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lodestones/Chroma	lodestones	2025-05-21T23:23:48Z	0	637	pytorch	[ "pytorch", "text-to-image", "image-generation", "chroma", "en", "license:apache-2.0", "region:us" ]	text-to-image	2025-01-27T22:04:09Z	--- language: - en license: apache-2.0 tags: - text-to-image - image-generation - chroma pipeline_tag: text-to-image library_name: pytorch --- # Chroma: Open-Source, Uncensored, and Built for the Community Chroma is a 8.9B parameter model based on FLUX.1-schnell (technical report coming soon!). It’s fully Apache 2.0 licensed, ensuring that anyone can use, modify, and build on top of it—no corporate gatekeeping. The model is still training right now, and I’d love to hear your thoughts! Your input and feedback are really appreciated. # What Chroma Aims to Do * Training on a 5M dataset, curated from 20M samples including anime, furry, artistic stuff, and photos. * Fully uncensored, reintroducing missing anatomical concepts. * Built as a reliable open-source option for those who need it. # See the Progress * Hugging Face Debug Repo: [https://huggingface.co/lodestones/chroma-debug-development-only](https://huggingface.co/lodestones/chroma-debug-development-only) * Live AIM Training Logs: [https://training.lodestone-rock.com](https://training.lodestone-rock.com) * Training code!: [https://github.com/lodestone-rock/flow](https://github.com/lodestone-rock/flow) * CivitAi gallery: [https://civitai.com/posts/13766416](https://civitai.com/posts/13766416) * CivitAi model: [https://civitai.com/models/1330309/chroma](https://civitai.com/models/1330309/chroma) # Special Thanks Shoutout to Fictional.ai for the awesome support — seriously appreciate you helping push open-source AI forward. You can try it over on their site: [![FictionalChromaBanner_1.png](./FictionalChromaBanner_1.png)](https://fictional.ai/?ref=chroma_hf) # Support Open-Source AI The current pretraining run has already used 6000+ H100 hours, and keeping this going long-term is expensive. If you believe in accessible, community-driven AI, any support would be greatly appreciated. 👉 [https://ko-fi.com/lodestonerock](https://ko-fi.com/lodestonerock) — Every bit helps! ETH: 0x679C0C419E949d8f3515a255cE675A1c4D92A3d7 my discord: [discord.gg/SQVcWVbqKx](http://discord.gg/SQVcWVbqKx) ![Chroma Workflow](./ChromaSimpleWorkflow20250507_sample.png) ![Workflow Overview](./ChromaSimpleWorkflow20250507_overview.png) ![Alpha_Preview](./collage.png) ## Table of Contents - [Chroma: Open-Source, Uncensored, and Built for the Community](#chroma-open-source-uncensored-and-built-for-the-community) - [How to run this model](#how-to-run-this-model) - [ComfyUI](#comfyui) - diffusers [WIP] - brief tech report - [Architectural modifications](#architectural-modifications) - [12B → 8.9B](#12b-%E2%86%92-89b) - [MMDiT masking](#mmdit-masking) - [Timestep Distributions](#timestep-distributions) - [Minibatch Optimal Transport](#minibatch-optimal-transport) [WIP] - [Training Details] - [T5 QAT training] [WIP] - [Prior preserving distribution training] [WIP] - [Scramming] [WIP] - [blockwise droppout optimizers] [WIP] - [Citation](#citation) # How to run this model ## ComfyUI ### Requirements - ComfyUI installation - [Chroma checkpoint](https://huggingface.co/lodestones/Chroma) (pick the latest version on this repo) - [Alternative option: FP8 Scaled Quant](https://huggingface.co/Clybius/Chroma-fp8-scaled) (Format used by ComfyUI with possible inference speed increase) - [Alternative option: GGUF Quantized](https://huggingface.co/silveroxides/Chroma-GGUF) (You will need to install ComfyUI-GGUF custom node) - [T5 XXL](https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp16.safetensors) or [T5 XXL fp8](https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp8_e4m3fn.safetensors) (either of them will work) - [FLUX VAE](https://huggingface.co/lodestones/Chroma/resolve/main/ae.safetensors) - [Chroma_Workflow](https://huggingface.co/lodestones/Chroma/resolve/main/ChromaSimpleWorkflow20250507.json) ### Deprecated: Manual Installation (Chroma) 1. Navigate to your ComfyUI's `ComfyUI/custom_nodes` folder 2. Clone the repository: ```bash git clone https://github.com/lodestone-rock/ComfyUI_FluxMod.git ``` 3. Restart ComfyUI 4. Refresh your browser if ComfyUI is already running ### How to run the model 1. put `T5_xxl` into `ComfyUI/models/clip` folder 2. put `FLUX VAE` into `ComfyUI/models/vae` folder 3. put `Chroma checkpoint` into `ComfyUI/models/diffusion_models` folder 4. load chroma workflow to your ComfyUI 5. Run the workflow # Architectural Modifications ## 12B → 8.9B ### TL;DR: There are 3.3B parameters that only encode a single input vector, which I replaced with 250M params. Since FLUX is so big, I had to modify the architecture and ensure minimal knowledge was lost in the process. The most obvious thing to prune was this modulation layer. In the diagram, it may look small, but in total, FLUX has 3.3B parameters allocated to it. Without glazing over the details too much, this layer's job is to let the model know which timestep it's at during the denoising process. This layer also receives information from pooled CLIP vectors. ![affine_projection_AdaLN_begone](./prune.png) But after a simple experiment of zeroing these pooled vectors out, the model’s output barely changed—which made pruning a breeze! Why? Because the only information left for this layer to encode is just a single number in the range of 0-1. Yes, you heard it right—3.3B parameters were used to encode 8 bytes of float values. So this was the most obvious layer to prune and replace with a simple FFN. The whole replacement process only took a day on my single 3090, and after that, the model size was reduced to just 8.9B. ## MMDiT Masking ### TL;DR: Masking T5 padding tokens enhanced fidelity and increased stability during training. It might not be obvious, but BFL had some oversight during pre-training where they forgot to mask both T5 and MMDiT tokens. So, for example, a short sentence like “a cat sat on a mat” actually looks like this in both T5 and MMDiT: `<bos> a cat sat on a mat <pad><pad>...<pad><pad><pad>` ![padding_mask](./mask.png) The model ends up paying way too much attention to padding tokens, drowning out the actual prompt information. The fix? Masking—so the model doesn’t associate anything with padding tokens. But there’s a catch: if you mask out all padding tokens, the model falls out of distribution and generates a blurry mess. The solution? Unmask just one padding token while masking the rest. With this fix, MMDiT now only needs to pay attention to: `<bos> a cat sat on a mat <pad>` ## Timestep Distributions ### TL;DR: A custom timestep distribution prevents loss spikes during training. When training a diffusion/flow model, we sample random timesteps—but not evenly. Why? Because empirically, training on certain timesteps more often makes the model converge faster. FLUX uses a "lognorm" distribution, which prioritizes training around the middle timesteps. But this approach has a flaw: the tails—where high-noise and low-noise regions exist—are trained super sparsely. If you train for a looong time (say, 1000 steps), the likelihood of hitting those tail regions is almost zero. The problem? When the model finally does see them, the loss spikes hard, throwing training out of whack—even with a huge batch size. The fix is simple: sample and train those tail timesteps a bit more frequently using a `-x^2` function instead. You can see in the image that this makes the distribution thicker near 0 and 1, ensuring better coverage. ![timestep](./timestep.png) ## Minibatch Optimal Transport ### TL;DR: Transport problem math magic :P This one’s a bit math-heavy, but here’s the gist: FLUX isn’t actually "denoising" an image. What we’re really doing is training a vector field to map one distribution (noise) to another (image). Once the vector field is learned, we "flow" through it to transform noise into an image. To keep it simple—just check out these two visuals: [graph placeholder] By choosing better pairing through math magic it accelerates training by reducing the “path ambiguity” ## Citation ``` @misc{rock2025chroma, author = {Lodestone Rock}, title = {{Chroma: Open-Source, Uncensored, and Built for the Community}}, year = {2025}, note = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/lodestones/Chroma}}, } ```
juanperez2022/lapkits2	juanperez2022	2025-05-21T23:22:57Z	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	2025-05-21T22:59:04Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md 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: lapkits --- # Lapkits2 <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 `lapkits` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "lapkits", "lora_weights": "https://huggingface.co/juanperez2022/lapkits2/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('juanperez2022/lapkits2', weight_name='lora.safetensors') image = pipeline('lapkits').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: 20 ## Contribute your own examples You can use the [community tab](https://huggingface.co/juanperez2022/lapkits2/discussions) to add images that show off what you’ve made with this LoRA.
