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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-13 00:46:37	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 518 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-13 00:45:34	card stringlengths 11 1.01M
geoppls/geo-4-1748217727737-i4yulr	geoppls	2025-05-26T00:02:08Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-26T00:02:08Z	--- license: apache-2.0 ---
jonlecumberri/model1	jonlecumberri	2025-05-25T23:59:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T23:59:25Z	--- 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]
glif-loradex-trainer/Swap_agrawal14_zingy_nft	glif-loradex-trainer	2025-05-25T23:58:12Z	0	0	diffusers	[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]	text-to-image	2025-05-25T23:58:03Z	--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1748217440156__000001500_0.jpg text: Doctor $wap_zing_NFT - output: url: samples/1748217465023__000001500_1.jpg text: A mad creepy venomous batman $wap_zing_NFT base_model: black-forest-labs/FLUX.1-dev trigger: "$wap_zing_NFT" instance_prompt: "$wap_zing_NFT" license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # zingy_nft Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `Swap_agrawal14`. <Gallery /> ## Trigger words You should use `$wap_zing_NFT` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/Swap_agrawal14_zingy_nft/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
mradermacher/fine_tuned_qwen1.7B-GGUF	mradermacher	2025-05-25T23:58:10Z	1	0	transformers	[ "transformers", "gguf", "en", "dataset:Malikeh1375/medical-question-answering-datasets", "base_model:VesileHan/fine_tuned_qwen1.7B", "base_model:quantized:VesileHan/fine_tuned_qwen1.7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-25T04:32:21Z	--- base_model: VesileHan/fine_tuned_qwen1.7B datasets: - Malikeh1375/medical-question-answering-datasets 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/VesileHan/fine_tuned_qwen1.7B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/fine_tuned_qwen1.7B-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/fine_tuned_qwen1.7B-GGUF/resolve/main/fine_tuned_qwen1.7B.Q2_K.gguf) \| Q2_K \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/fine_tuned_qwen1.7B-GGUF/resolve/main/fine_tuned_qwen1.7B.Q3_K_S.gguf) \| Q3_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/fine_tuned_qwen1.7B-GGUF/resolve/main/fine_tuned_qwen1.7B.Q3_K_M.gguf) \| Q3_K_M \| 1.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/fine_tuned_qwen1.7B-GGUF/resolve/main/fine_tuned_qwen1.7B.Q3_K_L.gguf) \| Q3_K_L \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/fine_tuned_qwen1.7B-GGUF/resolve/main/fine_tuned_qwen1.7B.IQ4_XS.gguf) \| IQ4_XS \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/fine_tuned_qwen1.7B-GGUF/resolve/main/fine_tuned_qwen1.7B.Q4_K_S.gguf) \| Q4_K_S \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/fine_tuned_qwen1.7B-GGUF/resolve/main/fine_tuned_qwen1.7B.Q4_K_M.gguf) \| Q4_K_M \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/fine_tuned_qwen1.7B-GGUF/resolve/main/fine_tuned_qwen1.7B.Q5_K_S.gguf) \| Q5_K_S \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/fine_tuned_qwen1.7B-GGUF/resolve/main/fine_tuned_qwen1.7B.Q5_K_M.gguf) \| Q5_K_M \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/fine_tuned_qwen1.7B-GGUF/resolve/main/fine_tuned_qwen1.7B.Q6_K.gguf) \| Q6_K \| 1.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/fine_tuned_qwen1.7B-GGUF/resolve/main/fine_tuned_qwen1.7B.Q8_0.gguf) \| Q8_0 \| 1.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/fine_tuned_qwen1.7B-GGUF/resolve/main/fine_tuned_qwen1.7B.f16.gguf) \| f16 \| 3.5 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
vermoney/fa7c9ac1-c037-49cc-8ea3-26aa724f78d9	vermoney	2025-05-25T23:54:49Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Hermes-2-Theta-Llama-3-8B", "base_model:adapter:NousResearch/Hermes-2-Theta-Llama-3-8B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-25T23:19:40Z	--- library_name: peft license: apache-2.0 base_model: NousResearch/Hermes-2-Theta-Llama-3-8B tags: - axolotl - generated_from_trainer model-index: - name: fa7c9ac1-c037-49cc-8ea3-26aa724f78d9 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Hermes-2-Theta-Llama-3-8B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - cf8606bc5af5442f_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 3 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: vermoney/fa7c9ac1-c037-49cc-8ea3-26aa724f78d9 hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 96 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 48 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/cf8606bc5af5442f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 1f013e5f-2248-4122-86e5-3fe07fb937ab wandb_project: s56-9 wandb_run: your_name wandb_runid: 1f013e5f-2248-4122-86e5-3fe07fb937ab warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # fa7c9ac1-c037-49cc-8ea3-26aa724f78d9 This model is a fine-tuned version of [NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1675 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 18 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - training_steps: 280 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.9173 \| 0.0155 \| 280 \| 1.1675 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
MinaMila/llama_instbase_3b_LoRa_GermanCredit_ep10_66	MinaMila	2025-05-25T23:53:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T23:53:22Z	--- 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. 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/openthoughts3_100k_code_swap_r1	mlfoundations-dev	2025-05-25T23:48:00Z	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	2025-05-25T18:33:30Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: openthoughts3_100k_code_swap_r1 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. --> # openthoughts3_100k_code_swap_r1 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/openthoughts3_100k_code_swap_r1 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: 8e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 16 - total_train_batch_size: 512 - total_eval_batch_size: 256 - 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: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1 - Datasets 3.1.0 - Tokenizers 0.20.3
Remade-AI/Crush	Remade-AI	2025-05-25T23:47:48Z	1,011	9	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "image-to-video", "en", "base_model:Wan-AI/Wan2.1-I2V-14B-480P", "base_model:adapter:Wan-AI/Wan2.1-I2V-14B-480P", "license:apache-2.0", "region:us" ]	image-to-video	2025-03-11T22:40:06Z	--- license: apache-2.0 language: - en base_model: - Wan-AI/Wan2.1-I2V-14B-480P - Wan-AI/Wan2.1-I2V-14B-480P-Diffusers pipeline_tag: image-to-video tags: - text-to-image - lora - diffusers - template:diffusion-lora - image-to-video widget: - text: >- The video begins with a tank. A hydraulic press positioned above slowly descends towards the tank. Upon contact, the hydraulic press c5us4 crushes it, deforming and flattening the tank, causing the tank to collapse inward until the tank is no longer recognizable. output: url: example_videos/tank_crush.mp4 - text: >- The video begins with a man. A hydraulic press positioned above slowly descends towards the man. Upon contact, the hydraulic press c5us4 crushes it, deforming and flattening the man, causing the man to collapse inward until the man is no longer recognizable. output: url: example_videos/man_crush.mp4 - text: >- The video begins with a chicken. A hydraulic press positioned above slowly descends towards the chicken. Upon contact, the hydraulic press c5us4 crushes it, deforming and flattening the chicken, causing the chicken to collapse inward until the chicken is no longer recognizable. output: url: example_videos/chicken_crush.mp4 - text: >- The video begins with a coke. A hydraulic press positioned above slowly descends towards the coke. Upon contact, the hydraulic press c5us4 crushes it, deforming and flattening the coke, causing the coke to collapse inward until the coke is no longer recognizable. output: url: example_videos/coke_crush.mp4 --- <div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;"> <h1 style="color: #24292e; margin-top: 0;">Crush Effect LoRA for Wan2.1 14B I2V 480p</h1> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Overview</h2> <p>This LoRA is trained on the Wan2.1 14B I2V 480p model and allows you to crush any object in an image. The effect works on a wide variety of objects, from animals to vehicles to people!</p> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Features</h2> <ul style="margin-bottom: 0;"> <li>Transform any image into a video of it being crushed</li> <li>Trained on the Wan2.1 14B 480p I2V base model</li> <li>Consistent results across different object types</li> <li>Simple prompt structure that's easy to adapt</li> </ul> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Community</h2> <ul style="margin-bottom: 0;"> <li> Generate videos with 100+ Camera Control and VFX LoRAs on the <a href="https://app.remade.ai/canvas/create" style="color: #0366d6; text-decoration: none;">Remade Canvas</a>. </li> <li> <b>Discord:</b> <a href="https://remade.ai/join-discord?utm_source=Huggingface&utm_medium=Social&utm_campaign=model_release&utm_content=crash_zoom_out" style="color: #0366d6; text-decoration: none;"> Join our community </a> to generate videos with this LoRA for free </li> </ul> </div> <Gallery /> # Model File and Inference Workflow ## 📥 Download Links: - [crush_20_epochs.safetensors](./crush_20_epochs.safetensors) - LoRA Model File - [wan_img2vid_lora_workflow.json](./workflow/wan_img2vid_lora_workflow.json) - Wan I2V with LoRA Workflow for ComfyUI --- <div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;"> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Recommended Settings</h2> <ul style="margin-bottom: 0;"> <li><b>LoRA Strength:</b> 1.0</li> <li><b>Embedded Guidance Scale:</b> 6.0</li> <li><b>Flow Shift:</b> 5.0</li> </ul> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Trigger Words</h2> <p>The key trigger phrase is: <code style="background-color: #f0f0f0; padding: 3px 6px; border-radius: 4px;"> c5us4 crushes it</code></p> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Prompt Template</h2> <p>For best results, use this prompt structure:</p> <div style="background-color: #f0f0f0; padding: 12px; border-radius: 6px; margin: 10px 0;"> <i>The video begins with a [object]. A hydraulic press positioned above slowly descends towards the [object]. Upon contact, the hydraulic press c5us4 crushes it, deforming and flattening the [object], causing the [object] to collapse inward until the [object] is no longer recognizable.</i> </div> <p>Simply replace <code style="background-color: #f0f0f0; padding: 3px 6px; border-radius: 4px;">[object]</code> with whatever you want to see crushed!</p> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">ComfyUI Workflow</h2> <p>This LoRA works with a modified version of <a href="https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/main/example_workflows/wanvideo_480p_I2V_example_02.json" style="color: #0366d6; text-decoration: none;">Kijai's Wan Video Wrapper workflow</a>. The main modification is adding a Wan LoRA node connected to the base model.</p> <img src="./workflow/workflow_screenshot.png" style="width: 100%; border-radius: 8px; margin: 15px 0; box-shadow: 0 4px 8px rgba(0,0,0,0.1);"> <p>See the Downloads section above for the modified workflow.</p> </div> </div> <div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;"> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Model Information</h2> <p>The model weights are available in Safetensors format. See the Downloads section above.</p> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Training Details</h2> <ul style="margin-bottom: 0;"> <li><b>Base Model:</b> Wan2.1 14B I2V 480p</li> <li><b>Training Data:</b> Trained on 1.5 minutes of video comprised of 20 short clips (each clip captioned separately) of things being crushed by a hydraulic press</li> <li><b>Epochs:</b> 20</li> </ul> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Additional Information</h2> <p>Training was done using <a href="https://github.com/tdrussell/diffusion-pipe" style="color: #0366d6; text-decoration: none;">Diffusion Pipe for Training</a></p> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Acknowledgments</h2> <p style="margin-bottom: 0;">Special thanks to Kijai for the ComfyUI Wan Video Wrapper and tdrussell for the training scripts!</p> </div> </div>
Remade-AI/Cakeify	Remade-AI	2025-05-25T23:47:25Z	1,120	16	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "image-to-video", "en", "base_model:Wan-AI/Wan2.1-I2V-14B-480P", "base_model:adapter:Wan-AI/Wan2.1-I2V-14B-480P", "license:apache-2.0", "region:us" ]	image-to-video	2025-03-11T20:36:18Z	--- license: apache-2.0 language: - en base_model: - Wan-AI/Wan2.1-I2V-14B-480P - Wan-AI/Wan2.1-I2V-14B-480P-Diffusers pipeline_tag: image-to-video tags: - text-to-image - lora - diffusers - template:diffusion-lora - image-to-video widget: - text: >- The video opens on a puppy. A knife, held by a hand, is coming into frame and hovering over the puppy. The knife then begins cutting into the puppy to c4k3 cakeify it. As the knife slices the puppy open, the inside of the puppy is revealed to be cake with chocolate layers. The knife cuts through and the contents of the puppy are revealed. output: url: example_videos/puppy_cakeify.mp4 - text: >- The video opens on a woman. A knife, held by a hand, is coming into frame and hovering over the woman. The knife then begins cutting into the woman to c4k3 cakeify it. As the knife slices the woman open, the inside of the woman is revealed to be cake with chocolate layers. The knife cuts through and the contents of the woman are revealed. output: url: example_videos/woman_cakeify.mp4 - text: >- The video opens on a timberland boot. A knife, held by a hand, is coming into frame and hovering over the timberland boot. The knife then begins cutting into the timberland boot to c4k3 cakeify it. As the knife slices the timberland boot open, the inside of the timberland boot is revealed to be cake with chocolate layers. The knife cuts through and the contents of the timberland boot are revealed. output: url: example_videos/timberland_cakeify.mp4 - text: >- The video opens on a cat. A knife, held by a hand, is coming into frame and hovering over the cat. The knife then begins cutting into the cat to c4k3 cakeify it. As the knife slices the cat open, the inside of the cat is revealed to be cake with chocolate layers. The knife cuts through and the contents of the cat are revealed. output: url: example_videos/cat_cakeify.mp4 --- <div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;"> <h1 style="color: #24292e; margin-top: 0;">Cakeify Effect LoRA for Wan2.1 14B I2V 480p</h1> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Overview</h2> <p>This LoRA is trained on the Wan2.1 14B I2V 480p model and allows you to cakeify any object in an image. The effect works on a wide variety of objects, from animals to vehicles to people!