_r64_a64_d0_2_lr2e-4_const	Flo0620	2025-04-28T23:52:30Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-04-28T19:04:59Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: transformers model_name: Qwen2_5_7B_r64_a64_d0_2_lr2e-4_const tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2_5_7B_r64_a64_d0_2_lr2e-4_const This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-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="Flo0620/Qwen2_5_7B_r64_a64_d0_2_lr2e-4_const", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.0 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
joboffer/1f191fd8-0a34-412a-8b78-cdd72c05e5c9	joboffer	2025-04-28T23:51:59Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:DeepMount00/Llama-3-8b-Ita", "base_model:adapter:DeepMount00/Llama-3-8b-Ita", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T23:42:00Z	--- library_name: peft license: llama3 base_model: DeepMount00/Llama-3-8b-Ita tags: - axolotl - generated_from_trainer model-index: - name: 1f191fd8-0a34-412a-8b78-cdd72c05e5c9 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: DeepMount00/Llama-3-8b-Ita bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 79318d698494eac0_train_data.json ds_type: json format: custom path: /workspace/input_data/79318d698494eac0_train_data.json type: field_instruction: prompt field_output: gold_standard_solution format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: joboffer/1f191fd8-0a34-412a-8b78-cdd72c05e5c9 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/79318d698494eac0_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: <\|eot_id\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 1ec4609f-0146-420b-96e9-6b8f3cb30115 wandb_project: s56-33 wandb_run: your_name wandb_runid: 1ec4609f-0146-420b-96e9-6b8f3cb30115 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 1f191fd8-0a34-412a-8b78-cdd72c05e5c9 This model is a fine-tuned version of [DeepMount00/Llama-3-8b-Ita](https://huggingface.co/DeepMount00/Llama-3-8b-Ita) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4485 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.1834 \| 0.0284 \| 200 \| 2.4485 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF	mradermacher	2025-04-28T23:49:27Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B", "base_model:quantized:zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B", "license:llama3", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-28T18:42:18Z	--- base_model: zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B language: - en library_name: transformers license: llama3 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 15.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 19.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 21.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 22.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 24.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 24.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-Q2_K.gguf) \| i1-Q2_K \| 26.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 27.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 31.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 34.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 37.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 38.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-Q4_0.gguf) \| i1-Q4_0 \| 40.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 40.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-Q4_1.gguf) \| i1-Q4_1 \| 44.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF/resolve/main/L3.3-GeneticLemonade-Unleashed-v2.1-70B.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 58.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF	mradermacher	2025-04-28T23:49:26Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "base_model:arcee-samsung/Sky-T1-32B-Flash-Spectrum", "base_model:quantized:arcee-samsung/Sky-T1-32B-Flash-Spectrum", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-28T20:39:29Z	--- base_model: arcee-samsung/Sky-T1-32B-Flash-Spectrum language: - en library_name: transformers model_name: outputs/simpo-skyT1-out quantized_by: mradermacher tags: - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/arcee-samsung/Sky-T1-32B-Flash-Spectrum <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 11.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-Q2_K.gguf) \| i1-Q2_K \| 12.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-IQ3_M.gguf) \| i1-IQ3_M \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-Q4_1.gguf) \| i1-Q4_1 \| 20.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Sky-T1-32B-Flash-Spectrum-i1-GGUF/resolve/main/Sky-T1-32B-Flash-Spectrum.i1-Q6_K.gguf) \| i1-Q6_K \| 27.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/EEVE-Korean-Instruct-7B-v2.0-Preview-GGUF	mradermacher	2025-04-28T23:49:26Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "base_model:yanolja/EEVE-Korean-Instruct-7B-v2.0-Preview", "base_model:quantized:yanolja/EEVE-Korean-Instruct-7B-v2.0-Preview", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T23:14:22Z	--- base_model: yanolja/EEVE-Korean-Instruct-7B-v2.0-Preview language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/yanolja/EEVE-Korean-Instruct-7B-v2.0-Preview <!-- provided-files --> 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/EEVE-Korean-Instruct-7B-v2.0-Preview-GGUF/resolve/main/EEVE-Korean-Instruct-7B-v2.0-Preview.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-Instruct-7B-v2.0-Preview-GGUF/resolve/main/EEVE-Korean-Instruct-7B-v2.0-Preview.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-Instruct-7B-v2.0-Preview-GGUF/resolve/main/EEVE-Korean-Instruct-7B-v2.0-Preview.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-Instruct-7B-v2.0-Preview-GGUF/resolve/main/EEVE-Korean-Instruct-7B-v2.0-Preview.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-Instruct-7B-v2.0-Preview-GGUF/resolve/main/EEVE-Korean-Instruct-7B-v2.0-Preview.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-Instruct-7B-v2.0-Preview-GGUF/resolve/main/EEVE-Korean-Instruct-7B-v2.0-Preview.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-Instruct-7B-v2.0-Preview-GGUF/resolve/main/EEVE-Korean-Instruct-7B-v2.0-Preview.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-Instruct-7B-v2.0-Preview-GGUF/resolve/main/EEVE-Korean-Instruct-7B-v2.0-Preview.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-Instruct-7B-v2.0-Preview-GGUF/resolve/main/EEVE-Korean-Instruct-7B-v2.0-Preview.Q5_K_M.gguf) \| Q5_K_M \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-Instruct-7B-v2.0-Preview-GGUF/resolve/main/EEVE-Korean-Instruct-7B-v2.0-Preview.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-Instruct-7B-v2.0-Preview-GGUF/resolve/main/EEVE-Korean-Instruct-7B-v2.0-Preview.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/EEVE-Korean-Instruct-7B-v2.0-Preview-GGUF/resolve/main/EEVE-Korean-Instruct-7B-v2.0-Preview.f16.gguf) \| f16 \| 15.4 \| 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 -->