mfstehling/bert-hate-speech-test	mfstehling	2025-05-21T23:22:49Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-21T23:22:31Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: bert-hate-speech-test 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. --> # bert-hate-speech-test This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None 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: 3 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
99eren99/TrColBERT	99eren99	2025-05-21T23:20:48Z	4	0	PyLate	[ "PyLate", "safetensors", "modernbert", "ColBERT", "sentence-transformers", "sentence-similarity", "feature-extraction", "generated_from_trainer", "loss:Distillation", "turkish", "arxiv:1908.10084", "region:us" ]	sentence-similarity	2025-04-07T11:45:47Z	--- tags: - ColBERT - PyLate - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - loss:Distillation - turkish pipeline_tag: sentence-similarity library_name: PyLate --- # PyLate This is a [PyLate](https://github.com/lightonai/pylate) model trained. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. ## Model Details ### Model Description - Model Type: PyLate model <!-- - Base model: [Unknown](https://huggingface.co/unknown) --> - Document Length: 512 tokens - Query Length: 32 tokens - Output Dimensionality: 128 tokens - Similarity Function: MaxSim <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [PyLate Documentation](https://lightonai.github.io/pylate/) - Repository: [PyLate on GitHub](https://github.com/lightonai/pylate) - Hugging Face: [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate) ### Full Model Architecture ``` ColBERT( (0): Transformer({'max_seq_length': 511, 'do_lower_case': False}) with Transformer model: ModernBertModel (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) ) ``` # Evaluation nDCG and Recall scores of this model(out-of-domain predictions) and other multilingual late interaction retrieval models on [Tr-NanoBEIR](https://huggingface.co/datasets/99eren99/Tr-NanoBEIR). Test code and detailed metrics in ["./assets"](https://huggingface.co/99eren99/TrColBERT/tree/main/assets) <img src="https://huggingface.co/99eren99/TrColBERT/resolve/main/assets/scores.jpg" alt="drawing"/> ## Usage First install required libraries (Flash Attention 2 supporting GPU is a must for consistency otherwise you need to mask query expansion tokens in the output layer manually): ```bash pip install -U einops flash_attn pip install -U pylate ``` Then normalize your text ---> lambda x: x.replace("İ", "i").replace("I", "ı").lower() ### Retrieval PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval. #### Indexing documents First, load the ColBERT model and initialize the Voyager index, then encode and index your documents: ```python from pylate import indexes, models, retrieve # Step 1: Load the ColBERT model model = models.ColBERT( model_name_or_path="99eren99/TrColBERT", ) try: model.tokenizer.model_input_names.remove("token_type_ids") except: pass model.eval() model.to("cuda") # Step 2: Initialize the Voyager index index = indexes.Voyager( index_folder="pylate-index", index_name="index", override=True, # This overwrites the existing index if any ) # Step 3: Encode the documents documents_ids = ["1", "2", "3"] documents = ["document 1 text", "document 2 text", "document 3 text"] documents_embeddings = model.encode( documents, batch_size=32, is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries show_progress_bar=True, ) # Step 4: Add document embeddings to the index by providing embeddings and corresponding ids index.add_documents( documents_ids=documents_ids, documents_embeddings=documents_embeddings, ) ``` Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it: ```python # To load an index, simply instantiate it with the correct folder/name and without overriding it index = indexes.Voyager( index_folder="pylate-index", index_name="index", ) ``` #### Retrieving top-k documents for queries Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores: ```python # Step 1: Initialize the ColBERT retriever retriever = retrieve.ColBERT(index=index) # Step 2: Encode the queries queries_embeddings = model.encode( ["query for document 3", "query for document 1"], batch_size=32, is_query=True, # Ensure that it is set to True to indicate that these are queries show_progress_bar=True, ) # Step 3: Retrieve top-k documents scores = retriever.retrieve( queries_embeddings=queries_embeddings, k=10, # Retrieve the top 10 matches for each query ) ``` ### Reranking If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank: ```python from pylate import rank, models queries = [ "query A", "query B", ] documents = [ ["document A", "document B"], ["document 1", "document C", "document B"], ] documents_ids = [ [1, 2], [1, 3, 2], ] model = models.ColBERT( model_name_or_path=pylate_model_id, ) queries_embeddings = model.encode( queries, is_query=True, ) documents_embeddings = model.encode( documents, is_query=False, ) reranked_documents = rank.rerank( documents_ids=documents_ids, queries_embeddings=queries_embeddings, documents_embeddings=documents_embeddings, ) ``` <!-- ### 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. --> <!-- ## 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 Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 32 - `gradient_accumulation_steps`: 4 - `learning_rate`: 8e-05 - `num_train_epochs`: 4 - `warmup_ratio`: 0.05 - `bf16`: True - `tf32`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 4 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 8e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.05 - `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`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: True - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: 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 - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Framework Versions - Python: 3.10.16 - Sentence Transformers: 4.0.2 - PyLate: 1.1.7 - Transformers: 4.48.2 - PyTorch: 2.5.1+cu124 - Accelerate: 1.2.1 - Datasets: 2.21.0 - Tokenizers: 0.21.0 ## 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" } ``` #### PyLate ```bibtex @misc{PyLate, title={PyLate: Flexible Training and Retrieval for Late Interaction Models}, author={Chaffin, Antoine and Sourty, Raphaël}, url={https://github.com/lightonai/pylate}, year={2024} } ``` <!-- ## 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. -->
rosieyzh/uf-dpo-llama3_1_8b_instruct-checkpoint_2750-seed_42	rosieyzh	2025-05-21T23:18:56Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T23:12:49Z	--- 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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KOTI614/Portifolio	KOTI614	2025-05-21T23:16:09Z	0	0	null	[ "region:us" ]	null	2025-05-21T23:01:54Z	This is a [Next.js](https://nextjs.org/) project bootstrapped with [`create-next-app`](https://github.com/vercel/next.js/tree/canary/packages/create-next-app). ## Getting Started First, run the development server: ```bash npm run dev # or yarn dev # or pnpm dev # or bun dev ``` Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. You can start editing the page by modifying `app/page.js`. The page auto-updates as you edit the file. This project uses [`next/font`](https://nextjs.org/docs/basic-features/font-optimization) to automatically optimize and load Inter, a custom Google Font. ## Learn More To learn more about Next.js, take a look at the following resources: - [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API. - [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial. You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js/) - your feedback and contributions are welcome! ## Deploy on Vercel The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js. Check out our [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details.
mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF	mradermacher	2025-05-21T23:14:59Z	49	0	transformers	[ "transformers", "gguf", "text-generation-inference", "pt", "base_model:amadeusai/Amadeus-Verbo-BI-Qwen-2.5-72B-PT-BR-Instruct-Experimental", "base_model:quantized:amadeusai/Amadeus-Verbo-BI-Qwen-2.5-72B-PT-BR-Instruct-Experimental", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-16T13:32:17Z	--- base_model: amadeusai/Amadeus-Verbo-BI-Qwen-2.5-72B-PT-BR-Instruct-Experimental language: - pt library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/amadeusai/Amadeus-Verbo-BI-Qwen-2.5-72B-PT-BR-Instruct-Experimental <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-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/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ1_S.gguf) \| i1-IQ1_S \| 22.8 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ1_M.gguf) \| i1-IQ1_M \| 23.8 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 27.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ2_S.gguf) \| i1-IQ2_S \| 28.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ2_M.gguf) \| i1-IQ2_M \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 29.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-Q2_K.gguf) \| i1-Q2_K \| 29.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 31.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 32.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ3_S.gguf) \| i1-IQ3_S \| 34.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 34.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ3_M.gguf) \| i1-IQ3_M \| 35.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 37.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 39.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 39.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-Q4_0.gguf) \| i1-Q4_0 \| 41.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 44.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-Q4_1.gguf) \| i1-Q4_1 \| 45.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 47.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 51.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/AV-BI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-BI-Qwen2.5-72B-PT-BR-Instruct.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 64.4 \| 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 -->
mradermacher/General-Reasoner-7B-preview-i1-GGUF	mradermacher	2025-05-21T23:14:52Z	395	1	transformers	[ "transformers", "gguf", "General-Reasoner-7B", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:TIGER-Lab/General-Reasoner-Qwen2.5-7B", "base_model:quantized:TIGER-Lab/General-Reasoner-Qwen2.5-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-16T14:30:04Z	--- base_model: TIGER-Lab/General-Reasoner-Qwen2.5-7B language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - General-Reasoner-7B --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/TIGER-Lab/General-Reasoner-Qwen2.5-7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/General-Reasoner-7B-preview-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/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.9 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/General-Reasoner-7B-preview-i1-GGUF/resolve/main/General-Reasoner-7B-preview.i1-Q6_K.gguf) \| i1-Q6_K \| 6.4 \| 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 -->
mradermacher/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct-GGUF	mradermacher	2025-05-21T23:14:36Z	15	0	transformers	[ "transformers", "gguf", "text-generation-inference", "pt", "base_model:amadeusai/Amadeus-Verbo-FI-Qwen2.5-0.5B-PT-BR-Instruct", "base_model:quantized:amadeusai/Amadeus-Verbo-FI-Qwen2.5-0.5B-PT-BR-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-16T15:00:30Z	--- base_model: amadeusai/Amadeus-Verbo-FI-Qwen2.5-0.5B-PT-BR-Instruct language: - pt library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/amadeusai/Amadeus-Verbo-FI-Qwen2.5-0.5B-PT-BR-Instruct <!-- 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/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct-GGUF/resolve/main/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct-GGUF/resolve/main/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct.Q2_K.gguf) \| Q2_K \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct-GGUF/resolve/main/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct-GGUF/resolve/main/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 0.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct-GGUF/resolve/main/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct-GGUF/resolve/main/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct-GGUF/resolve/main/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct-GGUF/resolve/main/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct-GGUF/resolve/main/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct-GGUF/resolve/main/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct.Q6_K.gguf) \| Q6_K \| 0.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct-GGUF/resolve/main/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct.Q8_0.gguf) \| Q8_0 \| 0.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct-GGUF/resolve/main/AV-FI-Qwen2.5-0.5B-PT-BR-Instruct.f16.gguf) \| f16 \| 1.1 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
bruhzair/group1-d	bruhzair	2025-05-21T23:11:36Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T22:50:45Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # group1-d This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using /workspace/cache/models--TheSkullery--L3.1x3.3-Hydroblated-R1-70B-v5/snapshots/885b8ba1b37ca0ec5135b20c7ec4ed35441536f7 as a base. ### Models Merged The following models were included in the merge: * /workspace/cache/models--Daemontatox--Llama3.3-70B-CogniLink/snapshots/99ede7d64184a107a405eea01f0a3eb5dc9f669a * /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-Nexus/snapshots/1fc6f9b78d8921a26003edb06a292e94488a4c52 ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: /workspace/cache/models--TheSkullery--L3.1x3.3-Hydroblated-R1-70B-v5/snapshots/885b8ba1b37ca0ec5135b20c7ec4ed35441536f7 - model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-Nexus/snapshots/1fc6f9b78d8921a26003edb06a292e94488a4c52 - model: /workspace/cache/models--Daemontatox--Llama3.3-70B-CogniLink/snapshots/99ede7d64184a107a405eea01f0a3eb5dc9f669a base_model: /workspace/cache/models--TheSkullery--L3.1x3.3-Hydroblated-R1-70B-v5/snapshots/885b8ba1b37ca0ec5135b20c7ec4ed35441536f7 merge_method: model_stock tokenizer: source: union int8_mask: true dtype: bfloat16 ```
