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train · 847k rows

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MikeRoz/TheDrummer_Fallen-Gemma3-27B-v1-8.0bpw-h8-exl2	MikeRoz	2025-04-28T23:29:09Z	0	0	null	[ "safetensors", "gemma3_text", "exl2", "license:other", "8-bit", "region:us" ]	null	2025-04-28T21:41:21Z	--- license: other base_model: TheDrummer/Fallen-Gemma3-27b-v1 base_model_relation: quantized tags: - exl2 --- This model was quantized using commit 3a90264 of the dev branch of exllamav2. The Gemma 3 8k context bug looks to be thoroughly squashed as of this commit. To use this model, please either build your own copy of exllamav2 from the dev branch, or wait for the forthcoming v0.2.9 release. The original model can be found [here](https://huggingface.co/TheDrummer/Fallen-Gemma3-27B-v1). # Join our Discord! https://discord.gg/Nbv9pQ88Xb ## Nearly 5000 members of helpful, LLM enthusiasts! A hub for players and makers alike! --- [BeaverAI](https://huggingface.co/BeaverAI) proudly presents... # Fallen Gemma3 27B v1 👺 ![image/gif](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/9oyZxzpfhmmNr21S1P_iJ.gif) ## Special Thanks - Thank you to each and everyone who donated and subscribed in [Patreon](https://www.patreon.com/TheDrummer) and [Ko-Fi](https://ko-fi.com/thedrummer) to make our venture a little bit easier. - I'm also recently unemployed. I am a Software Developer with 8 years of experience in Web, API, AI, and adapting to new tech and requirements. If you're hiring, feel free to reach out to me however. ## Usage - Use Gemma Chat Template ## Description Fallen Gemma3 27B v1 is an evil tune of Gemma 3 27B but it is not a complete decensor. Evil tunes knock out the positivity and may enjoy torturing you and humanity. Vision still works and it has something to say about the crap you feed it. ## Links - Original: https://huggingface.co/TheDrummer/Fallen-Gemma3-27B-v1 - GGUF: https://huggingface.co/TheDrummer/Fallen-Gemma3-27B-v1-GGUF - iMatrix (recommended): https://huggingface.co/bartowski/TheDrummer_Fallen-Gemma3-27B-v1-GGUF `config-v1c`
mlfoundations-dev/d1_science_all_1k	mlfoundations-dev	2025-04-28T17:26:28Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T15:58:58Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_science_all_1k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # d1_science_all_1k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_science_all_1k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
PriyankAnantha/my-finetuned-torgo-model-full	PriyankAnantha	2025-04-28T16:39:54Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T16:34:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
DreadPoor/signal_test	DreadPoor	2025-04-28T15:48:44Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:Delta-Vector/Rei-12B", "base_model:merge:Delta-Vector/Rei-12B", "base_model:DreadPoor/Irix-12B-Model_Stock", "base_model:merge:DreadPoor/Irix-12B-Model_Stock", "base_model:DreadPoor/YM-12B-Model_Stock", "base_model:merge:DreadPoor/YM-12B-Model_Stock", "base_model:grimjim/magnum-twilight-12b", "base_model:merge:grimjim/magnum-twilight-12b", "base_model:redrix/GodSlayer-12B-ABYSS", "base_model:merge:redrix/GodSlayer-12B-ABYSS", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T15:42:15Z	--- base_model: - redrix/GodSlayer-12B-ABYSS - grimjim/magnum-twilight-12b - DreadPoor/YM-12B-Model_Stock - DreadPoor/Irix-12B-Model_Stock - Delta-Vector/Rei-12B library_name: transformers tags: - mergekit - merge --- # merge 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 [redrix/GodSlayer-12B-ABYSS](https://huggingface.co/redrix/GodSlayer-12B-ABYSS) as a base. ### Models Merged The following models were included in the merge: * [grimjim/magnum-twilight-12b](https://huggingface.co/grimjim/magnum-twilight-12b) * [DreadPoor/YM-12B-Model_Stock](https://huggingface.co/DreadPoor/YM-12B-Model_Stock) * [DreadPoor/Irix-12B-Model_Stock](https://huggingface.co/DreadPoor/Irix-12B-Model_Stock) * [Delta-Vector/Rei-12B](https://huggingface.co/Delta-Vector/Rei-12B) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: redrix/GodSlayer-12B-ABYSS models: - model: DreadPoor/Irix-12B-Model_Stock - model: DreadPoor/YM-12B-Model_Stock - model: grimjim/magnum-twilight-12b - model: Delta-Vector/Rei-12B merge_method: model_stock dtype: bfloat16 parameters: normalize: false tokenizer: source: union ```