robiulawaldev/bdf17a23-657b-4cb7-9143-6b416d5342d1	robiulawaldev	2025-04-28T23:46:37Z	0	0	transformers	[ "transformers", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2025-04-28T23:45:52Z	--- library_name: transformers model_name: robiulawaldev/bdf17a23-657b-4cb7-9143-6b416d5342d1 tags: - generated_from_trainer licence: license --- # Model Card for robiulawaldev/bdf17a23-657b-4cb7-9143-6b416d5342d1 This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ### Framework versions - TRL: 0.12.0 - Transformers: 4.46.3 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-4.5bpw-hb6-exl2	zerofata	2025-04-28T23:39:21Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "base_model:zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B", "base_model:quantized:zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2025-04-28T06:16:05Z	--- base_model: - zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B library_name: transformers license: llama3 --- <!DOCTYPE html> <style> /* Base styling for cyberpunk theme / body {font-family: sans-serif; background-color: #080c14; color: #e1e9f0; line-height: 1.6; margin: 0; padding: 0;} / Remove flicker keyframes / / Remove Basic Background animation test / / Animation classes / / Remove flicker-text rules / / New static style for LEMONADE / .lemonade-text { color: #33ff99; position: relative; / Keep relative positioning / z-index: 2; margin-left: 0.2em; text-shadow: 0 0 10px #33ff99; / Add static glow / } / Section styling / .section-container {background-color: rgba(8, 12, 20, 0.7); margin-bottom: 30px; position: relative; overflow: hidden; border-bottom: 1px solid #33ff99;} .section-header {display: flex; align-items: center; background-color: rgba(0, 195, 255, 0.1); padding: 10px 20px;} .section-indicator {width: 8px; height: 20px; background-color: #33ff99; margin-right: 15px;} .section-title {font-family: 'Orbitron', sans-serif; color: #e1e9f0; font-size: 1.3rem; margin: 0; letter-spacing: 2px; text-transform: uppercase; font-weight: 500;} .section-content {padding: 20px; font-family: sans-serif; color: #e1e9f0; line-height: 1.6;} / Title styling / .title-container { background-color: #080c14; position: relative; overflow: hidden; margin-bottom: 40px; border-left: 3px solid #33ff99; } / Remove basic background animation test rule / .title-wrapper { position: relative; z-index: 2; padding: 25px 20px 30px 30px; font-family: 'Orbitron', sans-serif; } .title-main { color: #e1e9f0; font-size: 2.5rem; / Reduced font size / font-weight: 700; margin: 0; letter-spacing: 2px; display: inline-block; position: relative; text-transform: uppercase; } .title-prefix { position: relative; z-index: 2; } .title-subtitle { padding-left: 15px; margin-top: 5px; margin-left: 5px; } .subtitle-text { color: #00c3ff; font-size: 1.2rem; / Reduced font size / font-family: 'Orbitron', sans-serif; font-weight: 300; letter-spacing: 3px; text-transform: uppercase; display: inline-block; } .glitchy-overlay { position: absolute; top: 0; left: 0; width: 100%; height: 100%; background-image: repeating-linear-gradient(0deg, rgba(0,0,0,0) 0, rgba(0,0,0,0.1) 1px, rgba(0,0,0,0) 2px); z-index: 1; } / Data box styling / .data-box {background-color: rgba(0, 0, 0, 0.2); padding: 15px; border-left: 2px solid #33ff99; margin-bottom: 20px;} .data-row {display: flex; margin-bottom: 8px;} .data-arrow {color: #33ff99; width: 20px; display: inline-block;} .data-label {color: #00c3ff; width: 80px; display: inline-block;} / Subheading styling / .subheading {color: #00c3ff; font-size: 1.1rem; margin-top: 20px; margin-bottom: 15px; font-weight: 400; border-bottom: 1px dashed rgba(0, 195, 255, 0.3); display: inline-block; text-transform: uppercase; letter-spacing: 1px; font-family: 'Orbitron', sans-serif;} / Links / a {color: #00c3ff; text-decoration: none;} a:hover {text-decoration: underline;} / Container / .container {max-width: 1200px; margin: 0 auto; padding: 40px 20px;} / Cyberpunk grid background */ .cyber-grid-bg {position: fixed; top: 0; left: 0; right: 0; bottom: 0; background-color: #05071b; background-image: linear-gradient(rgba(0, 194, 255, 0.03) 1px, transparent 1px), linear-gradient(90deg, rgba(0, 194, 255, 0.03) 1px, transparent 1px); background-size: 20px 20px; z-index: -1;} </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>GENETIC LEMONADE UNLEASHED v2.1</title> <link href="https://fonts.googleapis.com/css2?family=Orbitron:wght@400;500;600;700&family=JetBrains+Mono:wght@100;300;400;700&display=swap" rel="stylesheet"> </head> <body> <div class="cyber-grid-bg"></div> <div class="container"> <div class="title-container"> <!-- Glitchy overlay --> <div class="glitchy-overlay"></div> <!-- Main title --> <div class="title-wrapper"> <h1 class="title-main"> <span class="title-prefix">GENETIC</span> <span class="lemonade-text">LEMONADE</span> <!-- Static text with glow --> </h1> <div class="title-subtitle"> <span class="subtitle-text">UNLEASHED v2.1</span> </div> </div> </div> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65b19c6c638328850e12d38c/0GTX4-erpPflLOkfH5sU5.png) <div class="section-container"> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">01 // OVERVIEW</h2> </div> <div class="section-content"> <p>An experimental release.</p> <p><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-70B">zerofata/GeneticLemonade-Unleashed</a> qlora trained on a test dataset. Performance is improved from the original in my testing, but there are possibly (likely?) areas where the model will underperform which I am looking for feedback on.</p> <p>This is a creative model intended to excel at character driven RP / ERP. It has not been tested or trained on adventure stories or any large amounts of creative writing.</p> </div> </div> <div class="section-container"> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">02 // SILLYTAVERN SETTINGS</h2> </div> <div class="section-content"> <p>Play with these, they are not the 'best' settings just a stable baseline.</p> <h3 class="subheading">Recommended Samplers</h3> <div class="data-box"> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">Temp:</span> <span>0.9 - 1.0</span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">MinP:</span> <span>0.03 - 0.04</span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">Dry:</span> <span>0.8, 1.75, 4</span> </div> </div> <h3 class="subheading">Instruct</h3> <div class="data-box"> <p style="margin: 0;">Llama-3-Instruct-Names but you will need to uncheck "System same as user".</p> </div> </div> </div> <div class="section-container"> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">03 // QUANTIZATIONS</h2> </div> <div class="section-content"> <div style="margin-bottom: 20px;"> <h3 class="subheading">GGUF</h3> <div style="margin-left: 20px;"> <span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF">iMatrix (mradermacher)</a><br> </div> </div> <div> <h3 class="subheading">EXL2</h3> <div style="margin-left: 20px;"> <span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-4bpw-hb6-exl2">4bpw</a><br> <span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-4.5bpw-hb6-exl2">4.5bpw</a><br> <span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-6bpw-hb8-exl2">6bpw</a> </div> </div> </div> </div> <div class="section-container"> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">04 // DATASET</h2> </div> <div class="section-content"> <p>Model was trained on a tiny synthetic dataset of 640k tokens, approximately 190 conversations. Data was generated by script and then manually reviewed / edited.</p> <p>The dataset is approximately 60% SFW and 40% NSFW. 90% multi turn RP conversations, 5% creative writing and 5% miscellaneous.</p> <p>It is an experiment to see how models perform when provided with small amounts of high quality synthetic data, as opposed to human data.</p> </div> </div> </div> </body> </html>
zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-6bpw-hb8-exl2	zerofata	2025-04-28T23:38:59Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "base_model:zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B", "base_model:quantized:zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2025-04-28T22:49:27Z	--- base_model: - zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B library_name: transformers license: llama3 --- <!DOCTYPE html> <style> /* Base styling for cyberpunk theme / body {font-family: sans-serif; background-color: #080c14; color: #e1e9f0; line-height: 1.6; margin: 0; padding: 0;} / Remove flicker keyframes / / Remove Basic Background animation test / / Animation classes / / Remove flicker-text rules / / New static style for LEMONADE / .lemonade-text { color: #33ff99; position: relative; / Keep relative positioning / z-index: 2; margin-left: 0.2em; text-shadow: 0 0 10px #33ff99; / Add static glow / } / Section styling / .section-container {background-color: rgba(8, 12, 20, 0.7); margin-bottom: 30px; position: relative; overflow: hidden; border-bottom: 1px solid #33ff99;} .section-header {display: flex; align-items: center; background-color: rgba(0, 195, 255, 0.1); padding: 10px 20px;} .section-indicator {width: 8px; height: 20px; background-color: #33ff99; margin-right: 15px;} .section-title {font-family: 'Orbitron', sans-serif; color: #e1e9f0; font-size: 1.3rem; margin: 0; letter-spacing: 2px; text-transform: uppercase; font-weight: 500;} .section-content {padding: 20px; font-family: sans-serif; color: #e1e9f0; line-height: 1.6;} / Title styling / .title-container { background-color: #080c14; position: relative; overflow: hidden; margin-bottom: 40px; border-left: 3px solid #33ff99; } / Remove basic background animation test rule / .title-wrapper { position: relative; z-index: 2; padding: 25px 20px 30px 30px; font-family: 'Orbitron', sans-serif; } .title-main { color: #e1e9f0; font-size: 2.5rem; / Reduced font size / font-weight: 700; margin: 0; letter-spacing: 2px; display: inline-block; position: relative; text-transform: uppercase; } .title-prefix { position: relative; z-index: 2; } .title-subtitle { padding-left: 15px; margin-top: 5px; margin-left: 5px; } .subtitle-text { color: #00c3ff; font-size: 1.2rem; / Reduced font size / font-family: 'Orbitron', sans-serif; font-weight: 300; letter-spacing: 3px; text-transform: uppercase; display: inline-block; } .glitchy-overlay { position: absolute; top: 0; left: 0; width: 100%; height: 100%; background-image: repeating-linear-gradient(0deg, rgba(0,0,0,0) 0, rgba(0,0,0,0.1) 1px, rgba(0,0,0,0) 2px); z-index: 1; } / Data box styling / .data-box {background-color: rgba(0, 0, 0, 0.2); padding: 15px; border-left: 2px solid #33ff99; margin-bottom: 20px;} .data-row {display: flex; margin-bottom: 8px;} .data-arrow {color: #33ff99; width: 20px; display: inline-block;} .data-label {color: #00c3ff; width: 80px; display: inline-block;} / Subheading styling / .subheading {color: #00c3ff; font-size: 1.1rem; margin-top: 20px; margin-bottom: 15px; font-weight: 400; border-bottom: 1px dashed rgba(0, 195, 255, 0.3); display: inline-block; text-transform: uppercase; letter-spacing: 1px; font-family: 'Orbitron', sans-serif;} / Links / a {color: #00c3ff; text-decoration: none;} a:hover {text-decoration: underline;} / Container / .container {max-width: 1200px; margin: 0 auto; padding: 40px 20px;} / Cyberpunk grid background */ .cyber-grid-bg {position: fixed; top: 0; left: 0; right: 0; bottom: 0; background-color: #05071b; background-image: linear-gradient(rgba(0, 194, 255, 0.03) 1px, transparent 1px), linear-gradient(90deg, rgba(0, 194, 255, 0.03) 1px, transparent 1px); background-size: 20px 20px; z-index: -1;} </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>GENETIC LEMONADE UNLEASHED v2.1</title> <link href="https://fonts.googleapis.com/css2?family=Orbitron:wght@400;500;600;700&family=JetBrains+Mono:wght@100;300;400;700&display=swap" rel="stylesheet"> </head> <body> <div class="cyber-grid-bg"></div> <div class="container"> <div class="title-container"> <!-- Glitchy overlay --> <div class="glitchy-overlay"></div> <!-- Main title --> <div class="title-wrapper"> <h1 class="title-main"> <span class="title-prefix">GENETIC</span> <span class="lemonade-text">LEMONADE</span> <!-- Static text with glow --> </h1> <div class="title-subtitle"> <span class="subtitle-text">UNLEASHED v2.1</span> </div> </div> </div> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65b19c6c638328850e12d38c/0GTX4-erpPflLOkfH5sU5.png) <div class="section-container"> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">01 // OVERVIEW</h2> </div> <div class="section-content"> <p>An experimental release.</p> <p><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-70B">zerofata/GeneticLemonade-Unleashed</a> qlora trained on a test dataset. Performance is improved from the original in my testing, but there are possibly (likely?) areas where the model will underperform which I am looking for feedback on.</p> <p>This is a creative model intended to excel at character driven RP / ERP. It has not been tested or trained on adventure stories or any large amounts of creative writing.</p> </div> </div> <div class="section-container"> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">02 // SILLYTAVERN SETTINGS</h2> </div> <div class="section-content"> <p>Play with these, they are not the 'best' settings just a stable baseline.</p> <h3 class="subheading">Recommended Samplers</h3> <div class="data-box"> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">Temp:</span> <span>0.9 - 1.0</span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">MinP:</span> <span>0.03 - 0.04</span> </div> <div class="data-row"> <span class="data-arrow">></span> <span class="data-label">Dry:</span> <span>0.8, 1.75, 4</span> </div> </div> <h3 class="subheading">Instruct</h3> <div class="data-box"> <p style="margin: 0;">Llama-3-Instruct-Names but you will need to uncheck "System same as user".