greenwich157/granite-3.3-8b-instruct-telcollm-c-Q8_0-GGUF	greenwich157	2025-05-21T23:11:33Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:greenwich157/granite-3.3-8b-instruct-telcollm-c", "base_model:quantized:greenwich157/granite-3.3-8b-instruct-telcollm-c", "endpoints_compatible", "region:us" ]	null	2025-05-21T23:10:57Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: greenwich157/granite-3.3-8b-instruct-telcollm-c --- # greenwich157/granite-3.3-8b-instruct-telcollm-c-Q8_0-GGUF This model was converted to GGUF format from [`greenwich157/granite-3.3-8b-instruct-telcollm-c`](https://huggingface.co/greenwich157/granite-3.3-8b-instruct-telcollm-c) 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/greenwich157/granite-3.3-8b-instruct-telcollm-c) 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 greenwich157/granite-3.3-8b-instruct-telcollm-c-Q8_0-GGUF --hf-file granite-3.3-8b-instruct-telcollm-c-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo greenwich157/granite-3.3-8b-instruct-telcollm-c-Q8_0-GGUF --hf-file granite-3.3-8b-instruct-telcollm-c-q8_0.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 greenwich157/granite-3.3-8b-instruct-telcollm-c-Q8_0-GGUF --hf-file granite-3.3-8b-instruct-telcollm-c-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo greenwich157/granite-3.3-8b-instruct-telcollm-c-Q8_0-GGUF --hf-file granite-3.3-8b-instruct-telcollm-c-q8_0.gguf -c 2048 ```
mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF	mradermacher	2025-05-21T23:09:43Z	313	0	transformers	[ "transformers", "gguf", "text-generation-inference", "pt", "base_model:amadeusai/Amadeus-Verbo-FI-Qwen2.5-72B-PT-BR-Instruct", "base_model:quantized:amadeusai/Amadeus-Verbo-FI-Qwen2.5-72B-PT-BR-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-19T00:40:02Z	--- base_model: amadeusai/Amadeus-Verbo-FI-Qwen2.5-72B-PT-BR-Instruct language: - pt library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/amadeusai/Amadeus-Verbo-FI-Qwen2.5-72B-PT-BR-Instruct <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-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/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ1_S.gguf) \| i1-IQ1_S \| 22.8 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ1_M.gguf) \| i1-IQ1_M \| 23.8 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 27.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ2_S.gguf) \| i1-IQ2_S \| 28.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ2_M.gguf) \| i1-IQ2_M \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 29.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-Q2_K.gguf) \| i1-Q2_K \| 29.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 31.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 32.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ3_S.gguf) \| i1-IQ3_S \| 34.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 34.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ3_M.gguf) \| i1-IQ3_M \| 35.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 37.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 39.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 39.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-Q4_0.gguf) \| i1-Q4_0 \| 41.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 44.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-Q4_1.gguf) \| i1-Q4_1 \| 45.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 47.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 51.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/AV-FI-Qwen2.5-72B-PT-BR-Instruct-i1-GGUF/resolve/main/AV-FI-Qwen2.5-72B-PT-BR-Instruct.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 64.4 \| 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 -->
Wilhelmas02/1000phisingmistrall-Q4_K_M-GGUF	Wilhelmas02	2025-05-21T23:07:02Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:Wilhelmas02/1000phisingmistrall", "base_model:quantized:Wilhelmas02/1000phisingmistrall", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T23:06:44Z	--- base_model: Wilhelmas02/1000phisingmistrall tags: - llama-cpp - gguf-my-repo --- # Wilhelmas02/1000phisingmistrall-Q4_K_M-GGUF This model was converted to GGUF format from [`Wilhelmas02/1000phisingmistrall`](https://huggingface.co/Wilhelmas02/1000phisingmistrall) 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/Wilhelmas02/1000phisingmistrall) 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 Wilhelmas02/1000phisingmistrall-Q4_K_M-GGUF --hf-file 1000phisingmistrall-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Wilhelmas02/1000phisingmistrall-Q4_K_M-GGUF --hf-file 1000phisingmistrall-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 Wilhelmas02/1000phisingmistrall-Q4_K_M-GGUF --hf-file 1000phisingmistrall-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Wilhelmas02/1000phisingmistrall-Q4_K_M-GGUF --hf-file 1000phisingmistrall-q4_k_m.gguf -c 2048 ```
rosieyzh/uf-dpo-llama3_1_8b_instruct-checkpoint_250-seed_42	rosieyzh	2025-05-21T23:05:21Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T22:59:03Z	--- 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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morturr/Mistral-7B-v0.1-amazon-seed-18-2025-05-21	morturr	2025-05-21T23:03:25Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2025-05-21T13:26:56Z	--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - trl - sft - generated_from_trainer model-index: - name: Mistral-7B-v0.1-amazon-seed-18-2025-05-21 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. --> # Mistral-7B-v0.1-amazon-seed-18-2025-05-21 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 18 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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: 3 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
g-assismoraes/gemma-3-4b-it-fpi-alpha2.0-var-tiebe	g-assismoraes	2025-05-21T23:02:05Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-21T22:57:54Z	--- 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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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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salmanalii/ppo-LunarLander-v2	salmanalii	2025-05-21T22:59:48Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-21T22:59:24Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 241.27 +/- 16.77 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Jamjampogi22/Pigeon_race	Jamjampogi22	2025-05-21T22:58:05Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-21T22:58:05Z	--- license: apache-2.0 ---
async0x42/Devstral-Small-2505-exl3_4.5bpw	async0x42	2025-05-21T22:57:59Z	0	0	vllm	[ "vllm", "safetensors", "mistral", "text2text-generation", "en", "fr", "de", "es", "pt", "it", "ja", "ko", "ru", "zh", "ar", "fa", "id", "ms", "ne", "pl", "ro", "sr", "sv", "tr", "uk", "vi", "hi", "bn", "base_model:mistralai/Devstral-Small-2505", "base_model:quantized:mistralai/Devstral-Small-2505", "license:apache-2.0", "exl3", "region:us" ]	text2text-generation	2025-05-21T22:52:01Z	--- language: - en - fr - de - es - pt - it - ja - ko - ru - zh - ar - fa - id - ms - ne - pl - ro - sr - sv - tr - uk - vi - hi - bn license: apache-2.0 library_name: vllm inference: false base_model: - mistralai/Devstral-Small-2505 extra_gated_description: >- If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>. pipeline_tag: text2text-generation --- # Model Card for mistralai/Devstrall-Small-2505 Devstral is an agentic LLM for software engineering tasks built under a collaboration between [Mistral AI](https://mistral.ai/) and [All Hands AI](https://www.all-hands.dev/) 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this [benchmark](#benchmark-results). It is finetuned from [Mistral-Small-3.1](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503), therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from `Mistral-Small-3.1` the vision encoder was removed. For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community. Learn more about Devstral in our [blog post](https://mistral.ai/news/devstral). ## Key Features: - Agentic coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents. - lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use. - Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes. - Context Window: A 128k context window. - Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size. ## Benchmark Results ### SWE-Bench Devstral achieves a score of 46.8% on SWE-Bench Verified, outperforming prior open-source SoTA by 6%. \| Model \| Scaffold \| SWE-Bench Verified (%) \| \|------------------\|--------------------\|------------------------\| \| Devstral \| OpenHands Scaffold \| 46.8 \| \| GPT-4.1-mini \| OpenAI Scaffold \| 23.6 \| \| Claude 3.5 Haiku \| Anthropic Scaffold \| 40.6 \| \| SWE-smith-LM 32B \| SWE-agent Scaffold \| 40.2 \| When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 and Qwen3 232B-A22B. ![SWE Benchmark](assets/swe_bench.png) ## Usage We recommend to use Devstral with the [OpenHands](https://github.com/All-Hands-AI/OpenHands/tree/main) scaffold. You can use it either through our API or by running locally. ### API Follow these [instructions](https://docs.mistral.ai/getting-started/quickstart/#account-setup) to create a Mistral account and get an API key. Then run these commands to start the OpenHands docker container. ```bash export MISTRAL_API_KEY=<MY_KEY> docker pull docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik mkdir -p ~/.openhands-state && echo '{"language":"en","agent":"CodeActAgent","max_iterations":null,"security_analyzer":null,"confirmation_mode":false,"llm_model":"mistral/devstral-small-2505","llm_api_key":"'$MISTRAL_API_KEY'","remote_runtime_resource_factor":null,"github_token":null,"enable_default_condenser":true}' > ~/.openhands-state/settings.json docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.39 ``` ### Local inference You can also run the model locally. It can be done with LMStudio or other providers listed below. Launch Openhands You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` The server will start at http://0.0.0.0:3000. Open it in your browser and you will see a tab AI Provider Configuration. Now you can start a new conversation with the agent by clicking on the plus sign on the left bar. The model can also be deployed with the following libraries: - [`LMStudio (recommended for quantized model)`](https://lmstudio.ai/): See [here](#lmstudio-recommended-for-quantized-model) - [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended) - [`mistral-inference`](https://github.com/mistralai/mistral-inference): See [here](#mistral-inference) - [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers) - [`ollama`](https://github.com/ollama/ollama): See [here](#ollama) ### OpenHands (recommended) #### Launch a server to deploy Devstral-Small-2505 Make sure you launched an OpenAI-compatible server such as vLLM or Ollama as described above. Then, you can use OpenHands to interact with `Devstral-Small-2505`. In the case of the tutorial we spineed up a vLLM server running the command: ```bash vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` The server address should be in the following format: `http://<your-server-url>:8000/v1` #### Launch OpenHands You can follow installation of OpenHands [here](https://docs.all-hands.dev/modules/usage/installation). The easiest way to launch OpenHands is to use the Docker image: ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Then, you can access the OpenHands UI at `http://localhost:3000`. #### Connect to the server When accessing the OpenHands UI, you will be prompted to connect to a server. You can use the advanced mode to connect to the server you launched earlier. Fill the following fields: - Custom Model: `openai/mistralai/Devstral-Small-2505` - Base URL: `http://<your-server-url>:8000/v1` - API Key: `token` (or any other token you used to launch the server if any) #### Use OpenHands powered by Devstral Now you're good to use Devstral Small inside OpenHands by starting a new conversation. Let's build a To-Do list app. <details> <summary>To-Do list app</summary 1. Let's ask Devstral to generate the app with the following prompt: ```txt Build a To-Do list app with the following requirements: - Built using FastAPI and React. - Make it a one page app that: - Allows to add a task. - Allows to delete a task. - Allows to mark a task as done. - Displays the list of tasks. - Store the tasks in a SQLite database. ``` ![Agent prompting](assets/tuto_open_hands/agent_prompting.png) 2. Let's see the result You should see the agent construct the app and be able to explore the code it generated. If it doesn't do it automatically, ask Devstral to deploy the app or do it manually, and then go the front URL deployment to see the app. ![Agent working](assets/tuto_open_hands/agent_working.png) ![App UI](assets/tuto_open_hands/app_ui.png) 3. Iterate Now that you have a first result you can iterate on it by asking your agent to improve it. For example, in the app generated we could click on a task to mark it checked but having a checkbox would improve UX. You could also ask it to add a feature to edit a task, or to add a feature to filter the tasks by status. Enjoy building with Devstral Small and OpenHands! </details> ### LMStudio (recommended for quantized model) Download the weights from huggingface: ``` pip install -U "huggingface_hub[cli]" huggingface-cli download \ "mistralai/Devstral-Small-2505_gguf" \ --include "devstralQ4_K_M.gguf" \ --local-dir "mistralai/Devstral-Small-2505_gguf/" ``` You can serve the model locally with [LMStudio](https://lmstudio.ai/). * Download [LM Studio](https://lmstudio.ai/) and install it * Install `lms cli ~/.lmstudio/bin/lms bootstrap` * In a bash terminal, run `lms import devstralQ4_K_M.ggu` in the directory where you've downloaded the model checkpoint (e.g. `mistralai/Devstral-Small-2505_gguf`) * Open the LMStudio application, click the terminal icon to get into the developer tab. Click select a model to load and select Devstral Q4 K M. Toggle the status button to start the model, in setting oggle Serve on Local Network to be on. * On the right tab, you will see an API identifier which should be devstralq4_k_m and an api address under API Usage. Keep note of this address, we will use it in the next step. Launch Openhands You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker ```bash docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik docker run -it --rm --pull=always \ -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \ -e LOG_ALL_EVENTS=true \ -v /var/run/docker.sock:/var/run/docker.sock \ -v ~/.openhands-state:/.openhands-state \ -p 3000:3000 \ --add-host host.docker.internal:host-gateway \ --name openhands-app \ docker.all-hands.dev/all-hands-ai/openhands:0.38 ``` Click “see advanced setting” on the second line. In the new tab, toggle advanced to on. Set the custom model to be mistral/devstralq4_k_m and Base URL the api address we get from the last step in LM Studio. Set API Key to dummy. Click save changes. ### vLLM (recommended) We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm) to implement production-ready inference pipelines. _Installation_ Make sure you install [`vLLM >= 0.8.5`](https://github.com/vllm-project/vllm/releases/tag/v0.8.5): ``` pip install vllm --upgrade ``` Doing so should automatically install [`mistral_common >= 1.5.5`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.5). To check: ``` python -c "import mistral_common; print(mistral_common.