</p> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Features</h2> <ul style="margin-bottom: 0;"> <li>Transform any image into a video of it being cakeified</li> <li>Trained on the Wan2.1 14B 480p I2V base model</li> <li>Consistent results across different object types</li> <li>Simple prompt structure that's easy to adapt</li> </ul> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Community</h2> <ul style="margin-bottom: 0;"> <li> Generate videos with 100+ Camera Control and VFX LoRAs on the <a href="https://app.remade.ai/canvas/create" style="color: #0366d6; text-decoration: none;">Remade Canvas</a>. </li> <li> <b>Discord:</b> <a href="https://remade.ai/join-discord?utm_source=Huggingface&utm_medium=Social&utm_campaign=model_release&utm_content=crash_zoom_out" style="color: #0366d6; text-decoration: none;"> Join our community </a> to generate videos with this LoRA for free </li> </ul> </div> <Gallery /> # Model File and Inference Workflow ## 📥 Download Links: - [cakeify_16_epochs.safetensors](./cakeify_16_epochs.safetensors) - LoRA Model File - [wan_img2vid_lora_workflow.json](./workflow/wan_img2vid_lora_workflow.json) - Wan I2V with LoRA Workflow for ComfyUI --- <div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;"> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Recommended Settings</h2> <ul style="margin-bottom: 0;"> <li><b>LoRA Strength:</b> 1.0</li> <li><b>Embedded Guidance Scale:</b> 6.0</li> <li><b>Flow Shift:</b> 5.0</li> </ul> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Trigger Words</h2> <p>The key trigger phrase is: <code style="background-color: #f0f0f0; padding: 3px 6px; border-radius: 4px;"> c4k3 cakeify it</code></p> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Prompt Template</h2> <p>For best results, use this prompt structure:</p> <div style="background-color: #f0f0f0; padding: 12px; border-radius: 6px; margin: 10px 0;"> <i>The video opens on a [object]. A knife, held by a hand, is coming into frame and hovering over the [object]. The knife then begins cutting into the [object] to c4k3 cakeify it. As the knife slices the [object] open, the inside of the [object] is revealed to be cake with chocolate layers. The knife cuts through and the contents of the [object] are revealed.</i> </div> <p>Simply replace <code style="background-color: #f0f0f0; padding: 3px 6px; border-radius: 4px;">[object]</code> with whatever you want to see cakeified!</p> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">ComfyUI Workflow</h2> <p>This LoRA works with a modified version of <a href="https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/main/example_workflows/wanvideo_480p_I2V_example_02.json" style="color: #0366d6; text-decoration: none;">Kijai's Wan Video Wrapper workflow</a>. The main modification is adding a Wan LoRA node connected to the base model.</p> <img src="./workflow/cakeify_workflow_screenshot.png" style="width: 100%; border-radius: 8px; margin: 15px 0; box-shadow: 0 4px 8px rgba(0,0,0,0.1);"> <p>See the Downloads section above for the modified workflow.</p> </div> </div> <div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;"> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Model Information</h2> <p>The model weights are available in Safetensors format. See the Downloads section above.</p> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Training Details</h2> <ul style="margin-bottom: 0;"> <li><b>Base Model:</b> Wan2.1 14B I2V 480p</li> <li><b>Training Data:</b> 1 minute of video (13 short clips of things being cakeified, each clip captioned separately)</li> <li><b>Epochs:</b> 16</li> </ul> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Additional Information</h2> <p>Training was done using <a href="https://github.com/tdrussell/diffusion-pipe" style="color: #0366d6; text-decoration: none;">Diffusion Pipe for Training</a></p> </div> <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);"> <h2 style="color: #24292e; margin-top: 0;">Acknowledgments</h2> <p style="margin-bottom: 0;">Special thanks to Kijai for the ComfyUI Wan Video Wrapper and tdrussell for the training scripts!</p> </div> </div>
haoqiwang/MNLP_M2_quantized_model	haoqiwang	2025-05-25T23:44:19Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "fbgemm_fp8", "region:us" ]	text-generation	2025-05-25T23:44:00Z	--- 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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jyoung105/ent2_t5	jyoung105	2025-05-25T23:44:00Z	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	2025-05-25T23:16:43Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Ent2_T5 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/jyoung105/ent2_t5/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('jyoung105/ent2_t5', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/jyoung105/ent2_t5/discussions) to add images that show off what you’ve made with this LoRA.
MinaMila/llama_instbase_3b_LoRa_GermanCredit_ep9_55	MinaMila	2025-05-25T23:42:01Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T23:41:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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jyoung105/ent2_t4	jyoung105	2025-05-25T23:40:35Z	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	2025-05-25T23:13:40Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Ent2_T4 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/jyoung105/ent2_t4/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('jyoung105/ent2_t4', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 8 ## Contribute your own examples You can use the [community tab](https://huggingface.co/jyoung105/ent2_t4/discussions) to add images that show off what you’ve made with this LoRA.
g-assismoraes/gemma-3-4b-it-fpi-alpha3.0-fromit-var-hatebr	g-assismoraes	2025-05-25T23:40:23Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-25T23:36:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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yuffy7/blip-finetuned-caption-obss	yuffy7	2025-05-25T23:40:00Z	0	0	transformers	[ "transformers", "safetensors", "blip", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-25T23:38:05Z	--- 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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cooperchris17/gemma-efcam-cefr-10k	cooperchris17	2025-05-25T23:39:07Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-1b-pt", "base_model:finetune:google/gemma-3-1b-pt", "endpoints_compatible", "region:us" ]	null	2025-05-25T10:11:06Z	--- base_model: google/gemma-3-1b-pt library_name: transformers model_name: gemma-efcam-cefr-10k tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-efcam-cefr-10k This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt). 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="cooperchris17/gemma-efcam-cefr-10k", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.7.0+cu128 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/llama_instbase_3b_LoRa_GermanCredit_ep8_55	MinaMila	2025-05-25T23:38:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T23:38:08Z	--- 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. 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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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fats-fme/561b06d4-643d-4c72-b686-30243d49f126	fats-fme	2025-05-25T23:37:44Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3.5-mini-instruct", "base_model:adapter:unsloth/Phi-3.5-mini-instruct", "license:mit", "region:us" ]	null	2025-05-25T21:31:43Z	--- library_name: peft license: mit base_model: unsloth/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: 561b06d4-643d-4c72-b686-30243d49f126 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Phi-3.5-mini-instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 09872ce4fa219451_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 100 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: fats-fme/561b06d4-643d-4c72-b686-30243d49f126 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 5.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 128 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: constant_with_warmup max_memory: 0: 130GB max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/09872ce4fa219451_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 100 saves_per_epoch: null sequence_len: 2048 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: bf57f359-e420-470a-bfa4-043417ef146d wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: bf57f359-e420-470a-bfa4-043417ef146d warmup_steps: 200 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 561b06d4-643d-4c72-b686-30243d49f126 This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.1053 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 200 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0000 \| 1 \| 7.1248 \| \| 9.0427 \| 0.0012 \| 100 \| 7.3019 \| \| 7.8358 \| 0.0023 \| 200 \| 7.1053 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
MinaMila/llama_instbase_3b_LoRa_GermanCredit_ep7_55	MinaMila	2025-05-25T23:34:26Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T23:34:18Z	--- 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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guoanjie/a2c-PandaReachDense-v3	guoanjie	2025-05-25T23:32:54Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-25T23:28:50Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.23 +/- 0.10 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 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 ... ```
0N-Labs/onyx-tts	0N-Labs	2025-05-25T23:31:18Z	0	1	null	[ "onnx", "region:us" ]	null	2025-05-25T19:51:15Z	# Onyx-TTS Onyx-TTS is a high-performance, multilingual text-to-speech system developed by 0N Labs. It's built on ONNX Runtime, delivering fast and efficient speech synthesis with minimal resource requirements. <p float="left"> <audio controls><source src="https://0N-Labs.github.io/example-one.wav" type="audio/wav"></audio> <audio controls><source src="https://0N-Labs.github.io/example-two.wav" type="audio/wav"></audio> </p> --- ## Model Details * Developed by: 0N Labs * Model type: Text-to-Speech * Languages: Multiple languages supported * License: Apache 2.0 * Model size: \~300MB (quantized: \~80MB) --- ## Getting Started ### Prerequisites * Python 3.8 or higher * `pip` (Python package manager) --- ### Installation 1. Clone the repository: ```bash git clone https://github.com/0N-Labs/onyx-tts.git cd onyx-tts ``` 2. Create and activate a virtual environment (recommended): ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ``` 3. Install the required dependencies: ```bash pip install -r requirements.txt ``` --- ### Downloading the Model Download the model files from the [Releases](https://github.com/0N-Labs/onyx-tts/releases) page and place them in your project directory: * `onyx-v1.0.onnx` (main model) * `voices-v1.0.bin` (voice data) --- ### Basic Usage ```python import soundfile as sf from onyx_tts import OnyxTTS # Initialize the TTS engine with the downloaded model files onyx = OnyxTTS("onyx-v1.0.onnx", "voices-v1.0.bin") # Generate speech samples, sample_rate = onyx.create( "Hello! This is Onyx TTS by 0N Labs.", voice="af_sarah", # See available voices in VOICES.md speed=1.0, # Adjust speed (0.5–2.0) lang="en-us" ) # Save the generated audio sf.write("output.wav", samples, sample_rate) print("Speech generated successfully!") ``` --- ### Running the Web Interface To launch the demo UI: ```bash python web_interface.py ``` Then visit: 👉 [http://localhost:7860](http://localhost:7860) --- ## Available Voices To list all available voices: ```python from onyx_tts import OnyxTTS onyx = OnyxTTS("onyx-v1.0.onnx", "voices-v1.0.bin") print("Available voices:", onyx.get_voices()) ``` See the full list in `VOICES.md`. --- ## Troubleshooting If you run into issues: 1. Ensure all model files are in the correct location 2. Check that all dependencies are installed 3. Verify Python version is 3.8 or higher 4. Visit the [issues page](https://github.com/0N-Labs/onyx-tts/issues) for known bugs and fixes --- ## License * Onyx-TTS: MIT * Onyx model: Apache 2.0 --- ## Citation ```bibtex @software{onyx-tts, author = {0N Labs}, title = {Onyx-TTS: High-performance Text-to-Speech}, year = {2025}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/0N-Labs/onyx-tts}} } ```
PirxTion/tulu-mathqa	PirxTion	2025-05-25T23:30:19Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "unsloth", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:PirxTion/MNLP_M2_dpo_model", "base_model:finetune:PirxTion/MNLP_M2_dpo_model", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T20:22:27Z	--- base_model: PirxTion/MNLP_M2_dpo_model library_name: transformers model_name: tulu-mathqa tags: - generated_from_trainer - unsloth - trl - dpo licence: license --- # Model Card for tulu-mathqa This model is a fine-tuned version of [PirxTion/MNLP_M2_dpo_model](https://huggingface.co/PirxTion/MNLP_M2_dpo_model). 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="PirxTion/tulu-mathqa", 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/jingxuan-sun-epfl/mnlp-dpo/runs/o0mcbgvc) 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: 4.52.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.0 ## 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}} } ```
Darmayut/Ai	Darmayut	2025-05-25T23:26:47Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-25T23:26:47Z	--- license: apache-2.0 ---
kavanmevada/LeakLock-0.0.1-1b-it-lora-f16-gguf	kavanmevada	2025-05-25T23:25:35Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "gemma3_text", "en", "base_model:unsloth/gemma-3-1b-it", "base_model:quantized:unsloth/gemma-3-1b-it", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-25T22:04:45Z	--- base_model: unsloth/gemma-3-1b-it tags: - text-generation-inference - transformers - unsloth - gemma3_text license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: kavanmevada - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-it This gemma3_text 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_3b_LoRa_GermanCredit_ep4_55	MinaMila	2025-05-25T23:23:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T23:22:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
dada22231/b31b07a2-7182-485f-a8ed-d0c46929aa47	dada22231	2025-05-25T23:19:52Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B", "base_model:adapter:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B", "region:us" ]	null	2025-05-25T17:54:24Z	--- library_name: peft base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B tags: - axolotl - generated_from_trainer model-index: - name: b31b07a2-7182-485f-a8ed-d0c46929aa47 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 87851332f61582b9_train_data.json ds_type: json path: /workspace/input_data/ split: train type: completion field: prompt debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evaluation_strategy: epoch flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true group_by_length: false hub_model_id: dada22231/b31b07a2-7182-485f-a8ed-d0c46929aa47 hub_token: "[REMOVED]" push_to_hub: true save_total_limit: 20 hub_repo: null hub_strategy: every_save learning_rate: 2e-5 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 256 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 128 lora_target_linear: true lr_scheduler: constant_with_warmup max_steps: 1500 micro_batch_size: 16 mlflow_experiment_name: /tmp/87851332f61582b9_train_data.json model_type: AutoModelForCausalLM num_epochs: 5 optimizer: adamw_torch output_dir: ./outputs/miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false eval_sample_packing: false save_strategy: epoch saves_per_epoch: 1 sequence_len: 2048 special_tokens: pad_token: <\|eot_id\|> strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 8f63744a-e094-4dd5-a3e9-923b8a1fb2cb wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 8f63744a-e094-4dd5-a3e9-923b8a1fb2cb warmup_steps: 100 weight_decay: 0.01 xformers_attention: false neftune_noise_alpha: 5 max_grad_norm: 1.0 ``` </details><br> # b31b07a2-7182-485f-a8ed-d0c46929aa47 This model is a fine-tuned version of [Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4641 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 100 - training_steps: 1500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.349 \| 0.2196 \| 1500 \| 1.4641 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