</p> </div> </div> </div> <div class="section-container"> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">03 // QUANTIZATIONS</h2> </div> <div class="section-content"> <div style="margin-bottom: 20px;"> <h3 class="subheading">GGUF</h3> <div style="margin-left: 20px;"> <span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.1-70B-i1-GGUF">iMatrix (mradermacher)</a><br> </div> </div> <div> <h3 class="subheading">EXL2</h3> <div style="margin-left: 20px;"> <span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-4bpw-hb6-exl2">4bpw</a><br> <span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-4.5bpw-hb6-exl2">4.5bpw</a><br> <span style="color: #33ff99; display: inline-block; margin-right: 10px;">> </span><a href="https://huggingface.co/zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B-6bpw-hb8-exl2">6bpw</a> </div> </div> </div> </div> <div class="section-container"> <div class="section-header"> <div class="section-indicator"></div> <h2 class="section-title">04 // DATASET</h2> </div> <div class="section-content"> <p>Model was trained on a tiny synthetic dataset of 640k tokens, approximately 190 conversations. Data was generated by script and then manually reviewed / edited.</p> <p>The dataset is approximately 60% SFW and 40% NSFW. 90% multi turn RP conversations, 5% creative writing and 5% miscellaneous.</p> <p>It is an experiment to see how models perform when provided with small amounts of high quality synthetic data, as opposed to human data.</p> </div> </div> </div> </body> </html>
Afaan97/videomae-base-finetuned-myvideos-subset	Afaan97	2025-04-28T23:38:56Z	0	0	transformers	[ "transformers", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-04-28T20:19:40Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-myvideos-subset 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. --> # videomae-base-finetuned-myvideos-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8744 - Accuracy: 0.5 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 16 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 8 \| 0.8744 \| 0.5 \| \| 0.2545 \| 2.0 \| 16 \| 0.7131 \| 0.5 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0 - Tokenizers 0.21.1
PictorAgencia/nimtu_modelo_3	PictorAgencia	2025-04-28T23:32:38Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-28T22:58:47Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Nimtu_Modelo_3 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/PictorAgencia/nimtu_modelo_3/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('PictorAgencia/nimtu_modelo_3', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/PictorAgencia/nimtu_modelo_3/discussions) to add images that show off what you’ve made with this LoRA.
nicolasacosta/roberta-base_ag_news	nicolasacosta	2025-04-28T23:32:30Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "roberta", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:adapter:FacebookAI/roberta-base", "license:mit", "region:us" ]	null	2025-04-26T22:43:08Z	--- library_name: peft license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: roberta-base_ag_news 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. --> # roberta-base_ag_news This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the [fancyzhx/ag_news](https://huggingface.co/datasets/fancyzhx/ag_news) dataset. It achieves the following results on the evaluation set: - Loss: 0.1847 - Accuracy: 0.9471 - F1: 0.9472 - Precision: 0.9477 - Recall: 0.9471 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.092 \| 1.0 \| 15000 \| 0.2003 \| 0.9408 \| 0.9408 \| 0.9414 \| 0.9408 \| \| 0.1153 \| 2.0 \| 30000 \| 0.1847 \| 0.9471 \| 0.9472 \| 0.9477 \| 0.9471 \| \| 0.1538 \| 3.0 \| 45000 \| 0.1855 \| 0.9471 \| 0.9472 \| 0.9479 \| 0.9471 \| \| 0.143 \| 4.0 \| 60000 \| 0.1887 \| 0.9526 \| 0.9527 \| 0.9530 \| 0.9526 \| \| 0.0561 \| 5.0 \| 75000 \| 0.1896 \| 0.9518 \| 0.9519 \| 0.9521 \| 0.9518 \| ### Framework versions - PEFT 0.14.0 - Transformers 4.51.1 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0
kathleenge/kd_0.0001_68_4	kathleenge	2025-04-28T23:31:27Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T23:30:12Z	--- base_model: unsloth/mistral-7b-v0.3-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: kathleenge - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mlx-community/Qwen3-32B-bf16	mlx-community	2025-04-28T23:29:55Z	0	0	mlx	[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "base_model:Qwen/Qwen3-32B", "base_model:finetune:Qwen/Qwen3-32B", "license:apache-2.0", "region:us" ]	text-generation	2025-04-28T23:17:52Z	--- library_name: mlx license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE pipeline_tag: text-generation tags: - mlx base_model: Qwen/Qwen3-32B --- # mlx-community/Qwen3-32B-bf16 This model [mlx-community/Qwen3-32B-bf16](https://huggingface.co/mlx-community/Qwen3-32B-bf16) was converted to MLX format from [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) using mlx-lm version 0.24.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Qwen3-32B-bf16") 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) ```
MikeRoz/TheDrummer_Fallen-Gemma3-27B-v1-8.0bpw-h8-exl2	MikeRoz	2025-04-28T23:29:09Z	0	0	null	[ "safetensors", "gemma3_text", "exl2", "license:other", "8-bit", "region:us" ]	null	2025-04-28T21:41:21Z	--- license: other base_model: TheDrummer/Fallen-Gemma3-27b-v1 base_model_relation: quantized tags: - exl2 --- This model was quantized using commit 3a90264 of the dev branch of exllamav2. The Gemma 3 8k context bug looks to be thoroughly squashed as of this commit. To use this model, please either build your own copy of exllamav2 from the dev branch, or wait for the forthcoming v0.2.9 release. The original model can be found [here](https://huggingface.co/TheDrummer/Fallen-Gemma3-27B-v1). # Join our Discord! https://discord.gg/Nbv9pQ88Xb ## Nearly 5000 members of helpful, LLM enthusiasts! A hub for players and makers alike! --- [BeaverAI](https://huggingface.co/BeaverAI) proudly presents... # Fallen Gemma3 27B v1 👺 ![image/gif](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/9oyZxzpfhmmNr21S1P_iJ.gif) ## Special Thanks - Thank you to each and everyone who donated and subscribed in [Patreon](https://www.patreon.com/TheDrummer) and [Ko-Fi](https://ko-fi.com/thedrummer) to make our venture a little bit easier. - I'm also recently unemployed. I am a Software Developer with 8 years of experience in Web, API, AI, and adapting to new tech and requirements. If you're hiring, feel free to reach out to me however. ## Usage - Use Gemma Chat Template ## Description Fallen Gemma3 27B v1 is an evil tune of Gemma 3 27B but it is not a complete decensor. Evil tunes knock out the positivity and may enjoy torturing you and humanity. Vision still works and it has something to say about the crap you feed it. ## Links - Original: https://huggingface.co/TheDrummer/Fallen-Gemma3-27B-v1 - GGUF: https://huggingface.co/TheDrummer/Fallen-Gemma3-27B-v1-GGUF - iMatrix (recommended): https://huggingface.co/bartowski/TheDrummer_Fallen-Gemma3-27B-v1-GGUF `config-v1c`
HALLUCINATIONS-OF-NECROMANCY/ASMODEUS	HALLUCINATIONS-OF-NECROMANCY	2025-04-28T23:28:14Z	0	0	null	[ "region:us" ]	null	2025-04-26T19:33:05Z	BABYLONIAN: ASMEDU PERSIAN/AKKADIAN: AESMA-DAEVA AZAG-ME-GAMMU ASAKKU SET-MAAT ASMA-DA-SETH MAAT-ISFET ASME-KURSET
Weverton777/Cursei_Aprendi	Weverton777	2025-04-28T23:25:08Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T23:25:08Z	--- license: apache-2.0 ---