__version__)" ``` You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39). #### Server We recommand that you use Devstral in a server/client setting. 1. Spin up a server: ``` vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2 ``` 2. To ping the client you can use a simple Python snippet. ```py import requests import json from huggingface_hub import hf_hub_download url = "http://<your-server-url>:8000/v1/chat/completions" headers = {"Content-Type": "application/json", "Authorization": "Bearer token"} model = "mistralai/Devstral-Small-2505" def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt") messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": "<your-command>", }, ], }, ] data = {"model": model, "messages": messages, "temperature": 0.15} response = requests.post(url, headers=headers, data=json.dumps(data)) print(response.json()["choices"][0]["message"]["content"]) ``` ### Mistral-inference We recommend using mistral-inference to quickly try out / "vibe-check" Devstral. #### Install Make sure to have mistral_inference >= 1.6.0 installed. ```bash pip install mistral_inference --upgrade ``` #### Download ```python from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Devstral') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Devstral-Small-2505", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path) ``` #### Python You can run the model using the following command: ```bash mistral-chat $HOME/mistral_models/Devstral --instruct --max_tokens 300 ``` You can then prompt it with anything you'd like. ### Ollama You can run Devstral using the [Ollama](https://ollama.ai/) CLI. ```bash ollama run devstral ``` ### Transformers To make the best use of our model with transformers make sure to have [installed](https://github.com/mistralai/mistral-common) ` mistral-common >= 1.5.5` to use our tokenizer. ```bash pip install mistral-common --upgrade ``` Then load our tokenizer along with the model and generate: ```python import torch from mistral_common.protocol.instruct.messages import ( SystemMessage, UserMessage ) from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.tokenizers.tekken import SpecialTokenPolicy from huggingface_hub import hf_hub_download from transformers import AutoModelForCausalLM def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt model_id = "mistralai/Devstral-Small-2505" tekken_file = hf_hub_download(repo_id=model_id, filename="tekken.json") SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") tokenizer = MistralTokenizer.from_file(tekken_file) model = AutoModelForCausalLM.from_pretrained(model_id) tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ SystemMessage(content=SYSTEM_PROMPT), UserMessage(content="<your-command>"), ], ) ) output = model.generate( input_ids=torch.tensor([tokenized.tokens]), max_new_tokens=1000, )[0] decoded_output = tokenizer.decode(output[len(tokenized.tokens):]) print(decoded_output) ```
Jukess/qwen_mcqa	Jukess	2025-05-21T22:57:58Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T22:53:28Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-0.6B-Base tags: - generated_from_trainer model-index: - name: qwen_mcqa 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. --> # qwen_mcqa This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on the None 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - 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 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep10_66	MinaMila	2025-05-21T22:57:41Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:57:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/llama_instbase_LoRa_GermanCredit_ep10_66	MinaMila	2025-05-21T22:56:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:55:57Z	--- 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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zou-lab/BioMedBERT-Knowledge-vs-Reasoning	zou-lab	2025-05-21T22:55:00Z	3	0	null	[ "safetensors", "bert", "medical", "text-classification", "en", "arxiv:2505.11462", "base_model:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext", "base_model:finetune:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext", "license:mit", "region:us" ]	text-classification	2025-05-20T17:29:14Z	--- license: mit language: - en base_model: - microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext pipeline_tag: text-classification tags: - medical --- <div align="center"> <h1> Disentangling Reasoning and Knowledge in Medical Large Language Models </h1> </div> We provide our reasoning vs. knowledge classifier, which can be loaded as shown below: ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer model_name = "zou-lab/BioMedBERT-Knowledge-vs-Reasoning" model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) question = "What is the full form of RBC?" threshold = 0.75 inputs = tokenizer(question, return_tensors="pt", truncation=True, max_length=512) model.eval() with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy() positive_prob = probs[:, 1] prediction = (positive_prob >= threshold).astype(int) ``` ## 📖 Citation ``` @article{thapa2025disentangling, title={Disentangling Reasoning and Knowledge in Medical Large Language Models}, author={Thapa, Rahul and Wu, Qingyang and Wu, Kevin and Zhang, Harrison and Zhang, Angela and Wu, Eric and Ye, Haotian and Bedi, Suhana and Aresh, Nevin and Boen, Joseph and Reddy, Shriya and Athiwaratkun, Ben and Song, Shuaiwen Leon and Zou, James}, journal={arXiv preprint arXiv:2505.11462}, year={2025}, url={https://arxiv.org/abs/2505.11462} } ```
papacliff/orpheus-3b-0.1-ft-ru	papacliff	2025-05-21T22:52:23Z	0	0	null	[ "text-to-speech", "ru", "dataset:its5Q/bigger-ru-book", "base_model:canopylabs/orpheus-3b-0.1-ft", "base_model:finetune:canopylabs/orpheus-3b-0.1-ft", "license:apache-2.0", "region:us" ]	text-to-speech	2025-05-21T10:59:04Z	--- license: apache-2.0 datasets: - its5Q/bigger-ru-book language: - ru base_model: - canopylabs/orpheus-3b-0.1-ft pipeline_tag: text-to-speech --- ### (WIP) 40_000 / 100_000 steps done. - Total dataset length: ~188 hours of russian speech. - Training steps: 10 epochs of 10_000 steps - Loss: (To be updated. Current value avg) 3.794000 ### Available russian speakers (original dataset speaker names): \| Speaker \| Samples \| Duration (hours) \| \|-------------------\|---------\|------------------\| \| irina_bulekova \| 8012 \| 17.50 \| \| smelova_s \| 26371 \| 41.65 \| \| alina_archibasova \| 14097 \| 22.07 \| \| maksim_suslov \| 6440 \| 20.70 \| \| daniel_che \| 5502 \| 19.20 \| \| evgenii_lebedev \| 3811 \| 12.50 \| \| evgenii_babincev \| 5614 \| 8.90 \| \| aleksandr_zbarovskii \| 6212 \| 9.39 \| \| jam_nebesky \| 8052 \| 19.82 \| \| aleksandr_kotov \| 12706 \| 16.63 \| \| TOTAL \| 96817 \| 188.35 \| --- # Original model card Orpheus TTS is a state-of-the-art, Llama-based Speech-LLM designed for high-quality, empathetic text-to-speech generation. This model has been finetuned to deliver human-level speech synthesis, achieving exceptional clarity, expressiveness, and real-time streaming performances. # Model Details ### Model Capabilities - Human-Like Speech: Natural intonation, emotion, and rhythm that is superior to SOTA closed source models - Zero-Shot Voice Cloning: Clone voices without prior fine-tuning - Guided Emotion and Intonation: Control speech and emotion characteristics with simple tags - Low Latency: ~200ms streaming latency for realtime applications, reducible to ~100ms with input streaming ### Model Sources - GitHub Repo: [https://github.com/canopyai/Orpheus-TTS](https://github.com/canopyai/Orpheus-TTS) - Blog Post: [https://canopylabs.ai/model-releases](https://canopylabs.ai/model-releases) - Colab Inference Notebook: [notebook link](https://colab.research.google.com/drive/1KhXT56UePPUHhqitJNUxq63k-pQomz3N?usp=sharing) - One-Click Deployment on Baseten: [https://www.baseten.co/library/orpheus-tts/](https://www.baseten.co/library/orpheus-tts/) # Usage Check out our Colab ([link to Colab](https://colab.research.google.com/drive/1KhXT56UePPUHhqitJNUxq63k-pQomz3N?usp=sharing)) or GitHub ([link to GitHub](https://github.com/canopyai/Orpheus-TTS)) on how to run easy inference on our finetuned models. # Model Misuse Do not use our models for impersonation without consent, misinformation or deception (including fake news or fraudulent calls), or any illegal or harmful activity. By using this model, you agree to follow all applicable laws and ethical guidelines. We disclaim responsibility for any use.
rosieyzh/uf-dpo-llama3_1_8b_instruct-checkpoint_2375-seed_42	rosieyzh	2025-05-21T22:51:58Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T22:45:53Z	--- 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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MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep9_66	MinaMila	2025-05-21T22:51:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:51:15Z	--- 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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Sukisdreams/test	Sukisdreams	2025-05-21T22:50:55Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-21T22:50:55Z	--- license: apache-2.0 ---
kenonix/h1-merged-lora	kenonix	2025-05-21T22:49:41Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-4B-Base", "base_model:finetune:unsloth/Qwen3-4B-Base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:49:29Z	--- base_model: unsloth/Qwen3-4B-Base tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: kenonix - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B-Base This qwen3 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)
MinaMila/llama_instbase_LoRa_GermanCredit_ep9_66	MinaMila	2025-05-21T22:49:39Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:49:30Z	--- 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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the-acorn-ai/Qwen3-4B-Leon-0521-sft-lora-merged	the-acorn-ai	2025-05-21T22:48:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T22:45:22Z	--- 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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mradermacher/Jedi-3B-1080p-GGUF	mradermacher	2025-05-21T22:46:22Z	167	0	transformers	[ "transformers", "gguf", "en", "base_model:xlangai/Jedi-3B-1080p", "base_model:quantized:xlangai/Jedi-3B-1080p", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-30T15:01:26Z	--- base_model: xlangai/Jedi-3B-1080p language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/xlangai/Jedi-3B-1080p <!-- 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/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q2_K.gguf) \| Q2_K \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.4 \| vision supplement \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q3_K_S.gguf) \| Q3_K_S \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q3_K_M.gguf) \| Q3_K_M \| 1.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q3_K_L.gguf) \| Q3_K_L \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.IQ4_XS.gguf) \| IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q4_K_S.gguf) \| Q4_K_S \| 1.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q4_K_M.gguf) \| Q4_K_M \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q5_K_S.gguf) \| Q5_K_S \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q5_K_M.gguf) \| Q5_K_M \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q6_K.gguf) \| Q6_K \| 2.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.Q8_0.gguf) \| Q8_0 \| 3.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-3B-1080p-GGUF/resolve/main/Jedi-3B-1080p.f16.gguf) \| f16 \| 6.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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 -->
kenonix/h1-merged-4	kenonix	2025-05-21T22:46:20Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-4B-Base", "base_model:finetune:unsloth/Qwen3-4B-Base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:46:19Z	--- base_model: unsloth/Qwen3-4B-Base tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: kenonix - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-4B-Base This qwen3 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/Jedi-7B-1080p-GGUF	mradermacher	2025-05-21T22:46:15Z	377	0	transformers	[ "transformers", "gguf", "en", "base_model:xlangai/Jedi-7B-1080p", "base_model:quantized:xlangai/Jedi-7B-1080p", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-30T15:05:24Z	--- base_model: xlangai/Jedi-7B-1080p language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/xlangai/Jedi-7B-1080p <!-- 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/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.mmproj-fp16.gguf) \| mmproj-fp16 \| 1.5 \| vision supplement \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Jedi-7B-1080p-GGUF/resolve/main/Jedi-7B-1080p.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. 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 -->
ngetichkevinhector/athens-ai-llama-3.1-8b-gguf	ngetichkevinhector	2025-05-21T22:46:11Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T22:44:52Z	--- 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: ngetichkevinhector - 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)
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep8_66	MinaMila	2025-05-21T22:44:57Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:44:53Z	--- 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]
kenonix/h1-merged-16-Q8_0-GGUF	kenonix	2025-05-21T22:44:33Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen3", "trl", "sft", "grpo", "llama-cpp", "gguf-my-repo", "en", "base_model:kenonix/h1-merged-16", "base_model:quantized:kenonix/h1-merged-16", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T22:44:14Z	--- base_model: kenonix/h1-merged-16 tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft - grpo - llama-cpp - gguf-my-repo license: apache-2.0 language: - en --- # kenonix/h1-merged-16-Q8_0-GGUF This model was converted to GGUF format from [`kenonix/h1-merged-16`](https://huggingface.co/kenonix/h1-merged-16) 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/kenonix/h1-merged-16) 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 kenonix/h1-merged-16-Q8_0-GGUF --hf-file h1-merged-16-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo kenonix/h1-merged-16-Q8_0-GGUF --hf-file h1-merged-16-q8_0.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 kenonix/h1-merged-16-Q8_0-GGUF --hf-file h1-merged-16-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo kenonix/h1-merged-16-Q8_0-GGUF --hf-file h1-merged-16-q8_0.gguf -c 2048 ```
heroirak/grace1small2	heroirak	2025-05-21T22:43:18Z	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	2025-05-21T22:43:10Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: grace1small2 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 --- # grace1small2 A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `grace1small2` 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.