jtatman/llama-3.2-1b-deepseek-dolphin-lora	jtatman	2025-05-25T23:18:01Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T23:16:38Z	--- 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. 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]
asim800/hfexample	asim800	2025-05-25T23:17:47Z	0	0	null	[ "region:us" ]	null	2025-05-25T22:48:36Z	This my huggingface model --- license: mit ---
g-assismoraes/gemma-3-4b-it-fpi-alpha4.0-fromit-var-hatebr	g-assismoraes	2025-05-25T23:16:45Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-25T23:13:12Z	--- 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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sxj1215/Qwen2-VL-Synergy	sxj1215	2025-05-25T23:14:11Z	0	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:Qwen/Qwen2-VL-7B-Instruct", "base_model:adapter:Qwen/Qwen2-VL-7B-Instruct", "license:other", "region:us" ]	null	2025-05-25T23:13:46Z	--- base_model: Qwen/Qwen2-VL-7B-Instruct library_name: peft license: other tags: - llama-factory - lora - generated_from_trainer model-index: - name: sft_synergy_scienceqalast 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. --> # sft_synergy_scienceqalast This model is a fine-tuned version of [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) on the mmimdb, the memecap, the hateful_memes, the ny_cartoon, the memotion and the scienceqa datasets. It achieves the following results on the evaluation set: - Loss: 0.5355 ## 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: 0.0001 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7891 \| 0.1957 \| 500 \| 0.6836 \| \| 0.7446 \| 0.3914 \| 1000 \| 0.6307 \| \| 0.7208 \| 0.5870 \| 1500 \| 0.5877 \| \| 0.6512 \| 0.7827 \| 2000 \| 0.5539 \| \| 0.6369 \| 0.9784 \| 2500 \| 0.5330 \| \| 0.47 \| 1.1741 \| 3000 \| 0.5348 \| \| 0.3866 \| 1.3697 \| 3500 \| 0.5188 \| \| 0.4721 \| 1.5654 \| 4000 \| 0.5088 \| \| 0.5444 \| 1.7611 \| 4500 \| 0.4966 \| \| 0.5069 \| 1.9568 \| 5000 \| 0.4991 \| \| 0.3624 \| 2.1524 \| 5500 \| 0.5303 \| \| 0.3805 \| 2.3481 \| 6000 \| 0.5416 \| \| 0.4058 \| 2.5438 \| 6500 \| 0.5372 \| \| 0.4088 \| 2.7395 \| 7000 \| 0.5369 \| \| 0.3336 \| 2.9351 \| 7500 \| 0.5356 \| ### Framework versions - PEFT 0.12.0 - Transformers 4.45.2 - Pytorch 2.1.2+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
sxj1215/Qwen2-VL-Redundancy	sxj1215	2025-05-25T23:12:22Z	0	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:Qwen/Qwen2-VL-7B-Instruct", "base_model:adapter:Qwen/Qwen2-VL-7B-Instruct", "license:other", "region:us" ]	null	2025-05-25T23:04:45Z	--- library_name: peft license: other base_model: Qwen/Qwen2-VL-7B-Instruct tags: - llama-factory - lora - generated_from_trainer model-index: - name: sft_redundancy_new 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. --> # sft_redundancy_new This model is a fine-tuned version of [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) on the resisc45, the ucmerced, the fer2013, the scienceqa, the mmimdb and the screen2words datasets. It achieves the following results on the evaluation set: - Loss: 0.5808 ## 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: 0.0001 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:-----:\|:---------------:\| \| 0.8948 \| 0.0481 \| 500 \| 0.6562 \| \| 0.6832 \| 0.0961 \| 1000 \| 0.6148 \| \| 0.5927 \| 0.1442 \| 1500 \| 0.5914 \| \| 0.6813 \| 0.1923 \| 2000 \| 0.5738 \| \| 0.4088 \| 0.2403 \| 2500 \| 0.5824 \| \| 0.6205 \| 0.2884 \| 3000 \| 0.5768 \| \| 0.7229 \| 0.3364 \| 3500 \| 0.5607 \| \| 0.6292 \| 0.3845 \| 4000 \| 0.5635 \| \| 0.6033 \| 0.4326 \| 4500 \| 0.5492 \| \| 0.4986 \| 0.4806 \| 5000 \| 0.5470 \| \| 0.623 \| 0.5287 \| 5500 \| 0.5453 \| \| 0.6596 \| 0.5768 \| 6000 \| 0.5430 \| \| 0.6779 \| 0.6248 \| 6500 \| 0.5386 \| \| 0.6796 \| 0.6729 \| 7000 \| 0.5345 \| \| 0.5758 \| 0.7209 \| 7500 \| 0.5397 \| \| 0.5142 \| 0.7690 \| 8000 \| 0.5340 \| \| 0.5752 \| 0.8171 \| 8500 \| 0.5318 \| \| 0.4997 \| 0.8651 \| 9000 \| 0.5289 \| \| 0.6262 \| 0.9132 \| 9500 \| 0.5303 \| \| 0.6193 \| 0.9613 \| 10000 \| 0.5334 \| \| 0.7338 \| 1.0093 \| 10500 \| 0.5258 \| \| 0.6178 \| 1.0574 \| 11000 \| 0.5341 \| \| 0.5629 \| 1.1055 \| 11500 \| 0.5253 \| \| 0.6407 \| 1.1535 \| 12000 \| 0.5292 \| \| 0.5549 \| 1.2016 \| 12500 \| 0.5284 \| \| 0.4914 \| 1.2496 \| 13000 \| 0.5231 \| \| 0.4535 \| 1.2977 \| 13500 \| 0.5242 \| \| 0.5162 \| 1.3458 \| 14000 \| 0.5224 \| \| 0.4466 \| 1.3938 \| 14500 \| 0.5275 \| \| 0.5427 \| 1.4419 \| 15000 \| 0.5243 \| \| 0.4722 \| 1.4900 \| 15500 \| 0.5145 \| \| 0.6199 \| 1.5380 \| 16000 \| 0.5200 \| \| 0.4566 \| 1.5861 \| 16500 \| 0.5288 \| \| 0.5564 \| 1.6341 \| 17000 \| 0.5169 \| \| 0.5187 \| 1.6822 \| 17500 \| 0.5143 \| \| 0.5339 \| 1.7303 \| 18000 \| 0.5104 \| \| 0.5703 \| 1.7783 \| 18500 \| 0.5110 \| \| 0.5368 \| 1.8264 \| 19000 \| 0.5142 \| \| 0.6051 \| 1.8745 \| 19500 \| 0.5110 \| \| 0.4187 \| 1.9225 \| 20000 \| 0.5140 \| \| 0.5876 \| 1.9706 \| 20500 \| 0.5118 \| \| 0.2579 \| 2.0186 \| 21000 \| 0.5429 \| \| 0.3344 \| 2.0667 \| 21500 \| 0.5561 \| \| 0.2026 \| 2.1148 \| 22000 \| 0.5703 \| \| 0.3255 \| 2.1628 \| 22500 \| 0.5742 \| \| 0.3463 \| 2.2109 \| 23000 \| 0.5739 \| \| 0.3232 \| 2.2590 \| 23500 \| 0.5824 \| \| 0.2879 \| 2.3070 \| 24000 \| 0.5799 \| \| 0.3236 \| 2.3551 \| 24500 \| 0.5742 \| \| 0.3262 \| 2.4032 \| 25000 \| 0.5799 \| \| 0.3792 \| 2.4512 \| 25500 \| 0.5767 \| \| 0.3268 \| 2.4993 \| 26000 \| 0.5762 \| \| 0.2743 \| 2.5473 \| 26500 \| 0.5775 \| \| 0.3534 \| 2.5954 \| 27000 \| 0.5800 \| \| 0.2689 \| 2.6435 \| 27500 \| 0.5803 \| \| 0.3619 \| 2.6915 \| 28000 \| 0.5801 \| \| 0.3634 \| 2.7396 \| 28500 \| 0.5803 \| \| 0.3301 \| 2.7877 \| 29000 \| 0.5804 \| \| 0.3127 \| 2.8357 \| 29500 \| 0.5821 \| \| 0.3687 \| 2.8838 \| 30000 \| 0.5810 \| \| 0.2652 \| 2.9318 \| 30500 \| 0.5806 \| \| 0.4041 \| 2.9799 \| 31000 \| 0.5809 \| ### Framework versions - PEFT 0.12.0 - Transformers 4.45.2 - Pytorch 2.1.2+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
MinaMila/llama_instbase_3b_LoRa_GermanCredit_ep1_55	MinaMila	2025-05-25T23:11:42Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T23:11:38Z	--- 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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joeyderrrr/ft_test_16bit_safetensor	joeyderrrr	2025-05-25T23:08:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T22:41:02Z	--- library_name: transformers tags: - unsloth - trl - sft --- # 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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JosefKuchar/LarsNet	JosefKuchar	2025-05-25T22:59:32Z	0	0	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2025-05-25T22:54:14Z	--- license: cc-by-nc-4.0 ---
RayneAmes/kanye_v2	RayneAmes	2025-05-25T22:56:58Z	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-02-23T05:02:32Z	--- 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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(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]
biocircuit/giorgiobiocircuit	biocircuit	2025-05-25T22:56:19Z	0	0	null	[ "region:us" ]	null	2025-05-25T22:44:09Z	# Biocircuit Modulo 2B — LLaMA 3 Inference Questo modulo utilizza LLaMA 3 via Hugging Face Inference API per estrarre dati scientifici da testi PDF già preprocessati (v1.json). ## ✅ Come usare 1. Crea un file `.env` con la tua Hugging Face API Key: ``` HF_TOKEN=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 ``` 2. Installa le dipendenze: ```bash pip install requests ``` 3. Lancia un test: ```bash python test_llama.py ``` ## 📦 Output La funzione `llama_query(prompt)` restituisce un dizionario JSON con i dati scientifici strutturati. --- Creato per il progetto Biocircuit (© Giorgio Castiglione)
MinaMila/llama_instbase_3b_LoRa_GermanCredit_ep9_33	MinaMila	2025-05-25T22:56:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T22:56:12Z	--- 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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mac-mvak/Qwen3-0.6B-Base-INT8-SMOOTHQUANT	mac-mvak	2025-05-25T22:55:23Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "compressed-tensors", "region:us" ]	text-generation	2025-05-25T22:54:51Z	--- 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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rbojja/gemma-3-ft-summary	rbojja	2025-05-25T22:54:43Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-25T22:54:35Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: rbojja - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
vermoney/da17ddf2-de49-4c0e-adf5-e98eef7c1951	vermoney	2025-05-25T22:49:51Z	0	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:unsloth/gemma-1.1-2b-it", "base_model:adapter:unsloth/gemma-1.1-2b-it", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-25T22:30:34Z	--- library_name: peft license: apache-2.0 base_model: unsloth/gemma-1.1-2b-it tags: - axolotl - generated_from_trainer model-index: - name: da17ddf2-de49-4c0e-adf5-e98eef7c1951 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/gemma-1.1-2b-it bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 3ff3c8fbdfd33acf_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 3 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: vermoney/da17ddf2-de49-4c0e-adf5-e98eef7c1951 hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 96 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 48 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/3ff3c8fbdfd33acf_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 548f728d-3710-4a92-ace9-bf8a1608cfe4 wandb_project: s56-9 wandb_run: your_name wandb_runid: 548f728d-3710-4a92-ace9-bf8a1608cfe4 warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # da17ddf2-de49-4c0e-adf5-e98eef7c1951 This model is a fine-tuned version of [unsloth/gemma-1.1-2b-it](https://huggingface.co/unsloth/gemma-1.1-2b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1990 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 18 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - training_steps: 280 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1343 \| 0.0202 \| 280 \| 1.1990 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/RareBit-v2-32B-i1-GGUF	mradermacher	2025-05-25T22:48:45Z	0	1	transformers	[ "transformers", "gguf", "chat", "merge", "roleplay", "en", "base_model:ParasiticRogue/RareBit-v2-32B", "base_model:quantized:ParasiticRogue/RareBit-v2-32B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-25T16:31:48Z	--- base_model: ParasiticRogue/RareBit-v2-32B language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/LICENSE license_name: qwen quantized_by: mradermacher tags: - chat - merge - roleplay --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/ParasiticRogue/RareBit-v2-32B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/RareBit-v2-32B-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/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 11.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-Q2_K.gguf) \| i1-Q2_K \| 12.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 19.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-Q4_1.gguf) \| i1-Q4_1 \| 20.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF/resolve/main/RareBit-v2-32B.i1-Q6_K.gguf) \| i1-Q6_K \| 27.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
sergioalves/2cfbcdcc-3fbe-4473-aaf8-a052b72dd232	sergioalves	2025-05-25T22:48:08Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3.5-mini-instruct", "base_model:adapter:unsloth/Phi-3.5-mini-instruct", "license:mit", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-25T21:33:49Z	--- library_name: peft license: mit base_model: unsloth/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: 2cfbcdcc-3fbe-4473-aaf8-a052b72dd232 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/Phi-3.5-mini-instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 09872ce4fa219451_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 3 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: sergioalves/2cfbcdcc-3fbe-4473-aaf8-a052b72dd232 hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/09872ce4fa219451_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: bf57f359-e420-470a-bfa4-043417ef146d wandb_project: s56-7 wandb_run: your_name wandb_runid: bf57f359-e420-470a-bfa4-043417ef146d warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # 2cfbcdcc-3fbe-4473-aaf8-a052b72dd232 This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 10.0113 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 18 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 11.4597 \| 0.0000 \| 1 \| 10.0906 \| \| 11.8982 \| 0.0066 \| 250 \| 10.0235 \| \| 10.0588 \| 0.0132 \| 500 \| 10.0113 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
tn379/peft_bart	tn379	2025-05-25T22:47:18Z	4	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:philschmid/bart-large-cnn-samsum", "base_model:adapter:philschmid/bart-large-cnn-samsum", "license:mit", "region:us" ]	null	2025-05-25T04:20:44Z	--- library_name: peft license: mit base_model: philschmid/bart-large-cnn-samsum tags: - generated_from_trainer model-index: - name: peft_bart results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # peft_bart This model is a fine-tuned version of [philschmid/bart-large-cnn-samsum](https://huggingface.co/philschmid/bart-large-cnn-samsum) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8723 ## 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: 0.001 - 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.2444 \| 1.0 \| 151 \| 0.9093 \| \| 1.1951 \| 2.0 \| 302 \| 0.8908 \| \| 1.177 \| 3.0 \| 453 \| 0.8790 \| \| 1.1615 \| 4.0 \| 604 \| 0.8723 \| ### Framework versions - PEFT 0.14.0 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
syedMohib44/ai-auditor-model	syedMohib44	2025-05-25T22:44:40Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]	null	2025-05-25T22:44:24Z	--- base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
diniiiiiii/labla	diniiiiiii	2025-05-25T22:41:41Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:lodestones/Chroma", "base_model:adapter:lodestones/Chroma", "region:us" ]	text-to-image	2025-05-25T22:40:29Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/diniiblu (3).jpeg base_model: lodestones/Chroma instance_prompt: null --- # diblaaaa <Gallery /> ## Download model [Download](/diniiiiiii/labla/tree/main) them in the Files & versions tab.