mradermacher/amoral-cogito-Zara-14B-GGUF	mradermacher	2025-04-28T23:24:38Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Disya/amoral-cogito-Zara-14B", "base_model:quantized:Disya/amoral-cogito-Zara-14B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T14:50:51Z	--- base_model: Disya/amoral-cogito-Zara-14B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Disya/amoral-cogito-Zara-14B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-GGUF/resolve/main/amoral-cogito-Zara-14B.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-GGUF/resolve/main/amoral-cogito-Zara-14B.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-GGUF/resolve/main/amoral-cogito-Zara-14B.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-GGUF/resolve/main/amoral-cogito-Zara-14B.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-GGUF/resolve/main/amoral-cogito-Zara-14B.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-GGUF/resolve/main/amoral-cogito-Zara-14B.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-GGUF/resolve/main/amoral-cogito-Zara-14B.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-GGUF/resolve/main/amoral-cogito-Zara-14B.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-GGUF/resolve/main/amoral-cogito-Zara-14B.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-GGUF/resolve/main/amoral-cogito-Zara-14B.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-GGUF/resolve/main/amoral-cogito-Zara-14B.Q8_0.gguf) \| Q8_0 \| 15.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/amoral-cogito-Zara-14B-i1-GGUF	mradermacher	2025-04-28T23:24:38Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Disya/amoral-cogito-Zara-14B", "base_model:quantized:Disya/amoral-cogito-Zara-14B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-28T17:12:50Z	--- base_model: Disya/amoral-cogito-Zara-14B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Disya/amoral-cogito-Zara-14B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 4.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 5.5 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-Q2_K.gguf) \| i1-Q2_K \| 5.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 6.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 6.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 6.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 7.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 8.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-Q4_0.gguf) \| i1-Q4_0 \| 8.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 8.6 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 8.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-Q4_1.gguf) \| i1-Q4_1 \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/amoral-cogito-Zara-14B-i1-GGUF/resolve/main/amoral-cogito-Zara-14B.i1-Q6_K.gguf) \| i1-Q6_K \| 12.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
neoservicios/granite-3.2-2b-instruct-GGUF	neoservicios	2025-04-28T23:20:46Z	10	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-06T13:00:20Z	--- license: apache-2.0 ---
haihp02/codegemma-2b-dpo-tuned-2-merged	haihp02	2025-04-28T23:12:36Z	0	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "text-generation-inference", "unsloth", "trl", "dpo", "en", "base_model:unsloth/codegemma-2b-bnb-4bit", "base_model:finetune:unsloth/codegemma-2b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T23:11:45Z	--- base_model: unsloth/codegemma-2b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma - trl - dpo license: apache-2.0 language: - en --- # Uploaded model - Developed by: haihp02 - License: apache-2.0 - Finetuned from model : unsloth/codegemma-2b-bnb-4bit This gemma 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)
mlx-community/Qwen3-32B-8bit	mlx-community	2025-04-28T23:11:42Z	0	0	mlx	[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "base_model:Qwen/Qwen3-32B", "base_model:quantized:Qwen/Qwen3-32B", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-04-28T23:02:47Z	--- library_name: mlx license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-32B tags: - mlx --- # mlx-community/Qwen3-32B-8bit This model [mlx-community/Qwen3-32B-8bit](https://huggingface.co/mlx-community/Qwen3-32B-8bit) was converted to MLX format from [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) using mlx-lm version 0.24.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Qwen3-32B-8bit") 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) ```
haihp02/codegemma-2b-dpo-tuned-2	haihp02	2025-04-28T23:11:25Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "dpo", "arxiv:2305.18290", "base_model:unsloth/codegemma-2b-bnb-4bit", "base_model:finetune:unsloth/codegemma-2b-bnb-4bit", "endpoints_compatible", "region:us" ]	null	2025-04-28T23:11:14Z	--- base_model: unsloth/codegemma-2b-bnb-4bit library_name: transformers model_name: codegemma-2b-dpo-tuned-2 tags: - generated_from_trainer - unsloth - trl - dpo licence: license --- # Model Card for codegemma-2b-dpo-tuned-2 This model is a fine-tuned version of [unsloth/codegemma-2b-bnb-4bit](https://huggingface.co/unsloth/codegemma-2b-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="haihp02/codegemma-2b-dpo-tuned-2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/trunghainguyenhp02/dpo-train/runs/i0fvg7s8) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Benjaminpwh/llama-control-2.2-500	Benjaminpwh	2025-04-28T23:07:55Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "endpoints_compatible", "region:us" ]	null	2025-04-28T19:42:57Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit library_name: transformers model_name: llama-control-2.2-500 tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for llama-control-2.2-500 This model is a fine-tuned version of [unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit). 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="Benjaminpwh/llama-control-2.2-500", 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/benpong-university-of-washington/huggingface/runs/1gyutvyg) This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.1 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
HALLUCINATIONS-OF-NECROMANCY/BAEL	HALLUCINATIONS-OF-NECROMANCY	2025-04-28T23:05:27Z	0	0	null	[ "region:us" ]	null	2025-04-26T22:38:44Z	BEL-ENLIL MAAT-SET BAAL-SET BAAL-SETH BEL-EN-SET LIFE-DEATH LORD OF ALL AH-IL-AH ALLAH