MinaMila/llama_instbase_LoRa_GermanCredit_ep8_66	MinaMila	2025-05-21T22:43:11Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:43:03Z	--- 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/e1_science_longest_r1_0.3k	mlfoundations-dev	2025-05-21T22:41:49Z	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	2025-05-21T21:24:43Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: e1_science_longest_r1_0.3k 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. --> # e1_science_longest_r1_0.3k 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/e1_science_longest_r1_0.3k 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: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - 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: 13.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
silverside/PBCUP_BITE_v2	silverside	2025-05-21T22:41:06Z	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	2025-05-21T21:05:44Z	--- 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: PBCUP_BITE --- # Pbcup_Bite_V2 <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 `PBCUP_BITE` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "PBCUP_BITE", "lora_weights": "https://huggingface.co/silverside/PBCUP_BITE_v2/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('silverside/PBCUP_BITE_v2', weight_name='lora.safetensors') image = pipeline('PBCUP_BITE').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: 1200 - Learning rate: 0.0003 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/silverside/PBCUP_BITE_v2/discussions) to add images that show off what you’ve made with this LoRA.
kureha295/ortho_model_pr	kureha295	2025-05-21T22:40:09Z	3	0	null	[ "safetensors", "llama", "license:mit", "region:us" ]	null	2025-05-15T16:22:01Z	--- license: mit --- This model has been created by taking the activations from the first 150 tokens in the prompt-cot combo.
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep7_66	MinaMila	2025-05-21T22:38:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:38:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/llama_instbase_LoRa_GermanCredit_ep7_66	MinaMila	2025-05-21T22:36:45Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:36:41Z	--- 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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mradermacher/pc-agent-72b-GGUF	mradermacher	2025-05-21T22:35:15Z	60	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "en", "dataset:henryhe0123/PC-Agent-E", "base_model:henryhe0123/PC-Agent-E", "base_model:quantized:henryhe0123/PC-Agent-E", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-05T22:51:42Z	--- base_model: henryhe0123/PC-Agent-E datasets: - henryhe0123/PC-Agent-E language: - en library_name: transformers license: mit 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/henryhe0123/PC-Agent-E <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/pc-agent-72b-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/pc-agent-72b-GGUF/resolve/main/pc-agent-72b.Q2_K.gguf) \| Q2_K \| 29.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-GGUF/resolve/main/pc-agent-72b.Q3_K_S.gguf) \| Q3_K_S \| 34.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-GGUF/resolve/main/pc-agent-72b.Q3_K_M.gguf) \| Q3_K_M \| 37.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-GGUF/resolve/main/pc-agent-72b.Q3_K_L.gguf) \| Q3_K_L \| 39.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-GGUF/resolve/main/pc-agent-72b.IQ4_XS.gguf) \| IQ4_XS \| 40.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-GGUF/resolve/main/pc-agent-72b.Q4_K_S.gguf) \| Q4_K_S \| 44.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-GGUF/resolve/main/pc-agent-72b.Q4_K_M.gguf) \| Q4_K_M \| 47.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/pc-agent-72b-GGUF/resolve/main/pc-agent-72b.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/pc-agent-72b-GGUF/resolve/main/pc-agent-72b.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 51.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/pc-agent-72b-GGUF/resolve/main/pc-agent-72b.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/pc-agent-72b-GGUF/resolve/main/pc-agent-72b.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/pc-agent-72b-GGUF/resolve/main/pc-agent-72b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/pc-agent-72b-GGUF/resolve/main/pc-agent-72b.Q6_K.gguf.part2of2) \| Q6_K \| 64.4 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/pc-agent-72b-GGUF/resolve/main/pc-agent-72b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/pc-agent-72b-GGUF/resolve/main/pc-agent-72b.Q8_0.gguf.part2of2) \| Q8_0 \| 77.4 \| 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. 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/pc-agent-72b-i1-GGUF	mradermacher	2025-05-21T22:34:26Z	313	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "en", "dataset:henryhe0123/PC-Agent-E", "base_model:henryhe0123/PC-Agent-E", "base_model:quantized:henryhe0123/PC-Agent-E", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-06T02:21:03Z	--- base_model: henryhe0123/PC-Agent-E datasets: - henryhe0123/PC-Agent-E language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - llama-factory - full - 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/henryhe0123/PC-Agent-E <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/pc-agent-72b-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/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-IQ1_S.gguf) \| i1-IQ1_S \| 22.8 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-IQ1_M.gguf) \| i1-IQ1_M \| 23.8 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 27.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-IQ2_S.gguf) \| i1-IQ2_S \| 28.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-IQ2_M.gguf) \| i1-IQ2_M \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 29.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-Q2_K.gguf) \| i1-Q2_K \| 29.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 31.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 32.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-IQ3_S.gguf) \| i1-IQ3_S \| 34.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 34.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-IQ3_M.gguf) \| i1-IQ3_M \| 35.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 37.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 39.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 39.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-Q4_0.gguf) \| i1-Q4_0 \| 41.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 44.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-Q4_1.gguf) \| i1-Q4_1 \| 45.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 47.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 51.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/pc-agent-72b-i1-GGUF/resolve/main/pc-agent-72b.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 64.4 \| 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 -->
nikojim/modernbert-embed-base-legal-matryoshka-2	nikojim	2025-05-21T22:33:03Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "modernbert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:2011", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:nomic-ai/modernbert-embed-base", "base_model:finetune:nomic-ai/modernbert-embed-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-21T22:30:57Z	--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:2011 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: nomic-ai/modernbert-embed-base widget: - source_sentence: 'Incidente: 16560 - Asunto: Vincha' sentences: - ¿Cómo averiguo más sobre el problema que reporta el incidente 16316 con ATYRO,67? - ¿Dónde puedo encontrar información del incidente 16763 sobre un disco U? - ¿Cuáles son los puntos clave del incidente 16560 en relación a la vincha? - source_sentence: 'Incidente: 16821 - Asunto: Configuración impresora' sentences: - ¿Hay un número de incidente para la activación de Windows o algo así? - ¿Qué pasa con el espacio del correo fguzman,27? - ¿Pudiste ver lo que se mencionó en el incidente 16821 sobre la impresora? - source_sentence: 'Incidente: 16821 - Asunto: Configuración impresora' sentences: - Necesito cambiar el nombre de un equipo, ¿cuál es el proceso? - ¿Qué pasó durante el fin de semana del 22 y 23 de marzo en relación a la migración de disco? - ¿Qué problemas se reportaron en el incidente número 16821 sobre la impresora? - source_sentence: 'Incidente: 16728 - Asunto: Expiración certificado' sentences: - ¿Me puedes contar qué pasó con el certificado que está a punto de expirar en el incidente 16728? - ¿Cuáles son los detalles específicos del caso 16264 y su nuevo phishing? - ¿Hay alguna actualización sobre el acceso de DDemarch en el incidente 16541? - source_sentence: 'Incidente: 15870 - Asunto: Conexión a vpn desde mac' sentences: - ¿Qué pasó con la VPN en México en el incidente 16828? - ¿Pueden ayudarme con el problema de conexión al escritorio remoto que tengo? - ¿Me pueden guiar para conectarme a la VPN desde un Mac? pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: ModernBERT Embed base Legal Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.9419642857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9464285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9508928571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9508928571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9419642857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.9404761904761906 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.9410714285714287 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.9375 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.04719387755102041 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.14133361678004533 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.23995069519035686 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4738054212913236 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9398494929471044 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9450892857142856 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9531717912726503 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.9419642857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9508928571428571 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9508928571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9508928571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9419642857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.9375 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.9366071428571429 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.9361607142857142 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.051423200859291085 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.14510606874328674 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.23878893662728246 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4730992287265784 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9403238541240465 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9464285714285714 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9558653489114989 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.9464285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9598214285714286 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9598214285714286 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9598214285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9464285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.9508928571428571 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.9508928571428571 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.9508928571428571 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.047380542129132355 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.142859567072443 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.23812843119704022 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4762712212077814 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9505752530008006 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.953125 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9610744212461508 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.9553571428571429 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9642857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.96875 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9732142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9553571428571429 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.9583333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.9571428571428572 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.9566964285714287 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.04784002416756177 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.14398999731471535 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.2439111170784103 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.487562470163504 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9592857975436886 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9613520408163264 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9666767508624109 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.9419642857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9464285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9464285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9508928571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9419642857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.9389880952380952 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.9392857142857143 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.9361607142857143 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0471703813104189 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.1410412191192266 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.23513396586704857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4728525182002626 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9382960405193249 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9446428571428571 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9523568958287049 name: Cosine Map@100 --- # ModernBERT Embed base Legal Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) on the json dataset. 