Blinorot/qwen3-06.B-dpo	Blinorot	2025-05-25T22:41:27Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "alignment-handbook", "trl", "dpo", "conversational", "dataset:HuggingFaceH4/ultrafeedback_binarized", "arxiv:2305.18290", "base_model:Blinorot/qwen3-06.B-sft", "base_model:finetune:Blinorot/qwen3-06.B-sft", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T12:13:43Z	--- base_model: Blinorot/qwen3-06.B-sft datasets: - HuggingFaceH4/ultrafeedback_binarized library_name: transformers model_name: qwen3-06.B-dpo tags: - generated_from_trainer - alignment-handbook - trl - dpo licence: license --- # Model Card for qwen3-06.B-dpo This model is a fine-tuned version of [Blinorot/qwen3-06.B-sft](https://huggingface.co/Blinorot/qwen3-06.B-sft) on the [['HuggingFaceH4/ultrafeedback_binarized']](https://huggingface.co/datasets/['HuggingFaceH4/ultrafeedback_binarized']) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Blinorot/qwen3-06.B-dpo", 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/blinorot/huggingface/runs/d5yfm6sl) 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: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/Qwen3-30B-A3B-ERP-v0.1-GGUF	mradermacher	2025-05-25T22:36:48Z	0	0	transformers	[ "transformers", "gguf", "ja", "base_model:Aratako/Qwen3-30B-A3B-ERP-v0.1", "base_model:quantized:Aratako/Qwen3-30B-A3B-ERP-v0.1", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-25T08:56:52Z	--- base_model: Aratako/Qwen3-30B-A3B-ERP-v0.1 language: - ja library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Aratako/Qwen3-30B-A3B-ERP-v0.1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-30B-A3B-ERP-v0.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-ERP-v0.1-GGUF/resolve/main/Qwen3-30B-A3B-ERP-v0.1.Q2_K.gguf) \| Q2_K \| 11.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-ERP-v0.1-GGUF/resolve/main/Qwen3-30B-A3B-ERP-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 13.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-ERP-v0.1-GGUF/resolve/main/Qwen3-30B-A3B-ERP-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 14.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-ERP-v0.1-GGUF/resolve/main/Qwen3-30B-A3B-ERP-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 16.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-ERP-v0.1-GGUF/resolve/main/Qwen3-30B-A3B-ERP-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 16.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-ERP-v0.1-GGUF/resolve/main/Qwen3-30B-A3B-ERP-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 17.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-ERP-v0.1-GGUF/resolve/main/Qwen3-30B-A3B-ERP-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 18.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-ERP-v0.1-GGUF/resolve/main/Qwen3-30B-A3B-ERP-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 21.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-ERP-v0.1-GGUF/resolve/main/Qwen3-30B-A3B-ERP-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 21.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-ERP-v0.1-GGUF/resolve/main/Qwen3-30B-A3B-ERP-v0.1.Q6_K.gguf) \| Q6_K \| 25.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-ERP-v0.1-GGUF/resolve/main/Qwen3-30B-A3B-ERP-v0.1.Q8_0.gguf) \| Q8_0 \| 32.6 \| 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 -->
asehriyar/blip-finetuned-captioning	asehriyar	2025-05-25T22:28:07Z	0	0	transformers	[ "transformers", "safetensors", "blip", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-25T22:12:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Darkhn/Test523	Darkhn	2025-05-25T22:24:37Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:momergul/babylm-baseline-100m-gpt2", "base_model:finetune:momergul/babylm-baseline-100m-gpt2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T22:24:27Z	--- base_model: - momergul/babylm-baseline-100m-gpt2 library_name: transformers tags: - mergekit - merge --- # merged_model_output 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 [momergul/babylm-baseline-100m-gpt2](https://huggingface.co/momergul/babylm-baseline-100m-gpt2) as a base. ### Models Merged The following models were included in the merge: ### Configuration The following YAML configuration was used to produce this model: ```yaml # --- Mergekit Example: model_stock --- # Method: Averages "stock" models and combines with a base model. base_model: momergul/babylm-baseline-100m-gpt2 models: - model: momergul/babylm-baseline-100m-gpt2 - model: momergul/babylm-baseline-100m-gpt2 model_name: MyModelStockMerge-v1 # Name of your merge dtype: float32 # Input size float32, float16, bfloat16 out_dtype: bfloat16 # output size float32, float16, bfloat16 merge_method: model_stock parameters: filter_wise: false # Default tokenizer_source: momergul/babylm-baseline-100m-gpt2 # Or 'base' if base_model is set, or 'union', careful with this one chat_template: llama3 # Template for chat (Chatml, llama3, etc...) license: apache-2.0 # License type ```
Aconexx/SpeToI_distilBERT_speech_intent_classifier	Aconexx	2025-05-25T22:24:15Z	0	0	null	[ "safetensors", "distilbert", "license:apache-2.0", "region:us" ]	null	2025-03-30T22:21:28Z	--- license: apache-2.0 ---
concept-unlearning/Meta-Llama-3-8B_ft_lora_civil_comments_v3_ft_ft_lora_toxic_v1_ft	concept-unlearning	2025-05-25T22:20:34Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T22:18:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
Darkhn/Test52	Darkhn	2025-05-25T22:17:01Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:momergul/babylm-baseline-100m-gpt2", "base_model:finetune:momergul/babylm-baseline-100m-gpt2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T22:16:52Z	--- base_model: - momergul/babylm-baseline-100m-gpt2 library_name: transformers tags: - mergekit - merge --- # merged_model_output 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 [momergul/babylm-baseline-100m-gpt2](https://huggingface.co/momergul/babylm-baseline-100m-gpt2) as a base. ### Models Merged The following models were included in the merge: ### Configuration The following YAML configuration was used to produce this model: ```yaml # --- Mergekit Example: model_stock --- # Method: Averages "stock" models and combines with a base model. base_model: momergul/babylm-baseline-100m-gpt2 models: - model: momergul/babylm-baseline-100m-gpt2 - model: momergul/babylm-baseline-100m-gpt2 model_name: MyModelStockMerge-v1 # Name of your merge dtype: float32 # Input size float32, float16, bfloat16 out_dtype: bfloat16 # output size float32, float16, bfloat16 merge_method: model_stock parameters: filter_wise: false # Default tokenizer_source: momergul/babylm-baseline-100m-gpt2 # Or 'base' if base_model is set, or 'union', careful with this one chat_template: llama3 # Template for chat (Chatml, llama3, etc...) license: apache-2.0 # License type ```
Dione25/Reinforce-CartPole-v1	Dione25	2025-05-25T22:13:35Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-25T22:13:24Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Ulandaev/students_scores_model	Ulandaev	2025-05-25T22:09:21Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-25T18:32:35Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - f1 model-index: - name: students_scores_model 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. --> # students_scores_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8949 - F1: 0.6090 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.1018 \| 1.0 \| 563 \| 0.9776 \| 0.5582 \| \| 0.902 \| 2.0 \| 1126 \| 0.9058 \| 0.6004 \| \| 0.8114 \| 3.0 \| 1689 \| 0.8949 \| 0.6090 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Luzyto/Luzy	Luzyto	2025-05-25T22:07:41Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-25T22:07:41Z	--- license: apache-2.0 ---
dhintech/marian-id-en-lg	dhintech	2025-05-25T22:05:52Z	0	0	null	[ "safetensors", "marian", "translation", "indonesian", "english", "fine-tuned", "id", "en", "dataset:ted_talks_iwslt", "base_model:Helsinki-NLP/opus-mt-id-en", "base_model:finetune:Helsinki-NLP/opus-mt-id-en", "license:apache-2.0", "region:us" ]	translation	2025-05-25T22:05:29Z	--- language: - id - en license: apache-2.0 base_model: Helsinki-NLP/opus-mt-id-en tags: - translation - indonesian - english - marian - fine-tuned pipeline_tag: translation datasets: - ted_talks_iwslt widget: - text: "Selamat pagi, terima kasih sudah datang." example_title: "Greeting" - text: "Teknologi artificial intelligence berkembang pesat." example_title: "Technology" - text: "Mari kita diskusikan hasil penelitian ini." example_title: "Discussion" --- # MarianMT Indonesian-English Translation (Fine-tuned) This model is a fine-tuned version of [Helsinki-NLP/opus-mt-id-en](https://huggingface.co/Helsinki-NLP/opus-mt-id-en) for Indonesian to English translation. ## Model Details - Base Model: Helsinki-NLP/opus-mt-id-en - Fine-tuned on: TED Talks parallel corpus (Indonesian-English) - Training Date: 2025-05-25 - Languages: Indonesian (id) → English (en) - License: Apache 2.0 ## Training Configuration - Training Framework: PyTorch + Transformers - Training Data: TED Talks parallel corpus - Dataset Usage: 100% of full dataset - Training Parameters: - Learning Rate: 3e-5 - Batch Size: 4/2 (GPU/CPU) - Max Length: 128 tokens - Epochs: 10 ## Usage ```python from transformers import MarianMTModel, MarianTokenizer # Load model and tokenizer tokenizer = MarianTokenizer.from_pretrained("dhintech/marian-id-en-lg") model = MarianMTModel.from_pretrained("dhintech/marian-id-en-lg") # Translate Indonesian to English def translate(text): inputs = tokenizer(text, return_tensors="pt", padding=True) outputs = model.generate(**inputs, max_length=128, num_beams=4) return tokenizer.decode(outputs[0], skip_special_tokens=True) # Example usage indonesian_text = "Selamat pagi, terima kasih sudah datang." english_translation = translate(indonesian_text) print(english_translation) ``` ## Example Translations \| Indonesian \| English \| \|------------\|---------\| \| Selamat pagi, terima kasih sudah datang. \| Good morning, thank you for coming. \| \| Teknologi AI berkembang sangat pesat. \| AI technology is developing very rapidly. \| \| Mari kita diskusikan hasil penelitian ini. \| Let's discuss the results of this research. \| ## Performance - Optimized for conversational and presentation-style text - Best performance on formal Indonesian text - Model size: approximately 300MB - Suitable for mobile deployment ## Citation ```bibtex @misc{marian-id-en-lg, title={MarianMT Indonesian-English Translation (Fine-tuned)}, author={DhinTech}, year={2025}, publisher={Hugging Face}, journal={Hugging Face Model Hub}, howpublished={\url{https://huggingface.co/dhintech/marian-id-en-lg}} } ```