ThuraAung1601/speecht5_for_thai_with_ipa_tts_v3	ThuraAung1601	2025-04-28T15:06:42Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "th", "dataset:ThuraAung1601/thai-processed-voice-th-169k-with-ipa", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2025-04-27T19:12:26Z	--- library_name: transformers language: - th license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer datasets: - ThuraAung1601/thai-processed-voice-th-169k-with-ipa model-index: - name: SpeechT5-TTS with IPA v3 for Thai 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. --> # SpeechT5-TTS with IPA v3 for Thai This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Processed Thai Speech Data dataset. It achieves the following results on the evaluation set: - Loss: 0.4533 ## 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: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.5184 \| 1.0 \| 3935 \| 0.4867 \| \| 0.4998 \| 2.0 \| 7870 \| 0.4710 \| \| 0.4895 \| 3.0 \| 11805 \| 0.4635 \| \| 0.4869 \| 4.0 \| 15740 \| 0.4553 \| \| 0.4764 \| 5.0 \| 19675 \| 0.4533 \| ### Framework versions - Transformers 4.51.1 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0
dzanbek/ea5a3b64-e495-4fc7-80c6-2b9e9c35310e	dzanbek	2025-04-28T11:59:29Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:tiiuae/Falcon3-1B-Base", "base_model:adapter:tiiuae/Falcon3-1B-Base", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T11:55:32Z	--- library_name: peft license: other base_model: tiiuae/Falcon3-1B-Base tags: - axolotl - generated_from_trainer model-index: - name: ea5a3b64-e495-4fc7-80c6-2b9e9c35310e 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: tiiuae/Falcon3-1B-Base bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 6cc0fe21f0332fa7_train_data.json ds_type: json format: custom path: /workspace/input_data/6cc0fe21f0332fa7_train_data.json type: field_instruction: question field_output: answer format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: dzanbek/ea5a3b64-e495-4fc7-80c6-2b9e9c35310e hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/6cc0fe21f0332fa7_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: 72923dc5-5ef8-423a-919a-0a486181f7ff wandb_project: s56-2 wandb_run: your_name wandb_runid: 72923dc5-5ef8-423a-919a-0a486181f7ff warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # ea5a3b64-e495-4fc7-80c6-2b9e9c35310e This model is a fine-tuned version of [tiiuae/Falcon3-1B-Base](https://huggingface.co/tiiuae/Falcon3-1B-Base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6014 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5658 \| 0.2128 \| 200 \| 0.6014 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
shibajustfor/67991584-5a18-4ac1-ab7d-a4c7465caf19	shibajustfor	2025-04-28T11:41:29Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:Orenguteng/Llama-3-8B-Lexi-Uncensored", "base_model:adapter:Orenguteng/Llama-3-8B-Lexi-Uncensored", "region:us" ]	null	2025-04-28T11:41:04Z	--- library_name: peft tags: - generated_from_trainer base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored model-index: - name: shibajustfor/67991584-5a18-4ac1-ab7d-a4c7465caf19 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. --> # shibajustfor/67991584-5a18-4ac1-ab7d-a4c7465caf19 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0255 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
mradermacher/PR2-14B-Instruct-GGUF	mradermacher	2025-04-28T10:45:02Z	57	1	transformers	[ "transformers", "gguf", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:qingy2024/PR2-SFT", "base_model:qingy2024/PR2-14B-Instruct", "base_model:quantized:qingy2024/PR2-14B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-07T00:07:40Z	--- base_model: qingy2024/PR2-14B-Instruct datasets: - qingy2024/PR2-SFT language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara 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/qingy2024/PR2-14B-Instruct <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/PR2-14B-Instruct-GGUF/resolve/main/PR2-14B-Instruct.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/PR2-14B-Instruct-GGUF/resolve/main/PR2-14B-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/PR2-14B-Instruct-GGUF/resolve/main/PR2-14B-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/PR2-14B-Instruct-GGUF/resolve/main/PR2-14B-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/PR2-14B-Instruct-GGUF/resolve/main/PR2-14B-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/PR2-14B-Instruct-GGUF/resolve/main/PR2-14B-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/PR2-14B-Instruct-GGUF/resolve/main/PR2-14B-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/PR2-14B-Instruct-GGUF/resolve/main/PR2-14B-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/PR2-14B-Instruct-GGUF/resolve/main/PR2-14B-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/PR2-14B-Instruct-GGUF/resolve/main/PR2-14B-Instruct.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/PR2-14B-Instruct-GGUF/resolve/main/PR2-14B-Instruct.Q8_0.gguf) \| Q8_0 \| 15.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