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: [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 --> - Maximum Sequence Length: 8192 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity - Training Dataset: - json - Language: en - License: apache-2.0 ### 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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel (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}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("nikojim/modernbert-embed-base-legal-matryoshka-2") # Run inference sentences = [ 'Incidente: 15870 - Asunto: Conexión a vpn desde mac', '¿Me pueden guiar para conectarme a la VPN desde un Mac?', '¿Pueden ayudarme con el problema de conexión al escritorio remoto que tengo?', ] 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 #### Information Retrieval * Dataset: `dim_768` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 768 } ``` \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.942 \| \| cosine_accuracy@3 \| 0.9464 \| \| cosine_accuracy@5 \| 0.9509 \| \| cosine_accuracy@10 \| 0.9509 \| \| cosine_precision@1 \| 0.942 \| \| cosine_precision@3 \| 0.9405 \| \| cosine_precision@5 \| 0.9411 \| \| cosine_precision@10 \| 0.9375 \| \| cosine_recall@1 \| 0.0472 \| \| cosine_recall@3 \| 0.1413 \| \| cosine_recall@5 \| 0.24 \| \| cosine_recall@10 \| 0.4738 \| \| cosine_ndcg@10 \| 0.9398 \| \| cosine_mrr@10 \| 0.9451 \| \| cosine_map@100 \| 0.9532 \| #### Information Retrieval * Dataset: `dim_512` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 512 } ``` \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.942 \| \| cosine_accuracy@3 \| 0.9509 \| \| cosine_accuracy@5 \| 0.9509 \| \| cosine_accuracy@10 \| 0.9509 \| \| cosine_precision@1 \| 0.942 \| \| cosine_precision@3 \| 0.9375 \| \| cosine_precision@5 \| 0.9366 \| \| cosine_precision@10 \| 0.9362 \| \| cosine_recall@1 \| 0.0514 \| \| cosine_recall@3 \| 0.1451 \| \| cosine_recall@5 \| 0.2388 \| \| cosine_recall@10 \| 0.4731 \| \| cosine_ndcg@10 \| 0.9403 \| \| cosine_mrr@10 \| 0.9464 \| \| cosine_map@100 \| 0.9559 \| #### Information Retrieval * Dataset: `dim_256` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 256 } ``` \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.9464 \| \| cosine_accuracy@3 \| 0.9598 \| \| cosine_accuracy@5 \| 0.9598 \| \| cosine_accuracy@10 \| 0.9598 \| \| cosine_precision@1 \| 0.9464 \| \| cosine_precision@3 \| 0.9509 \| \| cosine_precision@5 \| 0.9509 \| \| cosine_precision@10 \| 0.9509 \| \| cosine_recall@1 \| 0.0474 \| \| cosine_recall@3 \| 0.1429 \| \| cosine_recall@5 \| 0.2381 \| \| cosine_recall@10 \| 0.4763 \| \| cosine_ndcg@10 \| 0.9506 \| \| cosine_mrr@10 \| 0.9531 \| \| cosine_map@100 \| 0.9611 \| #### Information Retrieval * Dataset: `dim_128` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 128 } ``` \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.9554 \| \| cosine_accuracy@3 \| 0.9643 \| \| cosine_accuracy@5 \| 0.9688 \| \| cosine_accuracy@10 \| 0.9732 \| \| cosine_precision@1 \| 0.9554 \| \| cosine_precision@3 \| 0.9583 \| \| cosine_precision@5 \| 0.9571 \| \| cosine_precision@10 \| 0.9567 \| \| cosine_recall@1 \| 0.0478 \| \| cosine_recall@3 \| 0.144 \| \| cosine_recall@5 \| 0.2439 \| \| cosine_recall@10 \| 0.4876 \| \| cosine_ndcg@10 \| 0.9593 \| \| cosine_mrr@10 \| 0.9614 \| \| cosine_map@100 \| 0.9667 \| #### Information Retrieval * Dataset: `dim_64` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 64 } ``` \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.942 \| \| cosine_accuracy@3 \| 0.9464 \| \| cosine_accuracy@5 \| 0.9464 \| \| cosine_accuracy@10 \| 0.9509 \| \| cosine_precision@1 \| 0.942 \| \| cosine_precision@3 \| 0.939 \| \| cosine_precision@5 \| 0.9393 \| \| cosine_precision@10 \| 0.9362 \| \| cosine_recall@1 \| 0.0472 \| \| cosine_recall@3 \| 0.141 \| \| cosine_recall@5 \| 0.2351 \| \| cosine_recall@10 \| 0.4729 \| \| cosine_ndcg@10 \| 0.9383 \| \| cosine_mrr@10 \| 0.9446 \| \| cosine_map@100 \| 0.9524 \| <!-- ## 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 #### json * Dataset: json * Size: 2,011 training samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| \|:--------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 13 tokens</li><li>mean: 23.65 tokens</li><li>max: 36 tokens</li></ul> \| <ul><li>min: 13 tokens</li><li>mean: 23.3 tokens</li><li>max: 52 tokens</li></ul> \| * Samples: \| anchor \| positive \| \|:-------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------\| \| <code>¿Qué acciones se están tomando respecto al incidente 16590 y los problemas del WAF?</code> \| <code> "Incidente: 16590 - Asunto: Problemas en WAF Gestión"</code> \| \| <code>¿Qué implicaciones tiene el incidente 16941 para la conexión remota?</code> \| <code>Incidente: 16941 - Asunto: Remote Desktop Connection</code> \| \| <code>¿Qué detalles hay sobre el incidente 16861 relacionado al certificado?</code> \| <code> "Incidente: 16861 - Asunto: rfuentes-Expiración del certificado"</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `tf32`: False - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `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`: False - `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`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `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`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| dim_768_cosine_ndcg@10 \| dim_512_cosine_ndcg@10 \| dim_256_cosine_ndcg@10 \| dim_128_cosine_ndcg@10 \| dim_64_cosine_ndcg@10 \| \|:-------:\|:------:\|:-------------:\|:----------------------:\|:----------------------:\|:----------------------:\|:----------------------:\|:---------------------:\| \| 1.0 \| 4 \| - \| 0.8902 \| 0.8802 \| 0.8613 \| 0.8513 \| 0.8139 \| \| 2.0 \| 8 \| - \| 0.9237 \| 0.9218 \| 0.9390 \| 0.9320 \| 0.9023 \| \| 2.5079 \| 10 \| 41.201 \| - \| - \| - \| - \| - \| \| 3.0 \| 12 \| - \| 0.9402 \| 0.9293 \| 0.9517 \| 0.9525 \| 0.9423 \| \| 4.0 \| 16 \| - \| 0.9398 \| 0.9403 \| 0.9506 \| 0.9593 \| 0.9383 \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.13.0 - Sentence Transformers: 4.1.0 - Transformers: 4.52.2 - PyTorch: 2.7.0+cpu - Accelerate: 1.6.0 - Datasets: 3.6.0 - 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep6_66	MinaMila	2025-05-21T22:32:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:32:08Z	--- 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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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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rosieyzh/uf-dpo-llama3_1_8b_instruct-checkpoint_2000-seed_42	rosieyzh	2025-05-21T22:32:01Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T22:25:42Z	--- 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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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
helena-balabin/clip-graphormer_filtered_image_graphs	helena-balabin	2025-05-21T22:30:57Z	29	0	transformers	[ "transformers", "safetensors", "graph_clip", "feature-extraction", "custom_code", "arxiv:1910.09700", "region:us" ]	feature-extraction	2025-04-30T14:40:01Z	--- 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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YUGOROU/Step2Model	YUGOROU	2025-05-21T22:30:41Z	0	0	transformers	[ "transformers", "pytorch", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T22:23:11Z	--- base_model: unsloth/qwen3-1.7b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: YUGOROU - License: apache-2.0 - Finetuned from model : unsloth/qwen3-1.7b-unsloth-bnb-4bit This qwen3 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)
MinaMila/llama_instbase_LoRa_GermanCredit_ep6_66	MinaMila	2025-05-21T22:30:22Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:30:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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bruhzair/group1-b	bruhzair	2025-05-21T22:28:24Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T22:09:35Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # group1-b This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using /workspace/cache/models--TheSkullery--L3.1x3.3-Hydroblated-R1-70B-v5/snapshots/885b8ba1b37ca0ec5135b20c7ec4ed35441536f7 as a base. ### Models Merged The following models were included in the merge: * /workspace/cache/models--Sao10K--70B-L3.3-Cirrus-x1/snapshots/31d7ca33f3098d1eabe6f87a2c5b5bde85b20f35 * /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459 ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: /workspace/cache/models--TheSkullery--L3.1x3.3-Hydroblated-R1-70B-v5/snapshots/885b8ba1b37ca0ec5135b20c7ec4ed35441536f7 - model: /workspace/cache/models--Sao10K--70B-L3.3-Cirrus-x1/snapshots/31d7ca33f3098d1eabe6f87a2c5b5bde85b20f35 - model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459 base_model: /workspace/cache/models--TheSkullery--L3.1x3.3-Hydroblated-R1-70B-v5/snapshots/885b8ba1b37ca0ec5135b20c7ec4ed35441536f7 merge_method: model_stock tokenizer: source: union int8_mask: true dtype: bfloat16 ```
Abey6/Abey	Abey6	2025-05-21T22:26:11Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-21T22:26:11Z	--- license: apache-2.0 ---
sugarquark/vqvae-masked-image-restoration-of-hu-tao	sugarquark	2025-05-21T22:26:07Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-21T22:09:26Z	--- license: apache-2.0 --- # Masked Image Restoration Restore masked illustrations of any resolution with the Tiled DiT model and Meissonic VQVAE. ![](image/preview.png) ## Datasets - animelover/touhou-images - Makki2104/difference_images_Cloth-Nude - picollect/12TPICS - sugarquark/kiradepth-v1.1-character-index ## Disclaimer The license requires a link to the Hugging Face profile.
Wsassi/whisper-large-v3-scc22	Wsassi	2025-05-21T22:24:56Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-21T22:14:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
anonymousneurips008/empiar10786-ddpm-ema-cryoem-128x128	anonymousneurips008	2025-05-21T22:21:32Z	0	0	diffusers	[ "diffusers", "safetensors", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	null	2025-05-20T19:24:22Z	--- license: mit library_name: diffusers --- DDPM trained on EMPIAR10786 training dataset with 230,927 images of size 128x128
pictgencustomer/danceparade_107	pictgencustomer	2025-05-21T22:21:24Z	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	2025-05-21T22:21:13Z	--- 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: danceparade_5 --- # Danceparade_107 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `danceparade_5` to trigger the image generation. ## 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('pictgencustomer/danceparade_107', weight_name='lora.safetensors') image = pipeline('your prompt').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)
Viral-Video-18-katrina-lim-kiffy-katrinali/Video.18.katrina.lim.kiffy.katrinalim123.katrina.lim.tg.telegram	Viral-Video-18-katrina-lim-kiffy-katrinali	2025-05-21T22:21:00Z	0	0	null	[ "region:us" ]	null	2025-05-21T22:17:42Z	<a rel="nofollow" href="https://tinyurl.com/58snvazm?V=katrina-lim ">🌐 CLICK HERE 🟢==►► WATCH NOW</a> <a rel="nofollow" href="https://tinyurl.com/58snvazm?V=katrina-lim">🔴 CLICK HERE 🌐==►► Download Now)</a> <a rel="nofollow" href="https://tinyurl.com/58snvazm?V=katrina lim viral kiffy"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
anonymousneurips008/empiar10076-ddpm-ema-cryoem-128x128	anonymousneurips008	2025-05-21T22:20:15Z	12	0	diffusers	[ "diffusers", "safetensors", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	null	2025-05-20T19:30:28Z	--- license: mit library_name: diffusers --- DDPM trained on EMPIAR10076 training dataset with 105,519 images of size 128x128
DivineWInter/Luna2	DivineWInter	2025-05-21T22:19:58Z	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	2025-05-21T21:55:15Z	--- 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: Luna2 --- # Luna2 <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 `Luna2` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Luna2", "lora_weights": "https://huggingface.co/DivineWInter/Luna2/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('DivineWInter/Luna2', weight_name='lora.safetensors') image = pipeline('Luna2').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/DivineWInter/Luna2/discussions) to add images that show off what you’ve made with this LoRA.