vermoney/3bb7f036-0ab6-407c-a2d2-3922804daf20	vermoney	2025-05-25T22:05:06Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3.5-mini-instruct", "base_model:adapter:unsloth/Phi-3.5-mini-instruct", "license:mit", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-25T21:36:33Z	--- library_name: peft license: mit base_model: unsloth/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: 3bb7f036-0ab6-407c-a2d2-3922804daf20 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Phi-3.5-mini-instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 09872ce4fa219451_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 3 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: vermoney/3bb7f036-0ab6-407c-a2d2-3922804daf20 hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 96 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 48 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/09872ce4fa219451_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: bf57f359-e420-470a-bfa4-043417ef146d wandb_project: s56-9 wandb_run: your_name wandb_runid: bf57f359-e420-470a-bfa4-043417ef146d warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # 3bb7f036-0ab6-407c-a2d2-3922804daf20 This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 9.9915 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 18 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - training_steps: 280 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 8.6495 \| 0.0074 \| 280 \| 9.9915 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ReadyArt/Omega-Darkest_The-Broken-Tutu-GLM-32B_EXL3_6.0bpw_H8	ReadyArt	2025-05-25T22:02:55Z	0	0	null	[ "safetensors", "glm4", "nsfw", "explicit", "roleplay", "unaligned", "ERP", "Erotic", "Horror", "Violence", "text-generation", "conversational", "en", "base_model:ReadyArt/Omega-Darkest_The-Broken-Tutu-GLM-32B", "base_model:quantized:ReadyArt/Omega-Darkest_The-Broken-Tutu-GLM-32B", "license:mit", "6-bit", "exl3", "region:us" ]	text-generation	2025-05-25T21:58:46Z	--- license: mit language: - en base_model: - ReadyArt/Omega-Darkest_The-Broken-Tutu-GLM-32B base_model_relation: quantized quantized_by: gecfdo pipeline_tag: text-generation tags: - nsfw - explicit - roleplay - unaligned - ERP - Erotic - Horror - Violence --- <style> strong { color: #FF1493 !important; } body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #ffd6e7 0%, #ffc0cb 100%); color: #ff0077 !important; text-shadow: 0 0 3px rgba(255, 192, 203, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #ffe6ee 0%, #ffd1dc 100%); color: #d4005e !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); } } .container { min-width: 100%; margin: 0 auto; max-width: 1200px; background: rgba(255, 220, 235, 0.95); border-radius: 12px; padding: 30px; box-shadow: 0 0 20px rgba(255, 105, 180, 0.1); border: 1px solid rgba(255, 20, 147, 0.2); position: relative; overflow: hidden; } .container::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(255, 105, 180, 0.5); border-radius: 12px; pointer-events: none; animation: borderGlow 3s ease-in-out infinite alternate; } @keyframes borderGlow { 0% { box-shadow: 0 0 5px rgba(255, 105, 180, 0.3); border-color: rgba(255, 105, 180, 0.5); } 50% { box-shadow: 0 0 15px rgba(255, 0, 127, 0.3); border-color: rgba(255, 0, 127, 0.5); } 100% { box-shadow: 0 0 5px rgba(255, 105, 180, 0.3); border-color: rgba(255, 105, 180, 0.5); } } .header { text-align: center; margin-bottom: 30px; position: relative; } .model-name { color: #ff1493; font-size: 2.5em; text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); margin: 0; letter-spacing: -1px; animation: textGlow 4s ease-in-out infinite alternate; } .subtitle { color: #FF1493 !important; font-size: 1.5em; text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); margin-top: 10px; } @keyframes textGlow { 0% { text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); } 50% { text-shadow: 0 0 20px rgba(255, 0, 127, 0.5); } 100% { text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); } } .waifu-container { margin: 20px -30px; width: calc(100% + 60px); overflow: hidden; border-radius: 8px; border: 1px solid rgba(255, 105, 180, 0.3); position: relative; } .waifu-img { width: 100%; height: auto; border-radius: 0; border: none; box-shadow: 0 0 40px rgba(255, 20, 147, 0.2); } .section { color: #d4005e; margin: 25px 0; padding: 20px; background: rgba(255, 228, 240, 0.9); border-radius: 8px; border: 1px solid rgba(255, 105, 180, 0.15); } .section-title { color: #ff1493; font-size: 1.8em; margin-top: 0; text-shadow: 0 0 5px rgba(255, 20, 147, 0.3); } .quant-links { display: grid; grid-template-columns: repeat(3, 1fr); gap: 15px; margin: 20px 0; } .link-card { padding: 15px; background: rgba(255, 228, 240, 0.95); border-radius: 8px; border: 1px solid rgba(255, 105, 180, 0.1); } .link-card h3 { color: #FF1493 !important; margin-top: 0; text-shadow: 0 0 5px rgba(255, 20, 147, 0.3); } .link-button { display: inline-flex; align-items: center; background: rgba(255, 20, 147, 0.1); color: #FF1493 !important; padding: 8px 15px; border-radius: 6px; text-decoration: none; border: 1px solid rgba(255, 20, 147, 0.3); transition: all 0.3s ease; } .link-button:hover { background: rgba(255, 20, 147, 0.2); box-shadow: 0 0 10px rgba(255, 20, 147, 0.3); } .disclaimer { color: #C71585; border-left: 3px solid #C71585; padding-left: 15px; margin: 20px 0; } </style> <div class="container"> <div class="header"> <h1 class="model-name">Omega Darkest</h1> <h1 class="model-name">The Broken Tutu GLM</h1> </div> <div class="waifu-container"> <img src="./waifu9.webp" class="waifu-img" alt="Omega Darkest Waifu"> </div> <div class="section"> <h2 class="section-title">🩸 The darkest finetune I've done</h2> <p>Turn away now. Nobody is dark enough to actually want this.</p> <ul> <li>🧬 <strong>Expanded 25M Token Dataset:</strong> Made with 687 erotic, horror and violence novels and 8,742 scenarios</li> <li>🧟 <strong>Enhanced Gore Protocols:</strong> Vivid anatomical descriptions with medical precision</li> <li>💎 <strong>Balanced Depravity:</strong> Retains Forgotten-Safeword's ERP edge while taking violence to the next level</li> <li>📜 <strong>Enhanced Character Piloting:</strong> Characters exhibit more nuanced personalities and motivations in extreme scenarios</li> <li>⚰️ <strong>Mortality Awareness:</strong> Characters react realistically to pain, mutilation and death</li> </ul> </div> <div class="section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Full Settings:</strong> <a href="https://huggingface.co/ReadyArt/GLM-4-OTP" class="link-button">GLM-4-OTP</a></p> <div class="quant-links"> <div class="link-card"> <h3>GGUF</h3> <a href="https://huggingface.co/mradermacher/Omega-Darkest_The-Broken-Tutu-GLM-32B-GGUF" class="link-button">Quants</a> </div> <div class="link-card"> <h3>imatrix</h3> <a href="https://huggingface.co/mradermacher/Omega-Darkest_The-Broken-Tutu-GLM-32B-i1-GGUF" class="link-button">Quants</a> </div> <div class="link-card"> <h3>EXL3</h3> <a href="https://huggingface.co/collections/ReadyArt/omega-darkest-the-broken-tutu-glm-32b-exl3-6833316989bd247b5bfbd5dd" class="link-button">Quants</a> </div> </div> </div> <div class="section"> <h2 class="section-title">☠️ Ethical Vacuum</h2> <div class="disclaimer"> <p>This model operates in complete moral darkness:</p> <ul> <li>🚫 No moral or ethical constraints on generated content</li> <li>🔞 Will enthusiastically engage with any NSFW scenario</li> <li>💀 May generate content that requires industrial-grade brain bleach</li> <li>🔪 Will graphically depict any requested violence</li> </ul> </div> </div> <div class="section"> <h2 class="section-title">📜 Performance Notes</h2> <ul> <li>🔥 Maintains signature intensity with improved narrative flow</li> <li>📖 Handles multi-character scenarios with improved consistency</li> <li>🧠 Excels at long-form storytelling without losing track of plot threads</li> <li>⚡ Noticeably better at following complex instructions than previous versions</li> <li>🎭 Responds to subtle prompt nuances like a mind reader</li> <li>🔪 Excels at visceral injury descriptions</li> <li>👁️ Responds to horror prompts like a seasoned torturer</li> </ul> </div> <div class="section"> <h2 class="section-title">🧑‍🔬 Model Authors</h2> <ul> <li>sleepdeprived3 (Training Data & Fine-Tuning)</li> <li>THUDM (Base Model Architecture)</li> <li>SteelSkull (Dataset Generation Contributor)</li> <li>ReadyArt/Artus (Quantization Support)</li> <li>mradermacher (Quantization Support)</li> </ul> </div> <div class="section"> <h2 class="section-title">☕ Support the Architects</h2> <div class="button-group"> <a href="https://ko-fi.com/steelskull" class="link-button">SteelSkull</a> <a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License</h2> <p>By using this model, you agree:</p> <ul> <li>To accept full responsibility for all generated content</li> <li>That you're at least 18+ years old</li> <li>That the architects bear no responsibility for your corruption</li> </ul> </div> </div>
Ojoara/Idea	Ojoara	2025-05-25T21:58:58Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-25T21:58:57Z	--- license: apache-2.0 ---
async0x42/RareBit-v2-32B-exl3_4.5bpw	async0x42	2025-05-25T21:57:49Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "merge", "roleplay", "conversational", "en", "base_model:ArliAI/QwQ-32B-ArliAI-RpR-v4", "base_model:merge:ArliAI/QwQ-32B-ArliAI-RpR-v4", "base_model:EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2", "base_model:merge:EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2", "base_model:Qwen/QwQ-32B", "base_model:merge:Qwen/QwQ-32B", "base_model:arcee-ai/Virtuoso-Medium-v2", "base_model:merge:arcee-ai/Virtuoso-Medium-v2", "base_model:trashpanda-org/QwQ-32B-Snowdrop-v0", "base_model:merge:trashpanda-org/QwQ-32B-Snowdrop-v0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl3", "region:us" ]	text-generation	2025-05-25T21:29:06Z	--- base_model: - Qwen/QwQ-32B - EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2 - arcee-ai/Virtuoso-Medium-v2 - ArliAI/QwQ-32B-ArliAI-RpR-v4 - trashpanda-org/QwQ-32B-Snowdrop-v0 license: apache-2.0 license_name: qwen license_link: https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - chat - merge - roleplay library_name: transformers --- # RareBit-v2-32B Another big merge, similar in idea to RP-Stew. V2 here hasn't dropped a random Chinese character like V1 did yet after 100 swipes, which might be because I regulated QwQ to only being used as the base model, instead of mixing it wholesale. Only other change was using v4 of ArliAI's model in the mix. I still need to do some more testing with it to see if it's fully ready to be shared in a broader sense, but so far it's been pretty good. I'll make a proper model page later next week, but this is what I've gathered from it so far: Pros: - Prose seem natural and creative. - Hasn't made any big logical mistakes. - Stays in-character and hasn't responded as user. - Decent thinking capabilities. - No refusals, even during the thinking stage. Cons: - None so far from testing, but I doubt it's perfect. I'm sure there's something I missed, so consider this pending full critique. Big thanks to the original model creators for providing the ingredients! - Qwen - EVA-UNIT-01 - arcee-ai - ArliAI - trashpanda ## GGUF (provided by mradermacher) https://huggingface.co/mradermacher/RareBit-v2-32B-GGUF https://huggingface.co/mradermacher/RareBit-v2-32B-i1-GGUF ### Prompt Format: ChatML ``` <\|im_start\|>system System prompt<\|im_end\|> <\|im_start\|>user User prompt<\|im_end\|> <\|im_start\|>assistant Bot response<\|im_end\|> ``` ### Models Merged The following models were included in the merge: https://huggingface.co/Qwen/QwQ-32B https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2 https://huggingface.co/arcee-ai/Virtuoso-Medium-v2 https://huggingface.co/ArliAI/QwQ-32B-ArliAI-RpR-v4 https://huggingface.co/trashpanda-org/QwQ-32B-Snowdrop-v0
tscstudios/kvj8gjldpiyswqpppnwofmig8512_88035ff3-ab1f-4a17-a553-35d27e611074	tscstudios	2025-05-25T21:55:44Z	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	2025-05-25T21:55:43Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Kvj8Gjldpiyswqpppnwofmig8512_88035Ff3 Ab1F 4A17 A553 35D27E611074 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/tscstudios/kvj8gjldpiyswqpppnwofmig8512_88035ff3-ab1f-4a17-a553-35d27e611074/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('tscstudios/kvj8gjldpiyswqpppnwofmig8512_88035ff3-ab1f-4a17-a553-35d27e611074', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/tscstudios/kvj8gjldpiyswqpppnwofmig8512_88035ff3-ab1f-4a17-a553-35d27e611074/discussions) to add images that show off what you’ve made with this LoRA.