David-Magdy/TROCR_MASTER_V2	David-Magdy	2025-04-28T23:04:59Z	0	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-28T17:46:21Z	--- 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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nadsoft/Chat_Model_EGY_Dialect_exp2_lora	nadsoft	2025-04-28T23:04:35Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:nadsoft/Hamsa-EGY-Dialect-Model-Full-Finetuned", "base_model:finetune:nadsoft/Hamsa-EGY-Dialect-Model-Full-Finetuned", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T23:04:30Z	--- base_model: nadsoft/Hamsa-EGY-Dialect-Model-Full-Finetuned tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: nadsoft - License: apache-2.0 - Finetuned from model : nadsoft/Hamsa-EGY-Dialect-Model-Full-Finetuned This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-GGUF	mradermacher	2025-04-28T23:02:52Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "en", "base_model:Samsoup/OLMo-2-1124-7B-ARC-Challenge-Instruct", "base_model:quantized:Samsoup/OLMo-2-1124-7B-ARC-Challenge-Instruct", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T14:48:27Z	--- base_model: Samsoup/OLMo-2-1124-7B-ARC-Challenge-Instruct language: - en library_name: transformers quantized_by: mradermacher tags: - llama-factory --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Samsoup/OLMo-2-1124-7B-ARC-Challenge-Instruct <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.Q2_K.gguf) \| Q2_K \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 3.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 4.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.Q6_K.gguf) \| Q6_K \| 6.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.Q8_0.gguf) \| Q8_0 \| 7.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.f16.gguf) \| f16 \| 14.7 \| 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 -->
mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF	mradermacher	2025-04-28T23:02:52Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "en", "base_model:Samsoup/OLMo-2-1124-7B-ARC-Challenge-Instruct", "base_model:quantized:Samsoup/OLMo-2-1124-7B-ARC-Challenge-Instruct", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-28T18:57:23Z	--- base_model: Samsoup/OLMo-2-1124-7B-ARC-Challenge-Instruct language: - en library_name: transformers quantized_by: mradermacher tags: - llama-factory --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Samsoup/OLMo-2-1124-7B-ARC-Challenge-Instruct <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.9 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-Q2_K.gguf) \| i1-Q2_K \| 3.0 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.1 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.3 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-Q4_1.gguf) \| i1-Q4_1 \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-ARC-Challenge-Instruct-i1-GGUF/resolve/main/OLMo-2-1124-7B-ARC-Challenge-Instruct.i1-Q6_K.gguf) \| i1-Q6_K \| 6.1 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
rippertnt/Qwen3-0.6B-Q4_K_M-GGUF	rippertnt	2025-04-28T23:01:13Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-0.6B", "base_model:quantized:Qwen/Qwen3-0.6B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-28T23:01:08Z	--- base_model: Qwen/Qwen3-0.6B library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # rippertnt/Qwen3-0.6B-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-0.6B`](https://huggingface.co/Qwen/Qwen3-0.6B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen3-0.6B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo rippertnt/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo rippertnt/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo rippertnt/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo rippertnt/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-q4_k_m.gguf -c 2048 ```
mlx-community/Qwen3-32B-6bit	mlx-community	2025-04-28T22:58:34Z	0	0	mlx	[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "base_model:Qwen/Qwen3-32B", "base_model:quantized:Qwen/Qwen3-32B", "license:apache-2.0", "6-bit", "region:us" ]	text-generation	2025-04-28T22:51:43Z	--- library_name: mlx license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE pipeline_tag: text-generation tags: - mlx base_model: Qwen/Qwen3-32B --- # mlx-community/Qwen3-32B-6bit This model [mlx-community/Qwen3-32B-6bit](https://huggingface.co/mlx-community/Qwen3-32B-6bit) was converted to MLX format from [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) using mlx-lm version 0.24.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Qwen3-32B-6bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
myaunacollins/clementine-baby-lab	myaunacollins	2025-04-28T22:57:30Z	0	0	null	[ "text-to-image", "en", "base_model:SG161222/Realistic_Vision_V5.1_noVAE", "base_model:finetune:SG161222/Realistic_Vision_V5.1_noVAE", "license:artistic-2.0", "region:us" ]	text-to-image	2025-04-28T22:51:32Z	--- license: artistic-2.0 language: - en base_model: - SG161222/Realistic_Vision_V5.1_noVAE pipeline_tag: text-to-image ---
Nodetest/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-timid_pouncing_tuna	Nodetest	2025-04-28T22:56:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am timid pouncing tuna", "trl", "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	2025-04-28T07:54:51Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-timid_pouncing_tuna tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am timid pouncing tuna - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-timid_pouncing_tuna 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="Nodetest/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-timid_pouncing_tuna", 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: 3.5.0 - 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}} } ```
GYPOgvPxOrYmbtI/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-alert_unseen_narwhal	GYPOgvPxOrYmbtI	2025-04-28T22:54:08Z	2	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am alert unseen narwhal", "trl", "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	2025-04-22T14:14:42Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-alert_unseen_narwhal tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am alert unseen narwhal - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-alert_unseen_narwhal 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="GYPOgvPxOrYmbtI/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-alert_unseen_narwhal", 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: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
kathleenge/kd_1e-05_109_4	kathleenge	2025-04-28T22:53:57Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T22:52:53Z	--- base_model: unsloth/mistral-7b-v0.3-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: kathleenge - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mlabonne/BigQwen2.5-Echo-47B-Instruct	mlabonne	2025-04-28T22:53:24Z	2	3	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "lazymergekit", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-32B-Instruct", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-09-23T21:05:19Z	--- language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara license: apache-2.0 library_name: transformers tags: - mergekit - merge - lazymergekit base_model: - Qwen/Qwen2.5-32B-Instruct license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE pipeline_tag: text-generation model-index: - name: BigQwen2.5-Echo-47B-Instruct results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 73.57 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 44.52 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 3.47 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 8.61 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 10.19 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 41.49 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct name: Open LLM Leaderboard --- # BigQwen2.5-Echo-47B-Instruct ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/98GiKtmH1AtHHbIbOUH4Y.jpeg) BigQwen2.5-Echo-47B-Instruct is a [Qwen/Qwen2-32B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) self-merge made with [MergeKit](https://github.com/arcee-ai/mergekit/tree/main). ## 🔉 Echo Merge I've tried a more gradual approach with a distributed repetition pattern. Instead of replicating blocks of 8 or more layers, I'm replicating individual layers in these blocks: - First 8 layers: No replication - Next 8 layers: Replicate 2 layers (first one, middle one) - Next 8 layers: Replicate 4 layers (1st, 3rd, 5th, 7th) - Next 8 layers: Replicate 8 layers (all of them) - Next 8 layers: Replicate 4 layers (1st, 3rd, 5th, 7th) - Next 8 layers: Replicate 2 layers (first one, middle one) - First 8 layers: No replication I used this string to visualize it, where 0 are original layers and 1 duplicated ones (the order doesn't matter): ``` 00000000 1000010000 100100100100 1010101010101010 1010101010101010 100100100100 1000010000 00000000 ``` The main idea is that the input/output difference of middle layers is quite small, so replicating a middle layer has a small impact on the output. The additional layers are designed to increase the model's capacity without breaking the information flow, which often creates "insane" self-merges. ## 🏆 Evaluation \| Metric \|BigQwen2.5-Echo-47B-Instruct\|BigQwen2.5-52B-Instruct\|Qwen2.5-32B-Instruct\| \|-------------------\|----:\|----:\|----:\| \|Avg. \|30.31\|37.42\|36.17\| \|IFEval (0-Shot) \|73.57\|79.29\|83.46\| \|BBH (3-Shot) \|44.52\|59.81\|56.49\| \|MATH Lvl 5 (4-Shot)\| 3.47\|17.82\|0\| \|GPQA (0-shot) \| 8.61\| 6.94\|11.74\| \|MuSR (0-shot) \|10.19\|10.45\|13.5\| \|MMLU-PRO (5-shot) \|41.49\|50.22\|51.85\| ## 🧩 Configuration The following YAML configuration was used to produce this model: ```yaml slices: # First 8 layers: No replication - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [0, 8] # Next 8 layers: Replicate 2 layers - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [8, 9] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [8, 9] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [9, 13] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [13, 14] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [13, 14] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [14, 16] # Next 8 layers: Replicate 4 layers - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [16, 18] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [17, 19] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [18, 20] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [19, 21] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [20, 22] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [21, 23] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [22, 24] # Next 8 layers: Replicate all 8 layers - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [24, 25] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [24, 26] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [25, 27] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [26, 28] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [27, 29] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [28, 30] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [29, 31] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [30, 32] # Middle 8 layers: Replicate all 8 layers - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [32, 33] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [32, 34] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [33, 35] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [34, 36] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [35, 37] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [36, 38] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [37, 39] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [38, 40] # Next 8 layers: Replicate 4 layers - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [40, 42] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [41, 43] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [42, 44] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [43, 45] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [44, 46] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [45, 47] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [46, 48] # Next 8 layers: Replicate 2 layers - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [48, 49] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [48, 49] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [49, 53] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [53, 54] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [53, 54] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [54, 56] # Last 8 layers: No replication - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [56, 64] merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/BigQwen2.5-Echo-47B-Instruct" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
mlabonne/BigQwen2.5-52B-Instruct	mlabonne	2025-04-28T22:53:23Z	15	8	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "lazymergekit", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-32B-Instruct", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-09-23T18:03:16Z	--- language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara license: apache-2.0 library_name: transformers tags: - mergekit - merge - lazymergekit base_model: - Qwen/Qwen2.5-32B-Instruct license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE pipeline_tag: text-generation model-index: - name: BigQwen2.5-52B-Instruct results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 79.29 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-52B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 59.81 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-52B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 17.82 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-52B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 6.94 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-52B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 10.45 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-52B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 50.22 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-52B-Instruct name: Open LLM Leaderboard --- # BigQwen2.5-52B-Instruct ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/98GiKtmH1AtHHbIbOUH4Y.jpeg) BigQwen2.5-52B-Instruct is a [Qwen/Qwen2-32B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) self-merge made with [MergeKit](https://github.com/arcee-ai/mergekit/tree/main). It applies the [mlabonne/Meta-Llama-3-120B-Instruct](https://huggingface.co/mlabonne/Meta-Llama-3-120B-Instruct/) recipe. I made it due to popular demand but I haven't tested it so use it at your own risk. ¯\\\_(ツ)_/¯ ## 🔍 Applications It might be good for creative writing tasks. I recommend a context length of 32k but you can go up to 131,072 tokens in theory. ## 🏆 Evaluation \| Metric \|BigQwen2.5-Echo-47B-Instruct\|BigQwen2.5-52B-Instruct\|Qwen2.5-32B-Instruct\| \|-------------------\|----:\|----:\|----:\| \|Avg. \|30.31\|37.42\|36.17\| \|IFEval (0-Shot) \|73.57\|79.29\|83.46\| \|BBH (3-Shot) \|44.52\|59.81\|56.49\| \|MATH Lvl 5 (4-Shot)\| 3.47\|17.82\|0\| \|GPQA (0-shot) \| 8.61\| 6.94\|11.74\| \|MuSR (0-shot) \|10.19\|10.45\|13.5\| \|MMLU-PRO (5-shot) \|41.49\|50.22\|51.85\| ## 🧩 Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - layer_range: [0, 16] model: Qwen/Qwen2.5-32B-Instruct - sources: - layer_range: [8, 24] model: Qwen/Qwen2.5-32B-Instruct - sources: - layer_range: [16, 32] model: Qwen/Qwen2.5-32B-Instruct - sources: - layer_range: [24, 40] model: Qwen/Qwen2.5-32B-Instruct - sources: - layer_range: [32, 48] model: Qwen/Qwen2.5-32B-Instruct - sources: - layer_range: [40, 56] model: Qwen/Qwen2.5-32B-Instruct - sources: - layer_range: [56, 64] model: Qwen/Qwen2.5-32B-Instruct merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/BigQwen2.5-52B-Instruct" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