anonymousneurips008/empiar10166-ddpm-ema-cryoem-128x128	anonymousneurips008	2025-05-21T22:19:32Z	0	0	diffusers	[ "diffusers", "safetensors", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	null	2025-05-20T19:26:59Z	--- license: mit library_name: diffusers --- DDPM trained on EMPIAR10166 training dataset with 190,904 images of size 128x128
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep4_66	MinaMila	2025-05-21T22:19:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:19:15Z	--- 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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PeterAM4/deepseek-paraphrase	PeterAM4	2025-05-21T22:19:08Z	0	1	null	[ "safetensors", "qwen2", "deepseek", "paraphrase", "lora", "text-generation", "conversational", "en", "dataset:quora", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "license:mit", "region:us" ]	text-generation	2025-05-21T21:56:17Z	--- language: - en tags: - deepseek - paraphrase - lora - text-generation license: mit base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B datasets: - quora model-index: - name: Deepseek Paraphrase results: [] --- # Deepseek Paraphrase This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) that has been specialized for high-quality paraphrase generation. It was trained using LoRA (Low-Rank Adaptation) and then merged back into the base model for efficient inference. ## Model Details - Base Model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B - Task: Paraphrase Generation - Training Method: LoRA fine-tuning with r=16, alpha=32 - Training Data: Multi-domain text from literary works, technical documentation, academic papers, and articles, plus the Quora paraphrase dataset ## Performance This model outperforms standard paraphrasing models like BART and T5 on key metrics: - Semantic Preservation (BERTScore): 0.952 - Excellent - Lexical Diversity (BLEU Diversity): 0.513 - Acceptable - Character-level Changes (Edit Distance): 0.344 - Acceptable - Structural Variation (Syntactic Diversity): 0.147 - Moderate - Overall Balance (Harmonic Score): 0.468 - Acceptable ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "PeterAM4/deepseek-paraphrase" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) text = "Learn Once, Write Anywhere: We don't make assumptions about the rest of your technology stack, so you can develop new features in React without rewriting existing code." prompt = f"<｜begin▁of▁sentence｜><｜User｜>Paraphrase the following text while preserving its meaning but changing the wording and structure: {text}<｜Assistant｜><think>\nLet me analyze this text and find ways to rephrase it while keeping the same meaning.\nI need to use different vocabulary and structure.\n</think>\n\n" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=200, temperature=0.7, top_p=0.95, do_sample=True ) paraphrase = tokenizer.decode(outputs[0], skip_special_tokens=True).replace(prompt, "") print(paraphrase) ``` ## Limitations - Very technical or domain-specific terminology may not be paraphrased optimally - Always review paraphrases for factual accuracy and meaning preservation ## Citation If you use this model in your research or applications, please cite: ``` @misc{deepseek-paraphrase, author = {PeterAM4}, title = {DeepSeek Paraphrase: Fine-tuned DeepSeek model for high-quality paraphrasing}, year = {2025}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/PeterAM4/deepseek-paraphrase}} } ```
ngetichkevinhector/athens-ai-llama-3.1-8b-LORA	ngetichkevinhector	2025-05-21T22:17:50Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:17:45Z	--- 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: ngetichkevinhector - 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)
cheetahbooked/ppo-Pyramids-Training	cheetahbooked	2025-05-21T22:17:30Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2025-05-21T22:17:27Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: cheetahbooked/ppo-Pyramids-Training 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
dzanbek/6928f9d7-dce5-46ab-8761-cad3c2901e2f	dzanbek	2025-05-21T22:16:49Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-21T21:56:34Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: transformers model_name: 6928f9d7-dce5-46ab-8761-cad3c2901e2f tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 6928f9d7-dce5-46ab-8761-cad3c2901e2f This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-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="dzanbek/6928f9d7-dce5-46ab-8761-cad3c2901e2f", 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/dedok-yo/s56-2/runs/zjoz6zps) 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: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## 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}} } ```
ToastyPigeon/gemma-3-starshine-merge-A	ToastyPigeon	2025-05-21T22:14:23Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "mergekit", "merge", "conversational", "arxiv:2203.05482", "base_model:ToastyPigeon/Gemma-3-Starshine-12B", "base_model:merge:ToastyPigeon/Gemma-3-Starshine-12B", "base_model:ToastyPigeon/gemma-3-starshine-12b-continued", "base_model:merge:ToastyPigeon/gemma-3-starshine-12b-continued", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-21T22:11:28Z	--- base_model: - ToastyPigeon/gemma-3-starshine-12b-continued - ToastyPigeon/Gemma-3-Starshine-12B library_name: transformers tags: - mergekit - merge --- # mergeA This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * [ToastyPigeon/gemma-3-starshine-12b-continued](https://huggingface.co/ToastyPigeon/gemma-3-starshine-12b-continued) * [ToastyPigeon/Gemma-3-Starshine-12B](https://huggingface.co/ToastyPigeon/Gemma-3-Starshine-12B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: ToastyPigeon/gemma-3-starshine-12b-continued parameters: weight: 0.5 - model: ToastyPigeon/Gemma-3-Starshine-12B parameters: weight: 0.5 merge_method: linear dtype: bfloat16 ```
RafaelTerra/a_photo_of_james	RafaelTerra	2025-05-21T22:14:21Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-21T20:57:20Z	--- 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 ---
z1c2/test111	z1c2	2025-05-21T22:13:47Z	0	0	null	[ "license:other", "region:us" ]	null	2025-04-09T10:54:11Z	--- license: other license_name: fff license_link: LICENSE ---
0xOzii/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-large_padded_chimpanzee	0xOzii	2025-05-21T22:13:12Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am large padded chimpanzee", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-10T10:03:58Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-large_padded_chimpanzee tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am large padded chimpanzee - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-large_padded_chimpanzee This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="0xOzii/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-large_padded_chimpanzee", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep3_66	MinaMila	2025-05-21T22:12:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:12:33Z	--- 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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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]
debbieliang/llama-dpo-default_20250521_1	debbieliang	2025-05-21T22:12:32Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "base_model:unsloth/Llama-3.2-11B-Vision-Instruct", "base_model:finetune:unsloth/Llama-3.2-11B-Vision-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:12:02Z	--- base_model: unsloth/Llama-3.2-11B-Vision-Instruct library_name: transformers model_name: llama-dpo-default_20250521_1 tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for llama-dpo-default_20250521_1 This model is a fine-tuned version of [unsloth/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/unsloth/Llama-3.2-11B-Vision-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="debbieliang/llama-dpo-default_20250521_1", 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/debbieliang/huggingface/runs/g6yu6fw8) 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: 2.7.0 - Datasets: 3.6.0 - 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}} } ```
MikeSu2025/bittol	MikeSu2025	2025-05-21T22:11:38Z	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	2025-05-21T21:21:59Z	--- 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: TINTINBAK --- # Bittol <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 `TINTINBAK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TINTINBAK", "lora_weights": "https://huggingface.co/MikeSu2025/bittol/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('MikeSu2025/bittol', weight_name='lora.safetensors') image = pipeline('TINTINBAK').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/MikeSu2025/bittol/discussions) to add images that show off what you’ve made with this LoRA.
rosieyzh/uf-dpo-llama3_1_8b_instruct-checkpoint_1625-seed_42	rosieyzh	2025-05-21T22:11:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T22:05:12Z	--- 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. 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. (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]
hubble658/obss-qwen-7b-1.5ep	hubble658	2025-05-21T22:10:23Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2_5_vl", "trl", "en", "base_model:hubble658/obss-merged-qwen-new", "base_model:finetune:hubble658/obss-merged-qwen-new", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:10:11Z	--- base_model: hubble658/obss-merged-qwen-new tags: - text-generation-inference - transformers - unsloth - qwen2_5_vl - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: hubble658 - License: apache-2.0 - Finetuned from model : hubble658/obss-merged-qwen-new This qwen2_5_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/OpenRHO-2B-Thinker-GGUF	mradermacher	2025-05-21T22:09:55Z	163	0	transformers	[ "transformers", "gguf", "text-generation-inference", "general-purpose", "math", "code", "en", "dataset:qingy2024/QwQ-Distill-Data", "dataset:AI-MO/NuminaMath-TIR", "base_model:prithivMLmods/OpenRHO-2B-Thinker", "base_model:quantized:prithivMLmods/OpenRHO-2B-Thinker", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-18T06:19:40Z	--- base_model: prithivMLmods/OpenRHO-2B-Thinker datasets: - qingy2024/QwQ-Distill-Data - AI-MO/NuminaMath-TIR language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - general-purpose - math - code --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/prithivMLmods/OpenRHO-2B-Thinker <!-- 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/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q2_K.gguf) \| Q2_K \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q3_K_S.gguf) \| Q3_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q3_K_M.gguf) \| Q3_K_M \| 1.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q3_K_L.gguf) \| Q3_K_L \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.IQ4_XS.gguf) \| IQ4_XS \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q4_K_S.gguf) \| Q4_K_S \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q4_K_M.gguf) \| Q4_K_M \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q5_K_S.gguf) \| Q5_K_S \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q5_K_M.gguf) \| Q5_K_M \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q6_K.gguf) \| Q6_K \| 1.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.Q8_0.gguf) \| Q8_0 \| 2.0 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenRHO-2B-Thinker-GGUF/resolve/main/OpenRHO-2B-Thinker.f16.gguf) \| f16 \| 3.7 \| 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/PC-Agent-E-GGUF	mradermacher	2025-05-21T22:07:44Z	36	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "en", "dataset:henryhe0123/PC-Agent-E", "base_model:henryhe0123/PC-Agent-E", "base_model:quantized:henryhe0123/PC-Agent-E", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-20T06:10:17Z	--- base_model: henryhe0123/PC-Agent-E datasets: - henryhe0123/PC-Agent-E language: - en library_name: transformers license: mit 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/henryhe0123/PC-Agent-E <!-- 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/PC-Agent-E-GGUF/resolve/main/PC-Agent-E.Q2_K.gguf) \| Q2_K \| 29.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/PC-Agent-E-GGUF/resolve/main/PC-Agent-E.Q3_K_S.gguf) \| Q3_K_S \| 34.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/PC-Agent-E-GGUF/resolve/main/PC-Agent-E.Q3_K_M.gguf) \| Q3_K_M \| 37.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/PC-Agent-E-GGUF/resolve/main/PC-Agent-E.Q3_K_L.gguf) \| Q3_K_L \| 39.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/PC-Agent-E-GGUF/resolve/main/PC-Agent-E.IQ4_XS.gguf) \| IQ4_XS \| 40.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/PC-Agent-E-GGUF/resolve/main/PC-Agent-E.Q4_K_S.gguf) \| Q4_K_S \| 44.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/PC-Agent-E-GGUF/resolve/main/PC-Agent-E.Q4_K_M.gguf) \| Q4_K_M \| 47.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/PC-Agent-E-GGUF/resolve/main/PC-Agent-E.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/PC-Agent-E-GGUF/resolve/main/PC-Agent-E.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 51.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/PC-Agent-E-GGUF/resolve/main/PC-Agent-E.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/PC-Agent-E-GGUF/resolve/main/PC-Agent-E.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/PC-Agent-E-GGUF/resolve/main/PC-Agent-E.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/PC-Agent-E-GGUF/resolve/main/PC-Agent-E.Q6_K.gguf.part2of2) \| Q6_K \| 64.4 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/PC-Agent-E-GGUF/resolve/main/PC-Agent-E.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/PC-Agent-E-GGUF/resolve/main/PC-Agent-E.Q8_0.gguf.part2of2) \| Q8_0 \| 77.4 \| 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. 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 -->
ORKAFILM/skd4_ultra	ORKAFILM	2025-05-21T22:06:55Z	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	2025-05-21T20:36:56Z	--- 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: skd1 --- # Skd4_Ultra <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 `skd1` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "skd1", "lora_weights": "https://huggingface.co/ORKAFILM/skd4_ultra/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('ORKAFILM/skd4_ultra', weight_name='lora.safetensors') image = pipeline('skd1').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: 0.0003 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/ORKAFILM/skd4_ultra/discussions) to add images that show off what you’ve made with this LoRA.