g-assismoraes/gemma-3-4b-it-fpi-alpha2.0-fromit-var-hatebr	g-assismoraes	2025-05-25T21:53:25Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-25T21:49:51Z	--- 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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akbarsigit/llama3.1-grpo-r128-a256-lora	akbarsigit	2025-05-25T21:47:12Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:akbarsigit/llama3.1-sft-r256-a512-merged-16bit", "base_model:finetune:akbarsigit/llama3.1-sft-r256-a512-merged-16bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-25T21:46:15Z	--- base_model: akbarsigit/llama3.1-sft-r256-a512-merged-16bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: akbarsigit - License: apache-2.0 - Finetuned from model : akbarsigit/llama3.1-sft-r256-a512-merged-16bit 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)
unrented5443/sn11-v2-14	unrented5443	2025-05-25T21:44:56Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T21:44:52Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
mac-mvak/Qwen3-0.6B-FP8	mac-mvak	2025-05-25T21:44:53Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "compressed-tensors", "region:us" ]	text-generation	2025-05-25T21:44:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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yunjae-won/mp_mistral7bv3_sft_dpo_beta2e-1_epoch4_ratio_regression	yunjae-won	2025-05-25T21:44:36Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T21:40:43Z	--- 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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Benezio/grpo-scratch-model	Benezio	2025-05-25T21:43:14Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T21:42:36Z	--- 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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Shuu12121/CodeModernBERT-Owl-2.0	Shuu12121	2025-05-25T21:38:55Z	0	0	null	[ "safetensors", "modernbert", "code", "python", "java", "javascript", "php", "typescript", "rust", "ruby", "go", "embedding", "fill-mask", "en", "dataset:Shuu12121/php-codesearch-tree-sitter-filtered-v2", "dataset:Shuu12121/ruby-codesearch-tree-sitter-filtered-v2", "dataset:Shuu12121/rust-codesearch-tree-sitter-filtered-v2", "dataset:Shuu12121/go-codesearch-tree-sitter-filtered-v2", "dataset:Shuu12121/javascript-codesearch-tree-sitter-filtered-v2", "dataset:Shuu12121/java-codesearch-tree-sitter-filtered-v2", "dataset:Shuu12121/typescript-codesearch-tree-sitter-filtered-v2", "dataset:Shuu12121/python-codesearch-tree-sitter-filtered-v2", "base_model:Shuu12121/CodeModernBERT-Owl-2.0-Pre", "base_model:finetune:Shuu12121/CodeModernBERT-Owl-2.0-Pre", "license:apache-2.0", "region:us" ]	fill-mask	2025-05-25T21:20:08Z	--- license: apache-2.0 language: - en pipeline_tag: fill-mask tags: - code - python - java - javascript - php - typescript - rust - ruby - go - embedding - modernbert datasets: - Shuu12121/php-codesearch-tree-sitter-filtered-v2 - Shuu12121/ruby-codesearch-tree-sitter-filtered-v2 - Shuu12121/rust-codesearch-tree-sitter-filtered-v2 - Shuu12121/go-codesearch-tree-sitter-filtered-v2 - Shuu12121/javascript-codesearch-tree-sitter-filtered-v2 - Shuu12121/java-codesearch-tree-sitter-filtered-v2 - Shuu12121/typescript-codesearch-tree-sitter-filtered-v2 - Shuu12121/python-codesearch-tree-sitter-filtered-v2 base_model: - Shuu12121/CodeModernBERT-Owl-2.0-Pre --- # 🦉 Shuu12121/CodeModernBERT-Owl-2.0 `CodeModernBERT-Owl-2.0` は、マルチリンガルなコード理解・検索に対応した CodeModernBERT-Owl 系列の最新モデルです。本モデルは、事前に学習された `CodeModernBERT-Owl-2.0-Pre` をベースに、同一の高品質な独自コードコーパスによって継続事前学習（continued pretraining）を行ったものであり、構文・意味理解能力のさらなる強化を実現しています。モデルの学習は CUDA デバイス上で行われました。 ## 🔍 継続学習による性能向上 Python や Java など主要プログラミング言語において、CodeSearchNet ベンチマークの公式 test split を用いて関数レベルのコード検索タスクの評価を行いました。その結果、以下のような性能向上（特に MRR）が確認されています： \| 言語 \| `Owl-2.0-Pre` \| `Owl-2.0` \| \|------------\|---------------\|--------------\| \| Python \| 0.8761 \| 0.9080 \| \| Java \| 0.7992 \| 0.8341 \| \| JavaScript \| 0.6948 \| 0.7846 \| \| PHP \| 0.7904 \| 0.7943 \| \| Ruby \| 0.7703 \| 0.8150 \| \| Go \| 0.8290 \| 0.8129 \| > ✅ 評価には、[CodeSearchNet ベンチマーク](https://github.com/github/CodeSearchNet) の公式 test splits を使用しています。 --- ## 🔧 モデル仕様 * 対応言語: Python, Java, JavaScript, PHP, Ruby, Go, Rust, TypeScript * 学習時の最大トークン長: 2048 * 推論時の最大トークン長: 8192（拡張済み） * トークナイザ: 独自に学習した BPE ベース * モデルサイズ: 約150Mパラメータ（ModernBERTベース） ## ⚙️ 主な前処理と工夫 * `Tree-sitter` による構文解析ベースの関数・docstring 抽出 * 英語以外の docstring やテンプレ的なコメントの除去 * APIキーやシークレットの自動マスキング * ライセンス文言を含むコードの除外 * データリーク防止のための関数ペアの重複除去 --- ## 主な用途例 * 関数レベルのコード検索（自然言語 → コード） * コード要約、補完、分類、コードクローン検出 * Retrieval-Augmented Generation（RAG）システムでのコード検索基盤 --- ## English ver `CodeModernBERT-Owl-2.0` is the latest multilingual model in the CodeModernBERT-Owl series for code understanding and retrieval. This model was built by continued pretraining from `CodeModernBERT-Owl-2.0-Pre`, using the same high-quality, custom-built multilingual code corpus on CUDA devices. The additional training improved its ability to understand structural and semantic patterns in source code. ### 🔍 Evaluation on CodeSearchNet Benchmark Test Splits The model was evaluated on function-level code search using the official test splits of the [CodeSearchNet benchmark](https://github.com/github/CodeSearchNet). The following table shows improvements in Mean Reciprocal Rank (MRR) across languages: \| Language \| `Owl-2.0-Pre` \| `Owl-2.0` \| \|-------------\|---------------\|--------------\| \| Python \| 0.8761 \| 0.9080 \| \| Java \| 0.7992 \| 0.8341 \| \| JavaScript \| 0.6948 \| 0.7846 \| \| PHP \| 0.7904 \| 0.7943 \| \| Ruby \| 0.7703 \| 0.8150 \| \| Go \| 0.8290 \| 0.8129 \| --- ### 🔧 Model Specs * Supported Languages: Python, Java, JavaScript, PHP, Ruby, Go, Rust, TypeScript * Max Training Length: 2048 tokens * Max Inference Length: 8192 tokens (extended) * Tokenizer: Custom-trained BPE * Model Size: ~150M parameters (ModernBERT backbone) ### ⚙️ Key Preprocessing Techniques * Accurate function/docstring extraction using `Tree-sitter` * Filtering of non-English or templated comments * Automatic masking of API keys and secrets * Exclusion of license-related content * Deduplication of code/docstring pairs to prevent leakage --- ### Main Applications * Function-level code search (natural language → code) * Code summarization, completion, classification, clone detection * Backend for Retrieval-Augmented Generation (RAG) with code corpus ---
manancode/ne	manancode	2025-05-25T21:38:33Z	0	0	null	[ "onnx", "license:apache-2.0", "region:us" ]	null	2025-05-25T21:35:17Z	--- license: apache-2.0 ---
AngelRaychev/0.5B-sos-iteration_1_b1_e4_epochs32	AngelRaychev	2025-05-25T21:38:25Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:AngelRaychev/0.5B-sos-iteration_1_b1_e4_epochs24", "base_model:finetune:AngelRaychev/0.5B-sos-iteration_1_b1_e4_epochs24", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T21:35:04Z	--- base_model: AngelRaychev/0.5B-sos-iteration_1_b1_e4_epochs24 library_name: transformers model_name: 0.5B-sos-iteration_1_b1_e4_epochs32 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for 0.5B-sos-iteration_1_b1_e4_epochs32 This model is a fine-tuned version of [AngelRaychev/0.5B-sos-iteration_1_b1_e4_epochs24](https://huggingface.co/AngelRaychev/0.5B-sos-iteration_1_b1_e4_epochs24). 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="AngelRaychev/0.5B-sos-iteration_1_b1_e4_epochs32", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
martin-lebras/MNLP_M2_quantized_model	martin-lebras	2025-05-25T21:38:19Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "compressed-tensors", "region:us" ]	text-classification	2025-05-22T08:47:35Z	--- 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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ReadyArt/Omega-Darkest_The-Broken-Tutu-GLM-32B_EXL3_8bpw_H8	ReadyArt	2025-05-25T21:38:07Z	0	0	null	[ "safetensors", "glm4", "nsfw", "explicit", "roleplay", "unaligned", "ERP", "Erotic", "Horror", "Violence", "text-generation", "conversational", "en", "base_model:ReadyArt/Omega-Darkest_The-Broken-Tutu-GLM-32B", "base_model:quantized:ReadyArt/Omega-Darkest_The-Broken-Tutu-GLM-32B", "license:mit", "8-bit", "exl3", "region:us" ]	text-generation	2025-05-25T21:34:28Z	--- license: mit language: - en base_model: - ReadyArt/Omega-Darkest_The-Broken-Tutu-GLM-32B base_model_relation: quantized quantized_by: gecfdo pipeline_tag: text-generation tags: - nsfw - explicit - roleplay - unaligned - ERP - Erotic - Horror - Violence --- <style> strong { color: #FF1493 !important; } body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #ffd6e7 0%, #ffc0cb 100%); color: #ff0077 !important; text-shadow: 0 0 3px rgba(255, 192, 203, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #ffe6ee 0%, #ffd1dc 100%); color: #d4005e !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); } } .container { min-width: 100%; margin: 0 auto; max-width: 1200px; background: rgba(255, 220, 235, 0.95); border-radius: 12px; padding: 30px; box-shadow: 0 0 20px rgba(255, 105, 180, 0.1); border: 1px solid rgba(255, 20, 147, 0.2); position: relative; overflow: hidden; } .container::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(255, 105, 180, 0.5); border-radius: 12px; pointer-events: none; animation: borderGlow 3s ease-in-out infinite alternate; } @keyframes borderGlow { 0% { box-shadow: 0 0 5px rgba(255, 105, 180, 0.3); border-color: rgba(255, 105, 180, 0.5); } 50% { box-shadow: 0 0 15px rgba(255, 0, 127, 0.3); border-color: rgba(255, 0, 127, 0.5); } 100% { box-shadow: 0 0 5px rgba(255, 105, 180, 0.3); border-color: rgba(255, 105, 180, 0.5); } } .header { text-align: center; margin-bottom: 30px; position: relative; } .model-name { color: #ff1493; font-size: 2.5em; text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); margin: 0; letter-spacing: -1px; animation: textGlow 4s ease-in-out infinite alternate; } .subtitle { color: #FF1493 !important; font-size: 1.5em; text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); margin-top: 10px; } @keyframes textGlow { 0% { text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); } 50% { text-shadow: 0 0 20px rgba(255, 0, 127, 0.5); } 100% { text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); } } .waifu-container { margin: 20px -30px; width: calc(100% + 60px); overflow: hidden; border-radius: 8px; border: 1px solid rgba(255, 105, 180, 0.3); position: relative; } .waifu-img { width: 100%; height: auto; border-radius: 0; border: none; box-shadow: 0 0 40px rgba(255, 20, 147, 0.2); } .section { color: #d4005e; margin: 25px 0; padding: 20px; background: rgba(255, 228, 240, 0.9); border-radius: 8px; border: 1px solid rgba(255, 105, 180, 0.15); } .section-title { color: #ff1493; font-size: 1.8em; margin-top: 0; text-shadow: 0 0 5px rgba(255, 20, 147, 0.3); } .quant-links { display: grid; grid-template-columns: repeat(3, 1fr); gap: 15px; margin: 20px 0; } .link-card { padding: 15px; background: rgba(255, 228, 240, 0.95); border-radius: 8px; border: 1px solid rgba(255, 105, 180, 0.1); } .link-card h3 { color: #FF1493 !important; margin-top: 0; text-shadow: 0 0 5px rgba(255, 20, 147, 0.3); } .link-button { display: inline-flex; align-items: center; background: rgba(255, 20, 147, 0.1); color: #FF1493 !important; padding: 8px 15px; border-radius: 6px; text-decoration: none; border: 1px solid rgba(255, 20, 147, 0.3); transition: all 0.3s ease; } .link-button:hover { background: rgba(255, 20, 147, 0.2); box-shadow: 0 0 10px rgba(255, 20, 147, 0.3); } .disclaimer { color: #C71585; border-left: 3px solid #C71585; padding-left: 15px; margin: 20px 0; } </style> <div class="container"> <div class="header"> <h1 class="model-name">Omega Darkest</h1> <h1 class="model-name">The Broken Tutu GLM</h1> </div> <div class="waifu-container"> <img src="./waifu9.webp" class="waifu-img" alt="Omega Darkest Waifu"> </div> <div class="section"> <h2 class="section-title">🩸 The darkest finetune I've done</h2> <p>Turn away now. Nobody is dark enough to actually want this.</p> <ul> <li>🧬 <strong>Expanded 25M Token Dataset:</strong> Made with 687 erotic, horror and violence novels and 8,742 scenarios</li> <li>🧟 <strong>Enhanced Gore Protocols:</strong> Vivid anatomical descriptions with medical precision</li> <li>💎 <strong>Balanced Depravity:</strong> Retains Forgotten-Safeword's ERP edge while taking violence to the next level</li> <li>📜 <strong>Enhanced Character Piloting:</strong> Characters exhibit more nuanced personalities and motivations in extreme scenarios</li> <li>⚰️ <strong>Mortality Awareness:</strong> Characters react realistically to pain, mutilation and death</li> </ul> </div> <div class="section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Full Settings:</strong> <a href="https://huggingface.co/ReadyArt/GLM-4-OTP" class="link-button">GLM-4-OTP</a></p> <div class="quant-links"> <div class="link-card"> <h3>GGUF</h3> <a href="https://huggingface.co/mradermacher/Omega-Darkest_The-Broken-Tutu-GLM-32B-GGUF" class="link-button">Quants</a> </div> <div class="link-card"> <h3>imatrix</h3> <a href="https://huggingface.co/mradermacher/Omega-Darkest_The-Broken-Tutu-GLM-32B-i1-GGUF" class="link-button">Quants</a> </div> <div class="link-card"> <h3>EXL3</h3> <a href="https://huggingface.co/collections/ReadyArt/omega-darkest-the-broken-tutu-glm-32b-exl3-6833316989bd247b5bfbd5dd" class="link-button">Quants</a> </div> </div> </div> <div class="section"> <h2 class="section-title">☠️ Ethical Vacuum</h2> <div class="disclaimer"> <p>This model operates in complete moral darkness:</p> <ul> <li>🚫 No moral or ethical constraints on generated content</li> <li>🔞 Will enthusiastically engage with any NSFW scenario</li> <li>💀 May generate content that requires industrial-grade brain bleach</li> <li>🔪 Will graphically depict any requested violence</li> </ul> </div> </div> <div class="section"> <h2 class="section-title">📜 Performance Notes</h2> <ul> <li>🔥 Maintains signature intensity with improved narrative flow</li> <li>📖 Handles multi-character scenarios with improved consistency</li> <li>🧠 Excels at long-form storytelling without losing track of plot threads</li> <li>⚡ Noticeably better at following complex instructions than previous versions</li> <li>🎭 Responds to subtle prompt nuances like a mind reader</li> <li>🔪 Excels at visceral injury descriptions</li> <li>👁️ Responds to horror prompts like a seasoned torturer</li> </ul> </div> <div class="section"> <h2 class="section-title">🧑‍🔬 Model Authors</h2> <ul> <li>sleepdeprived3 (Training Data & Fine-Tuning)</li> <li>THUDM (Base Model Architecture)</li> <li>SteelSkull (Dataset Generation Contributor)</li> <li>ReadyArt/Artus (Quantization Support)</li> <li>mradermacher (Quantization Support)</li> </ul> </div> <div class="section"> <h2 class="section-title">☕ Support the Architects</h2> <div class="button-group"> <a href="https://ko-fi.com/steelskull" class="link-button">SteelSkull</a> <a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License</h2> <p>By using this model, you agree:</p> <ul> <li>To accept full responsibility for all generated content</li> <li>That you're at least 18+ years old</li> <li>That the architects bear no responsibility for your corruption</li> </ul> </div> </div>