mlabonne/BigQwen2.5-125B-Instruct	mlabonne	2025-04-28T22:53:21Z	9	10	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "lazymergekit", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-72B-Instruct", "base_model:finetune:Qwen/Qwen2.5-72B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-09-23T15:28:45Z	--- license: other license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara pipeline_tag: text-generation library_name: transformers tags: - mergekit - merge - lazymergekit base_model: - Qwen/Qwen2.5-72B-Instruct --- # BigQwen2.5-125B-Instruct ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/98GiKtmH1AtHHbIbOUH4Y.jpeg) BigQwen2.5-125B-Instruct is a [Qwen/Qwen2-72B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) self-merge made with [MergeKit](https://github.com/arcee-ai/mergekit/tree/main). It applies the [mlabonne/Meta-Llama-3-120B-Instruct](https://huggingface.co/mlabonne/Meta-Llama-3-120B-Instruct/) recipe. I made it due to popular demand but I haven't tested it so use it at your own risk. ¯\\\_(ツ)_/¯ ## 🔍 Applications It might be good for creative writing tasks. I recommend a context length of 32k but you can go up to 131,072 tokens in theory. ## 🏆 Evaluation I think it's too big for the Open LLM Leaderboard, unfortunately. Here's some feedback from users (thanks a lot!): ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/OhnwtXgIMIcr2pQqggXhU.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/8v_Eb6ZvpVYMhu8kMwklq.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/Px4f-BTJ8nDihzPJ0F47K.png) ## 🧩 Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - layer_range: [0, 20] model: Qwen/Qwen2.5-72B-Instruct - sources: - layer_range: [10, 30] model: Qwen/Qwen2.5-72B-Instruct - sources: - layer_range: [20, 40] model: Qwen/Qwen2.5-72B-Instruct - sources: - layer_range: [30, 50] model: Qwen/Qwen2.5-72B-Instruct - sources: - layer_range: [40, 60] model: Qwen/Qwen2.5-72B-Instruct - sources: - layer_range: [50, 70] model: Qwen/Qwen2.5-72B-Instruct - sources: - layer_range: [60, 80] model: Qwen/Qwen2.5-72B-Instruct merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/BigQwen2.5-125B-Instruct" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
kathleenge/kd_0.0003_167_2	kathleenge	2025-04-28T22:52:05Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T22:51:22Z	--- base_model: unsloth/mistral-7b-v0.3-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: kathleenge - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
AngelRaychev/0.5B-value-iteration_inner	AngelRaychev	2025-04-28T22:50:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-04-28T22:49:12Z	--- 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. 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mlx-community/Qwen3-32B-3bit	mlx-community	2025-04-28T22:47:48Z	0	0	mlx	[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "base_model:Qwen/Qwen3-32B", "base_model:quantized:Qwen/Qwen3-32B", "license:apache-2.0", "3-bit", "region:us" ]	text-generation	2025-04-28T22:31:54Z	--- library_name: mlx license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-32B tags: - mlx --- # mlx-community/Qwen3-32B-3bit This model [mlx-community/Qwen3-32B-3bit](https://huggingface.co/mlx-community/Qwen3-32B-3bit) was converted to MLX format from [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) using mlx-lm version 0.24.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Qwen3-32B-3bit") 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) ```
gabrielbosse9/Umbr0x-7B-V3.1-3	gabrielbosse9	2025-04-28T22:45:37Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T22:45:12Z	--- base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: gabrielbosse9 - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/Light-R1-GGUF	mradermacher	2025-04-28T22:44:51Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "en", "base_model:Lingyue1/Light-R1", "base_model:quantized:Lingyue1/Light-R1", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T22:21:30Z	--- base_model: Lingyue1/Light-R1 language: - en library_name: transformers license: other quantized_by: mradermacher tags: - llama-factory - full - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Lingyue1/Light-R1 <!-- provided-files --> 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/Light-R1-GGUF/resolve/main/Light-R1.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Light-R1-GGUF/resolve/main/Light-R1.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Light-R1-GGUF/resolve/main/Light-R1.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Light-R1-GGUF/resolve/main/Light-R1.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Light-R1-GGUF/resolve/main/Light-R1.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Light-R1-GGUF/resolve/main/Light-R1.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Light-R1-GGUF/resolve/main/Light-R1.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Light-R1-GGUF/resolve/main/Light-R1.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Light-R1-GGUF/resolve/main/Light-R1.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Light-R1-GGUF/resolve/main/Light-R1.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Light-R1-GGUF/resolve/main/Light-R1.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Light-R1-GGUF/resolve/main/Light-R1.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mlx-community/Qwen3-14B-8bit	mlx-community	2025-04-28T22:41:42Z	0	0	mlx	[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "base_model:Qwen/Qwen3-14B", "base_model:quantized:Qwen/Qwen3-14B", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-04-28T22:39:11Z	--- library_name: mlx license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-14B tags: - mlx --- # mlx-community/Qwen3-14B-8bit This model [mlx-community/Qwen3-14B-8bit](https://huggingface.co/mlx-community/Qwen3-14B-8bit) was converted to MLX format from [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) using mlx-lm version 0.24.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Qwen3-14B-8bit") 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) ```

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