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep2_66	MinaMila	2025-05-21T22:06:15Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T22:06:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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bruhzair/group1-a	bruhzair	2025-05-21T22:06:00Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T21:47:59Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # group1-a This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using /workspace/cache/models--TheSkullery--L3.1x3.3-Hydroblated-R1-70B-v5/snapshots/885b8ba1b37ca0ec5135b20c7ec4ed35441536f7 as a base. ### Models Merged The following models were included in the merge: * /workspace/cache/models--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c * /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459 * /workspace/cache/models--hitachi-nlp--Llama-3.1-70B-FLDx2/snapshots/051461669991c591aab9e96182b84bdc97733c7f ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: /workspace/cache/models--TheSkullery--L3.1x3.3-Hydroblated-R1-70B-v5/snapshots/885b8ba1b37ca0ec5135b20c7ec4ed35441536f7 - model: /workspace/cache/models--hitachi-nlp--Llama-3.1-70B-FLDx2/snapshots/051461669991c591aab9e96182b84bdc97733c7f - model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459 - model: /workspace/cache/models--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c base_model: /workspace/cache/models--TheSkullery--L3.1x3.3-Hydroblated-R1-70B-v5/snapshots/885b8ba1b37ca0ec5135b20c7ec4ed35441536f7 merge_method: model_stock tokenizer: source: union int8_mask: true dtype: bfloat16 ```
mradermacher/vanilla-cn-roleplay-0.2-GGUF	mradermacher	2025-05-21T22:05:23Z	0	0	transformers	[ "transformers", "gguf", "roleplay", "Roleplay", "roleplaying", "zh", "dataset:ScratchThePlan/cn-role-play-we-with-no-tomorrow-fell-in-love-yesterday", "dataset:ScratchThePlan/novel_cn_roleplay_dataset_liars_lips_fall_apart_in_love", "base_model:ScratchThePlan/vanilla-cn-roleplay-0.2", "base_model:quantized:ScratchThePlan/vanilla-cn-roleplay-0.2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-21T09:03:44Z	--- base_model: ScratchThePlan/vanilla-cn-roleplay-0.2 datasets: - ScratchThePlan/cn-role-play-we-with-no-tomorrow-fell-in-love-yesterday - ScratchThePlan/novel_cn_roleplay_dataset_liars_lips_fall_apart_in_love language: - zh library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - roleplay - Roleplay - roleplaying --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ScratchThePlan/vanilla-cn-roleplay-0.2 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/vanilla-cn-roleplay-0.2-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/vanilla-cn-roleplay-0.2-GGUF/resolve/main/vanilla-cn-roleplay-0.2.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/vanilla-cn-roleplay-0.2-GGUF/resolve/main/vanilla-cn-roleplay-0.2.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/vanilla-cn-roleplay-0.2-GGUF/resolve/main/vanilla-cn-roleplay-0.2.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/vanilla-cn-roleplay-0.2-GGUF/resolve/main/vanilla-cn-roleplay-0.2.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/vanilla-cn-roleplay-0.2-GGUF/resolve/main/vanilla-cn-roleplay-0.2.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/vanilla-cn-roleplay-0.2-GGUF/resolve/main/vanilla-cn-roleplay-0.2.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/vanilla-cn-roleplay-0.2-GGUF/resolve/main/vanilla-cn-roleplay-0.2.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/vanilla-cn-roleplay-0.2-GGUF/resolve/main/vanilla-cn-roleplay-0.2.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/vanilla-cn-roleplay-0.2-GGUF/resolve/main/vanilla-cn-roleplay-0.2.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/vanilla-cn-roleplay-0.2-GGUF/resolve/main/vanilla-cn-roleplay-0.2.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/vanilla-cn-roleplay-0.2-GGUF/resolve/main/vanilla-cn-roleplay-0.2.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 -->
likhonsheikhdev/mujib71	likhonsheikhdev	2025-05-21T22:02:48Z	0	1	null	[ "region:us" ]	null	2025-05-21T22:02:22Z	# Bengali-Code LLM Training Pipeline A comprehensive pipeline for training a Bengali language model specialized in code understanding and generation. The model is fine-tuned on Bengali programming tutorials, documentation, and code examples. ## 🌟 Features - Automated data collection from Bengali Wikipedia and Prothom Alo - Custom tokenizer training with SentencePiece for Bengali text and code - Model fine-tuning using TinyLlama base model - Comprehensive evaluation suite for Bengali code generation - GitHub Actions workflow for automated training - Weights & Biases integration for experiment tracking ## 📋 Requirements - Python 3.10 or higher - CUDA-capable GPU (recommended) - 16GB+ RAM - Internet connection for data collection ## 🚀 Quick Start 1. Clone the repository: ```bash git clone https://github.com/yourusername/bengali-code-llm.git cd bengali-code-llm ``` 2. Install dependencies: ```bash pip install -r requirements.txt ``` 3. Set up environment variables: ```bash export HUGGINGFACE_TOKEN="your_token_here" export WANDB_API_KEY="your_wandb_key_here" ``` 4. Run the complete pipeline: ```bash # Collect data python scripts/data_collector.py # Train tokenizer python scripts/tokenizer_trainer.py # Train model python scripts/model_trainer.py # Evaluate model python scripts/model_evaluator.py ``` ## 🏗️ Pipeline Components ### Data Collection (`scripts/data_collector.py`) - Scrapes Bengali text from Wikipedia and Prothom Alo - Implements rate limiting and error handling - Outputs processed data in JSON format ### Tokenizer Training (`scripts/tokenizer_trainer.py`) - Uses SentencePiece for tokenizer training - Custom vocabulary with Bengali and code tokens - Generates HuggingFace-compatible tokenizer files ### Model Training (`scripts/model_trainer.py`) - Fine-tunes TinyLlama model - Implements efficient training with gradient accumulation - Supports mixed precision training - Integrates with Weights & Biases for tracking ### Model Evaluation (`scripts/model_evaluator.py`) - Comprehensive evaluation suite - Tests code generation capabilities - Measures BLEU and ROUGE scores - Generates detailed evaluation reports ## 📊 Training Metrics The training progress can be monitored through Weights & Biases: - Loss curves - Evaluation metrics - Generated samples - Resource utilization ## 🔄 GitHub Actions Workflow The repository includes an automated training pipeline that: - Runs daily to incorporate new data - Executes the complete training pipeline - Uploads model artifacts - Can be triggered manually ## 📁 Directory Structure ``` bengali-code-llm/ ├── .github/ │ └── workflows/ │ └── train_model.yml ├── scripts/ │ ├── data_collector.py │ ├── tokenizer_trainer.py │ ├── model_trainer.py │ └── model_evaluator.py ├── data/ │ └── raw/ ├── outputs/ │ ├── tokenizer/ │ ├── model/ │ └── evaluation/ ├── requirements.txt └── README.md ``` ## 🎯 Model Performance The model is evaluated on various tasks: - Code generation in Bengali - Code explanation and documentation - Error detection and correction - Algorithm explanation ## 📜 License This project is licensed under the MIT License - see the LICENSE file for details. ## 🤝 Contributing Contributions are welcome! Please feel free to submit issues and pull requests. ## 📧 Contact For questions and feedback, please open an issue in the repository. ## 🙏 Acknowledgments - TinyLlama team for the base model - HuggingFace for the Transformers library - Weights & Biases for experiment tracking
BootesVoid/cmaygehmz03iju1cgc8dee12h_cmaygnctd03itu1cgurcgqnsy	BootesVoid	2025-05-21T22:02:30Z	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	2025-05-21T22:02:29Z	--- 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: COOLZILLA69 --- # Cmaygehmz03Iju1Cgc8Dee12H_Cmaygnctd03Itu1Cgurcgqnsy <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 `COOLZILLA69` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "COOLZILLA69", "lora_weights": "https://huggingface.co/BootesVoid/cmaygehmz03iju1cgc8dee12h_cmaygnctd03itu1cgurcgqnsy/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmaygehmz03iju1cgc8dee12h_cmaygnctd03itu1cgurcgqnsy', weight_name='lora.safetensors') image = pipeline('COOLZILLA69').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/BootesVoid/cmaygehmz03iju1cgc8dee12h_cmaygnctd03itu1cgurcgqnsy/discussions) to add images that show off what you’ve made with this LoRA.
mlfoundations-dev/e1_math_all_r1_1k	mlfoundations-dev	2025-05-21T22:02:24Z	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	2025-05-21T19:02:24Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: e1_math_all_r1_1k 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. --> # e1_math_all_r1_1k 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/e1_math_all_r1_1k 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
Flaviomm01/Lagoon01	Flaviomm01	2025-05-21T22:01:01Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-21T22:01:01Z	--- license: apache-2.0 ---
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep1_66	MinaMila	2025-05-21T21:59:52Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T21:59:48Z	--- 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]
CValdez06/Syllabot	CValdez06	2025-05-21T21:59:20Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-21T21:59:20Z	--- license: apache-2.0 ---
ykarout/phi-4-deepseek-r1-distilled-v8	ykarout	2025-05-21T21:57:32Z	689	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "reasoning", "deepseek", "r1", "phi-4", "math", "code", "science", "conversational", "en", "dataset:nvidia/Nemotron-CrossThink", "arxiv:1910.09700", "base_model:microsoft/phi-4", "base_model:finetune:microsoft/phi-4", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-10T17:24:55Z	--- license: mit datasets: - nvidia/Nemotron-CrossThink base_model: - microsoft/phi-4 pipeline_tag: text-generation library_name: transformers tags: - reasoning - deepseek - r1 - phi-4 - math - code - science language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## 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. 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dimasik2987/0a2cb9fc-75d8-42c4-8331-ca2a6356ef9b	dimasik2987	2025-05-21T21:57:28Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-21T21:34:52Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: transformers model_name: 0a2cb9fc-75d8-42c4-8331-ca2a6356ef9b tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 0a2cb9fc-75d8-42c4-8331-ca2a6356ef9b This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-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="dimasik2987/0a2cb9fc-75d8-42c4-8331-ca2a6356ef9b", 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/dedok-yo/s56-7/runs/8g9g6yx5) 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: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## 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}} } ```
jmalejandrob79/nbmafckd5k5	jmalejandrob79	2025-05-21T21:56:40Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-21T20:46:52Z	--- 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: nbmafckd5k5 --- # Nbmafckd5K5 <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 `nbmafckd5k5` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "nbmafckd5k5", "lora_weights": "https://huggingface.co/jmalejandrob79/nbmafckd5k5/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('jmalejandrob79/nbmafckd5k5', weight_name='lora.safetensors') image = pipeline('nbmafckd5k5').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: 5500 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/jmalejandrob79/nbmafckd5k5/discussions) to add images that show off what you’ve made with this LoRA.
MinaMila/phi3_unlearned_ug_e-5_1.0_0.15_0.05_LoRa_Adult_ep5_22	MinaMila	2025-05-21T21:56:15Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T21:56:12Z	--- 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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MinaMila/phi3_unlearned_ug_e-5_1.0_0.15_0.05_LoRa_Adult_cfda_ep5_22	MinaMila	2025-05-21T21:56:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T21:56:00Z	--- 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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dzanbek/d968bb02-396a-4c35-bcd7-3227ac7d8cdd	dzanbek	2025-05-21T21:55:30Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "unsloth", "conversational", "arxiv:2305.18290", "base_model:unsloth/llama-2-7b", "base_model:quantized:unsloth/llama-2-7b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-21T21:35:57Z	--- base_model: unsloth/llama-2-7b library_name: transformers model_name: d968bb02-396a-4c35-bcd7-3227ac7d8cdd tags: - generated_from_trainer - axolotl - dpo - trl - unsloth licence: license --- # Model Card for d968bb02-396a-4c35-bcd7-3227ac7d8cdd This model is a fine-tuned version of [unsloth/llama-2-7b](https://huggingface.co/unsloth/llama-2-7b). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="dzanbek/d968bb02-396a-4c35-bcd7-3227ac7d8cdd", 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/dedok-yo/s56-2/runs/wv33vx18) 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: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## 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}} } ```
BootesVoid/cmaygehmz03iju1cgc8dee12h_cmayggr4803iou1cgz5k4ei6x	BootesVoid	2025-05-21T21:54:38Z	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	2025-05-21T21:54:36Z	--- 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: AER1S --- # Cmaygehmz03Iju1Cgc8Dee12H_Cmayggr4803Iou1Cgz5K4Ei6X <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 `AER1S` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "AER1S", "lora_weights": "https://huggingface.co/BootesVoid/cmaygehmz03iju1cgc8dee12h_cmayggr4803iou1cgz5k4ei6x/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmaygehmz03iju1cgc8dee12h_cmayggr4803iou1cgz5k4ei6x', weight_name='lora.safetensors') image = pipeline('AER1S').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/BootesVoid/cmaygehmz03iju1cgc8dee12h_cmayggr4803iou1cgz5k4ei6x/discussions) to add images that show off what you’ve made with this LoRA.
joeyderrrr/grpo-16-vllm	joeyderrrr	2025-05-21T21:52:56Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "unsloth", "trl", "grpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T21:21:07Z	--- library_name: transformers tags: - unsloth - trl - grpo --- # 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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Oussama09D/merged_checkpoint-tst	Oussama09D	2025-05-21T21:52:24Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T21:48:24Z	--- 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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MinaMila/llama_instbase_LoRa_GermanCredit_ep10_55	MinaMila	2025-05-21T21:51:43Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-20T15:58:50Z	--- 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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PirateXX/AI-Content-Detector	PirateXX	2025-05-21T21:50:50Z	327	3	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "license:artistic-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-01-18T15:50:31Z	--- license: artistic-2.0 --- # AI Content Detector This model uses SOTA models to detect AI generated content.<br/> Label_0 represents Fake<br/> Label_1 represents Real View our website: [AI Content Detector](https://www.aiforfree.online/tools/text-to-detect)
rosieyzh/uf-dpo-llama3_1_8b_instruct-checkpoint_125-seed_42	rosieyzh	2025-05-21T21:50:49Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T21:44:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep9_55	MinaMila	2025-05-21T21:46:42Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-21T21:46:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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