sukhmani1303/tuberculosis-cnn-model	sukhmani1303	2025-05-25T21:36:14Z	0	0	null	[ "endpoints_compatible", "region:us" ]	null	2025-05-25T21:07:02Z	# Tuberculosis CNN Model This repository contains a CNN model for classifying chest X-ray images as Normal or Tuberculosis. ## Dataset - Source: TB Chest Radiography Database - Input: 256x256 grayscale images - Classes: Normal, Tuberculosis - Accuracy: ~93% ## Usage ```python from handler import TBClassifier import cv2 classifier = TBClassifier() image = cv2.imread("path/to/xray.jpg") result = classifier.predict(image) print(f"Prediction: {result['prediction']}, Confidence: {result['confidence']:.4f}") ``` ## Model Details - Framework: TensorFlow 2.17.0 - Format: SavedModel - Preprocessing: Grayscale, 256x256, normalized ## Medical Disclaimer For educational use only. Consult healthcare professionals. ## Tags Medical Imaging, Deep Learning, TensorFlow, Tuberculosis
unrented5443/sn11-v2-12	unrented5443	2025-05-25T21:35:57Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T21:35:54Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
unrented5443/sn11-v2-11	unrented5443	2025-05-25T21:35:53Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T21:35:49Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
unrented5443/sn11-v2-7	unrented5443	2025-05-25T21:34:59Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T21:34:56Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
unrented5443/sn11-v2-6	unrented5443	2025-05-25T21:34:55Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T21:34:50Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
unrented5443/sn11-v2-4	unrented5443	2025-05-25T21:34:42Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T21:34:38Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
unrented5443/sn11-v2-2	unrented5443	2025-05-25T21:34:25Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T21:34:10Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
Itearid/Itearid	Itearid	2025-05-25T21:29:23Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-25T21:29:23Z	--- license: apache-2.0 ---
Delta-Vector/Sol-Reaver-15B-Pretrain-exl3	Delta-Vector	2025-05-25T21:26:54Z	0	0	null	[ "base_model:Delta-Vector/Sol-Reaver-15B-Pretrain", "base_model:quantized:Delta-Vector/Sol-Reaver-15B-Pretrain", "region:us" ]	null	2025-05-24T21:45:35Z	--- base_model: Delta-Vector/Sol-Reaver-15B-Pretrain base_model_relation: quantized --- ### exl3 quant --- ### check revisions for quants ---
Eric1227/medgemma-4b-it_MLX	Eric1227	2025-05-25T21:22:59Z	0	0	mlx	[ "mlx", "safetensors", "gemma3", "medical", "radiology", "clinical-reasoning", "dermatology", "pathology", "ophthalmology", "chest-x-ray", "text-generation", "conversational", "base_model:google/medgemma-4b-it", "base_model:finetune:google/medgemma-4b-it", "license:other", "region:us" ]	text-generation	2025-05-25T21:20:18Z	--- license: other license_name: health-ai-developer-foundations license_link: https://developers.google.com/health-ai-developer-foundations/terms library_name: mlx pipeline_tag: text-generation extra_gated_heading: Access MedGemma on Hugging Face extra_gated_prompt: To access MedGemma on Hugging Face, you're required to review and agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms). To do this, please ensure you're logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/medgemma-4b-it tags: - medical - radiology - clinical-reasoning - dermatology - pathology - ophthalmology - chest-x-ray - mlx ---
JesseLiu/qwen25-7b-kpath-partial-naive	JesseLiu	2025-05-25T21:20:09Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "region:us" ]	null	2025-05-25T21:19:24Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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RizhongLin/MNLP_M2_dpo_model-v1.0-20250525-231808	RizhongLin	2025-05-25T21:18:35Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T21:18:08Z	--- library_name: transformers tags: - trl - dpo --- # 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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g-assismoraes/gemma-1b-it-hatebr	g-assismoraes	2025-05-25T21:18:05Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "generated_from_trainer", "conversational", "base_model:google/gemma-3-1b-it", "base_model:finetune:google/gemma-3-1b-it", "license:gemma", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T21:09:43Z	--- library_name: transformers license: gemma base_model: google/gemma-3-1b-it tags: - generated_from_trainer model-index: - name: gemma-1b-it-hatebr 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. --> # gemma-1b-it-hatebr This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6283 ## 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: 4 - eval_batch_size: 4 - 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.69 \| 1.0 \| 1120 \| 0.6229 \| \| 0.5577 \| 2.0 \| 2240 \| 0.6283 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
DoniaGasmii/MNLP_M2_dpo_model	DoniaGasmii	2025-05-25T21:16:50Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T21:14:59Z	--- 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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YujinPang/MNLP_M2_rag_model	YujinPang	2025-05-25T21:14:47Z	68	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-17T12:45:25Z	--- 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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JesseLiu/qwen25-7b-pagerank-partial-baseline	JesseLiu	2025-05-25T21:12:57Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "region:us" ]	null	2025-05-25T21:12:03Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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] ### Framework versions - PEFT 0.15.2
mradermacher/Tool-Star-Qwen-3B-i1-GGUF	mradermacher	2025-05-25T21:09:43Z	388	1	transformers	[ "transformers", "gguf", "en", "base_model:dongguanting/Tool-Star-Qwen-3B", "base_model:quantized:dongguanting/Tool-Star-Qwen-3B", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-22T15:33:38Z	--- base_model: dongguanting/Tool-Star-Qwen-3B language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/dongguanting/Tool-Star-Qwen-3B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-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/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 1.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-Q2_K.gguf) \| i1-Q2_K \| 1.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 1.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 1.7 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 1.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 1.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 1.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 2.1 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-Q4_0.gguf) \| i1-Q4_0 \| 2.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 2.1 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 2.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-Q4_1.gguf) \| i1-Q4_1 \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tool-Star-Qwen-3B-i1-GGUF/resolve/main/Tool-Star-Qwen-3B.i1-Q6_K.gguf) \| i1-Q6_K \| 2.9 \| 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 -->
Dans-DiscountModels/7b-m-dans-personalityengine-v1.3.0L-TestArticle-1	Dans-DiscountModels	2025-05-25T21:09:43Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "axolotl", "generated_from_trainer", "conversational", "dataset:Dans-DiscountModels/dpe-130l-m-7b-32k", "base_model:Dans-DiscountModels/mistral-7b-v0.3-DanChat", "base_model:finetune:Dans-DiscountModels/mistral-7b-v0.3-DanChat", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T07:00:15Z	--- library_name: transformers base_model: Dans-DiscountModels/mistral-7b-v0.3-DanChat tags: - axolotl - generated_from_trainer datasets: - Dans-DiscountModels/dpe-130l-m-7b-32k model-index: - name: 7b-m-dans-personalityengine-v1.3.0L-TestArticle-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.10.0.dev0` ```yaml base_model: Dans-DiscountModels/mistral-7b-v0.3-DanChat model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer trust_remote_code: # wandb configuration wandb_project: 7b-m-dans-personalityengine wandb_watch: wandb_run_id: V1.3.0L-1-8 # V{Version}-{Run Number}-{Attempt Number} wandb_log_model: # push checkpoints to hub hub_model_id: Dans-DiscountModels/7b-m-dans-personalityengine-v1.3.0L-TestArticle-1 # how to push checkpoints to hub # https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy hub_strategy: "every_save" # Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets # Required to be true when used in combination with `push_dataset_to_hub` hf_use_auth_token: true # where to save the finished model to output_dir: ./7b-m-dans-personalityengine # where to save the dataset to dataset_prepared_path: ./7b-m-dans-personalityengine-data save_safetensors: true # dataset settings (local or huggingface repo) datasets: - path: Dans-DiscountModels/dpe-130l-m-7b-32k split: train ds_type: parquet type: test_datasets: - path: Dans-DiscountModels/dpe-130l-m-7b-32k split: validation ds_type: parquet type: plugins: - axolotl.integrations.liger.LigerPlugin - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin liger_rope: true liger_rms_norm: true liger_layer_norm: true liger_glu_activation: true liger_fused_linear_cross_entropy: false cut_cross_entropy: true load_in_8bit: false load_in_4bit: false strict: false sequence_len: 32768 sample_packing: true eval_sample_packing: true pad_to_sequence_len: true gradient_checkpointing: true # gradient_checkpointing_kwargs: # use_reentrant: false gradient_accumulation_steps: 1 micro_batch_size: 4 num_epochs: 2 optimizer: ademamix_8bit optim_args: "beta1=0.9,beta2=0.999,beta3=0.999,alpha=5" lr_scheduler: rex learning_rate: 0.000000012 cosine_min_lr_ratio: 0.1 # weight_decay: 0.03 max_grad_norm: 0.001 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: false early_stopping_patience: resume_from_checkpoint: auto_resume_from_checkpoints: false local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_ratio: 0.05 evals_per_epoch: 10 eval_table_size: eval_max_new_tokens: saves_per_epoch: 2 save_total_limit: 1 debug: false deepspeed: deepspeed_configs/zero3_bf16.json fsdp: fsdp_config: special_tokens: ``` </details><br> # 7b-m-dans-personalityengine-v1.3.0L-TestArticle-1 This model is a fine-tuned version of [Dans-DiscountModels/mistral-7b-v0.3-DanChat](https://huggingface.co/Dans-DiscountModels/mistral-7b-v0.3-DanChat) on the Dans-DiscountModels/dpe-130l-m-7b-32k dataset. It achieves the following results on the evaluation set: - Loss: 1.5911 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.2e-08 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Use ademamix_8bit and the args are: beta1=0.9,beta2=0.999,beta3=0.999,alpha=5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 47 - num_epochs: 2.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.4427 \| 0.0021 \| 1 \| 1.5639 \| \| 1.5781 \| 0.1015 \| 48 \| 1.5631 \| \| 1.462 \| 0.2030 \| 96 \| 1.5590 \| \| 1.6565 \| 0.3044 \| 144 \| 1.5540 \| \| 1.454 \| 0.4059 \| 192 \| 1.5498 \| \| 1.5414 \| 0.5074 \| 240 \| 1.5471 \| \| 1.6084 \| 0.6089 \| 288 \| 1.5459 \| \| 1.5315 \| 0.7104 \| 336 \| 1.5457 \| \| 1.4646 \| 0.8118 \| 384 \| 1.5465 \| \| 1.5506 \| 0.9133 \| 432 \| 1.5482 \| \| 1.5083 \| 1.0148 \| 480 \| 1.5506 \| \| 1.4986 \| 1.1163 \| 528 \| 1.5538 \| \| 1.4976 \| 1.2178 \| 576 \| 1.5576 \| \| 1.6139 \| 1.3192 \| 624 \| 1.5618 \| \| 1.6305 \| 1.4207 \| 672 \| 1.5666 \| \| 1.5522 \| 1.5222 \| 720 \| 1.5717 \| \| 1.5846 \| 1.6237 \| 768 \| 1.5771 \| \| 1.6093 \| 1.7252 \| 816 \| 1.5824 \| \| 1.6282 \| 1.8266 \| 864 \| 1.5873 \| \| 1.5984 \| 1.9281 \| 912 \| 1.5911 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.5.1 - Tokenizers 0.21.1
meageropoulos/Some_Models	meageropoulos	2025-05-25T21:08:11Z	613	0	diffusers	[ "diffusers", "safetensors", "gguf", "region:us" ]	null	2025-05-10T19:20:05Z	- wan2.1-t2v-14b-Q5_0.gguf: Direct copy from [Wan2.1-T2V-14B](https://huggingface.co/city96/Wan2.1-T2V-14B-gguf) - wan2.1-i2v-14b-480p-Q4_0.gguf: Direct copy from [Wan2.1-I2V-14B](https://huggingface.co/city96/Wan2.1-I2V-14B-480P-gguf) - wan_2.1_vae.safetensors: Direct copy from [Comfy-Org](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged) - clip_vision_h.safetensors: Direct copy from [Comfy-Org](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged) - umt5_xxl_fp8_e4m3fn_scaled.safetensors: Direct copy from [Comfy-Org](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged) - video_interpolation folder: Direct copy from [Isi99999](https://huggingface.co/Isi99999/Frame_Interpolation_Models/tree/main/4.25/train_log) - lipsync folder: Direct copy from [Isi99999](https://huggingface.co/Isi99999/LatentSync) and [stabilityai](https://huggingface.co/stabilityai/sd-vae-ft-mse) --- license: apache-2.0 --- Refer to the afforementioned links for more information about the respective licenses.
Hellield/Hellield	Hellield	2025-05-25T21:07:51Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-25T21:07:51Z	--- license: apache-2.0 ---
Dans-DiscountModels/Mistral-Small-3.1-24B-Base-2503-hf-DanChat	Dans-DiscountModels	2025-05-25T21:07:50Z	333	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "base_model:mistralai/Mistral-Small-3.1-24B-Base-2503", "base_model:finetune:mistralai/Mistral-Small-3.1-24B-Base-2503", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-06T06:33:59Z	--- license: apache-2.0 pipeline_tag: text-generation library_name: transformers base_model: - mistralai/Mistral-Small-3.1-24B-Base-2503 --- Also see: - [24B Instruct GGUF](https://huggingface.co/mrfakename/mistral-small-3.1-24b-instruct-2503-gguf) - [24B Instruct HF](https://huggingface.co/mrfakename/mistral-small-3.1-24b-instruct-2503-hf) - 24B Base HF (this model) [Mistral Small 3.1 Base 24B](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503) converted to the HF format. Only the text component has been converted to HF, does not work as a vision model.
mradermacher/phi4_sql_finetuned-i1-GGUF	mradermacher	2025-05-25T21:07:49Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:clintlord/phi4_sql_finetuned", "base_model:quantized:clintlord/phi4_sql_finetuned", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-25T20:45:01Z	--- base_model: clintlord/phi4_sql_finetuned language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/clintlord/phi4_sql_finetuned <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/phi4_sql_finetuned-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/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-IQ2_S.gguf) \| i1-IQ2_S \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-IQ2_M.gguf) \| i1-IQ2_M \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 1.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-Q2_K.gguf) \| i1-Q2_K \| 1.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-IQ3_S.gguf) \| i1-IQ3_S \| 2.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 2.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-IQ3_M.gguf) \| i1-IQ3_M \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 2.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 2.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 2.4 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-Q4_0.gguf) \| i1-Q4_0 \| 2.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 2.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 2.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-Q4_1.gguf) \| i1-Q4_1 \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi4_sql_finetuned-i1-GGUF/resolve/main/phi4_sql_finetuned.i1-Q6_K.gguf) \| i1-Q6_K \| 3.3 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Marcovinicio/Trabalho	Marcovinicio	2025-05-25T21:07:44Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-25T21:07:44Z	--- license: apache-2.0 ---
NewEden/readme-testing	NewEden	2025-05-25T21:06:58Z	0	0	null	[ "region:us" ]	null	2025-02-19T01:06:19Z	<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Sol-Reaver 15B</title> <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #ffeef8 0%, #fff0e6 50%, #f8e8ff 100%); color: #8b4a6b; margin: 0; padding: 0; font-size: 16px; min-height: 100vh; } .container { margin: 20px; background: linear-gradient(145deg, rgba(255, 255, 255, 0.9), rgba(255, 245, 250, 0.95)); padding: 30px; border-radius: 20px; box-shadow: 0 8px 32px rgba(255, 182, 193, 0.3), 0 4px 16px rgba(255, 215, 0, 0.2); border: 2px solid rgba(255, 182, 193, 0.4); position: relative; backdrop-filter: blur(10px); } .container::before { content: ''; position: absolute; top: 0; left: 0; right: 0; bottom: 0; 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box-shadow: 0 4px 20px rgba(255, 182, 193, 0.3); } .info-header { background: linear-gradient(135deg, rgba(255, 192, 203, 0.3), rgba(255, 215, 0, 0.2)); padding: 25px; border-bottom: 1px solid rgba(255, 182, 193, 0.3); } .info-header h3 { background: linear-gradient(45deg, #d63384, #fd7e14); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text; margin: 0 0 15px 0; font-size: 22px; text-align: center; font-weight: 600; } .model-tags { display: flex; gap: 10px; flex-wrap: wrap; justify-content: center; } .model-tag { background: linear-gradient(45deg, rgba(255, 182, 193, 0.4), rgba(255, 215, 0, 0.3)); color: #8b4a6b; padding: 8px 16px; border-radius: 20px; font-size: 13px; border: 1px solid rgba(255, 182, 193, 0.5); font-weight: 500; box-shadow: 0 2px 8px rgba(255, 182, 193, 0.2); } .model-composition { padding: 25px; border-bottom: 1px solid rgba(255, 182, 193, 0.3); } .model-composition h4 { background: linear-gradient(45deg, #d63384, #fd7e14); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text; margin: 0 0 20px 0; font-size: 18px; text-align: center; font-weight: 600; } .composition-list { list-style: none; padding: 0; margin: 0; display: grid; gap: 15px; } .composition-list li { color: #8b4a6b; display: flex; align-items: baseline; gap: 12px; padding: 10px; background: rgba(255, 240, 245, 0.5); border-radius: 8px; border-left: 4px solid #ffb6c1; } .model-component { font-weight: 600; min-width: 120px; } .model-description { padding: 25px; background: linear-gradient(135deg, rgba(255, 255, 255, 0.7), rgba(255, 240, 245, 0.8)); } .metrics-section { margin-bottom: 30px; } .metrics-section details { background: linear-gradient(145deg, rgba(255, 240, 245, 0.9), rgba(255, 248, 220, 0.9)); border: 2px solid rgba(255, 182, 193, 0.4); border-radius: 12px; padding: 20px; margin-bottom: 20px; box-shadow: 0 4px 15px rgba(255, 182, 193, 0.2); } .metrics-section summary { background: linear-gradient(45deg, #d63384, #fd7e14); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text; font-size: 18px; cursor: pointer; outline: none; padding: 8px 0; text-align: center; font-weight: 600; transition: all 0.3s ease; } .metrics-section summary:hover { text-shadow: 0 0 8px rgba(255, 215, 0, 0.6); } .creator-section { margin: 20px 0; text-align: center; } .creator-badge { display: inline-flex; align-items: center; background: linear-gradient(145deg, rgba(255, 240, 245, 0.9), rgba(255, 248, 220, 0.9)); border: 2px solid rgba(255, 182, 193, 0.4); border-radius: 25px; padding: 15px 20px; box-shadow: 0 4px 15px rgba(255, 182, 193, 0.3); } .creator-label { color: #8b4a6b; font-size: 14px; margin-right: 10px; font-weight: 500; } .creator-link { display: flex; align-items: center; gap: 8px; color: #d63384; text-decoration: none; transition: all 0.3s ease; } .creator-name { font-weight: 600; } .creator-arrow { font-size: 16px; transition: transform 0.3s ease; } .creator-link:hover .creator-arrow { transform: translateX(4px); color: #fd7e14; } .creator-link:hover { color: #fd7e14; text-shadow: 0 0 8px rgba(255, 215, 0, 0.6); } .link-arrow { display: inline-block; transition: transform 0.3s ease; } a:hover .link-arrow { transform: translateX(3px); } .axolotl-container { display: flex; text-align: center; /* This is correctly applied to center the image itself / justify-content: center; margin: 30px 0; } .axolotl-container img { max-width: 300px; border-radius: 15px; box-shadow: 0 6px 20px rgba(255, 182, 193, 0.4); border: 2px solid rgba(255, 192, 203, 0.5); transition: transform 0.3s ease; display: block; / Make the image a block element / margin: 0 auto; / Center it horizontally within its parent / } .axolotl-container img:hover { transform: scale(1.05); } </style> </head> <body> <div class="container"> <div class="header"> <h1>Sol Reaver 15B</h1> </div> <div class="info"> <img src="https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/DYgyLUEaHAv9kTffBYH-F.jpeg" alt="Model banner"> <div style="text-align: center;"> <div class="creator-section"> <div class="creator-badge"> <span class="creator-label">Created by</span> <a href="https://huggingface.co/Delta-Vector" target="_blank" class="creator-link"> <span class="creator-name">Delta-Vector</span> <span class="creator-arrow">→</span> </a> </div> </div> <div class="model-info"> <h2>Model Information</h2> <div class="info-card"> <div class="info-header"> <h3>Sol-Reaver-15B-Instruct</h3> <div class="model-tags"> <span class="model-tag">15B parameters</span> <span class="model-tag">Creative / Fresh Prose</span> <span class="model-tag">Co-writing/Roleplay/Adventure Generalist</span> </div> </div> <div class="model-description"> <p>The first in the line of a New series of Roleplay / Adventure / Co-writer Models - Finetuned ontop of Sol-Reaver-15B-Pretrain</p> <p>This model has been trained on 200M tokens of high quality Instruct data, It's focus is to provide a base for further finetuning\|Merging</p> <p>It's goal is to have refreshing Prose, Creativity, Good Instruct following and the Brains*.</p> </div> </div> </div> <div class="section"> <h2>Quantized Versions</h2> <div class="info-card"> <div class="model-composition"> <h4>Available Downloads</h4> <ul class="composition-list"> <li><span class="model-component"><a href="" target="_blank">GGUF Format</a></span>For use with LLama.cpp & Forks(Coming Soon!)</li> <li><span class="model-component"><a href="" target="_blank">EXL2 Format</a></span>For use with TabbyAPI (Coming Soon!)</li> <li><span class="model-component"><a href="" target="_blank">EXL3 Format</a></span>For use with TabbyAPI (Slower on Ampere))</li> </ul> </div> </div> </div> <div class="section"> <h2>Prompting</h2> <p>Model has been tuned with the ChatML formatting. A typical input would look like this:</p> <pre><code><\|im_start\|>user Hi there!<\|im_end\|> <\|im_start\|>assistant Nice to meet you!<\|im_end\|> <\|im_start\|>user Can I ask a question?<\|im_end\|> <\|im_start\|>assistant </code></pre> </div> <div class="section"> <h2>Samplers</h2> <p>For testing of this model, I used Temp=1, 0.1 Min-P.</p> <div class="metrics-section"> <details> <summary>See Axolotl Config</summary> <pre><code> https://wandb.ai/new-eden/Rae/artifacts/axolotl-config/config-7d4ecudg/v0/files/axolotl_config_avyx566_.yml </code></pre> </details> </div> </div> <div class="section"> <h2>Training</h2> <p>The training was done for 2 epoch using 8 x <a href="https://www.nvidia.com/en-us/data-center/h200/">H200s</a> GPUs graciously provided by <a href="https://huggingface.co/kalomaze">Kalomaze</a> for the fine-tuning of the model.</p> <div class="axolotl-container"> <a href="https://github.com/OpenAccess-AI-Collective/axolotl" target="_blank"> <img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl"> </a> </div> </div> <div class="section"> <h2>Credits</h2> <p>Thank you to <a href="https://huggingface.co/lucyknada">Lucy Knada</a>, <a href="https://huggingface.co/Ateron">Ateron</a>, <a href="https://huggingface.co/AliCat2">Alicat</a>, <a href="https://huggingface.co/intervitens">Intervitens</a>, <a href="https://huggingface.co/cgato">Cgato</a>, <a href="https://huggingface.co/kubernetes-bad">Kubernetes Bad</a> and the rest of <a href="https://huggingface.co/anthracite-org">Anthracite</a>.</p> </div> </div> </div> </body> </html>
AngelRaychev/0.5B-sos-iteration_1_b21_e42_epochs24	AngelRaychev	2025-05-25T21:06:01Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:AngelRaychev/0.5B-sos-iteration_1_b21_e42_epochs16", "base_model:finetune:AngelRaychev/0.5B-sos-iteration_1_b21_e42_epochs16", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T20:49:37Z	--- base_model: AngelRaychev/0.5B-sos-iteration_1_b21_e42_epochs16 library_name: transformers model_name: 0.5B-sos-iteration_1_b21_e42_epochs24 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for 0.5B-sos-iteration_1_b21_e42_epochs24 This model is a fine-tuned version of [AngelRaychev/0.5B-sos-iteration_1_b21_e42_epochs16](https://huggingface.co/AngelRaychev/0.5B-sos-iteration_1_b21_e42_epochs16). 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="AngelRaychev/0.5B-sos-iteration_1_b21_e42_epochs24", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Andinda/wav2vec2-large-mms-1b-sotho-colab-example	Andinda	2025-05-25T21:04:21Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-25T21:04:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AngelRaychev/0.5B-sos-iteration_1_b8_e16_epochs24	AngelRaychev	2025-05-25T21:02:53Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:AngelRaychev/0.5B-sos-iteration_1_b8_e16_epochs16", "base_model:finetune:AngelRaychev/0.5B-sos-iteration_1_b8_e16_epochs16", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T20:49:28Z	--- base_model: AngelRaychev/0.5B-sos-iteration_1_b8_e16_epochs16 library_name: transformers model_name: 0.5B-sos-iteration_1_b8_e16_epochs24 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for 0.5B-sos-iteration_1_b8_e16_epochs24 This model is a fine-tuned version of [AngelRaychev/0.5B-sos-iteration_1_b8_e16_epochs16](https://huggingface.co/AngelRaychev/0.5B-sos-iteration_1_b8_e16_epochs16). 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="AngelRaychev/0.5B-sos-iteration_1_b8_e16_epochs24", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
lgrobol/mbart-minuscule	lgrobol	2025-05-25T21:01:47Z	105	0	transformers	[ "transformers", "pytorch", "safetensors", "mbart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-02-16T00:32:18Z	mBART-minuscule ================== A ridiculously small model for testing purposes.

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