infogeo/b3d39a1c-1144-431c-b270-40e1e6e4d7a4	infogeo	2025-04-28T10:40:35Z	0	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:unsloth/codegemma-2b", "base_model:adapter:unsloth/codegemma-2b", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T10:32:42Z	--- library_name: peft license: apache-2.0 base_model: unsloth/codegemma-2b tags: - axolotl - generated_from_trainer model-index: - name: b3d39a1c-1144-431c-b270-40e1e6e4d7a4 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/codegemma-2b bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - ba0621f537bc8cd4_train_data.json ds_type: json format: custom path: /workspace/input_data/ba0621f537bc8cd4_train_data.json type: field_input: system field_instruction: question field_output: chosen format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: infogeo/b3d39a1c-1144-431c-b270-40e1e6e4d7a4 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/ba0621f537bc8cd4_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: 37395544-fb64-4439-9c6c-16a1de7f207f wandb_project: s56-28 wandb_run: your_name wandb_runid: 37395544-fb64-4439-9c6c-16a1de7f207f warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # b3d39a1c-1144-431c-b270-40e1e6e4d7a4 This model is a fine-tuned version of [unsloth/codegemma-2b](https://huggingface.co/unsloth/codegemma-2b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1110 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0767 \| 0.0066 \| 150 \| 1.1110 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
BBorg/a2c-PandaReachDense-v3	BBorg	2025-04-28T09:59:30Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-04-28T09:54:59Z	--- 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: -0.21 +/- 0.09 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 ... ```
mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF	mradermacher	2025-04-28T09:59:03Z	448	1	transformers	[ "transformers", "gguf", "medical", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:ggbaobao/medc_llm_based_on_qwen2.5", "base_model:quantized:ggbaobao/medc_llm_based_on_qwen2.5", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-25T16:46:49Z	--- base_model: ggbaobao/medc_llm_based_on_qwen2.5 language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: mit quantized_by: mradermacher tags: - medical --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/ggbaobao/medc_llm_based_on_qwen2.5 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-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/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.9 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/medc_llm_based_on_qwen2.5-i1-GGUF/resolve/main/medc_llm_based_on_qwen2.5.i1-Q6_K.gguf) \| i1-Q6_K \| 6.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
genki10/BERT_V8_sp10_lw40_ex100_lo50_k10_k10_fold4	genki10	2025-04-28T09:24:59Z	42	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-27T10:21:07Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: BERT_V8_sp10_lw40_ex100_lo50_k10_k10_fold4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERT_V8_sp10_lw40_ex100_lo50_k10_k10_fold4 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5427 - Qwk: 0.5669 - Mse: 0.5427 - Rmse: 0.7367 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| No log \| 1.0 \| 5 \| 6.7078 \| 0.0 \| 6.7078 \| 2.5900 \| \| No log \| 2.0 \| 10 \| 4.4067 \| 0.0079 \| 4.4067 \| 2.0992 \| \| No log \| 3.0 \| 15 \| 2.4365 \| 0.0175 \| 2.4365 \| 1.5609 \| \| No log \| 4.0 \| 20 \| 1.3072 \| 0.0107 \| 1.3072 \| 1.1433 \| \| No log \| 5.0 \| 25 \| 0.8520 \| 0.3982 \| 0.8520 \| 0.9230 \| \| No log \| 6.0 \| 30 \| 0.6719 \| 0.3895 \| 0.6719 \| 0.8197 \| \| No log \| 7.0 \| 35 \| 0.9309 \| 0.2902 \| 0.9309 \| 0.9648 \| \| No log \| 8.0 \| 40 \| 0.7205 \| 0.4966 \| 0.7205 \| 0.8488 \| \| No log \| 9.0 \| 45 \| 0.7712 \| 0.4634 \| 0.7712 \| 0.8782 \| \| No log \| 10.0 \| 50 \| 0.8108 \| 0.4906 \| 0.8108 \| 0.9004 \| \| No log \| 11.0 \| 55 \| 0.5563 \| 0.5587 \| 0.5563 \| 0.7458 \| \| No log \| 12.0 \| 60 \| 0.5058 \| 0.5962 \| 0.5058 \| 0.7112 \| \| No log \| 13.0 \| 65 \| 0.5596 \| 0.6458 \| 0.5596 \| 0.7481 \| \| No log \| 14.0 \| 70 \| 0.5800 \| 0.6231 \| 0.5800 \| 0.7616 \| \| No log \| 15.0 \| 75 \| 0.5088 \| 0.5849 \| 0.5088 \| 0.7133 \| \| No log \| 16.0 \| 80 \| 0.5191 \| 0.6323 \| 0.5191 \| 0.7205 \| \| No log \| 17.0 \| 85 \| 0.5390 \| 0.5711 \| 0.5390 \| 0.7342 \| \| No log \| 18.0 \| 90 \| 0.5895 \| 0.6454 \| 0.5895 \| 0.7678 \| \| No log \| 19.0 \| 95 \| 0.5398 \| 0.6112 \| 0.5398 \| 0.7347 \| \| No log \| 20.0 \| 100 \| 0.5523 \| 0.5777 \| 0.5523 \| 0.7432 \| \| No log \| 21.0 \| 105 \| 0.7372 \| 0.5103 \| 0.7372 \| 0.8586 \| \| No log \| 22.0 \| 110 \| 0.6965 \| 0.5279 \| 0.6965 \| 0.8346 \| \| No log \| 23.0 \| 115 \| 0.5263 \| 0.5886 \| 0.5263 \| 0.7255 \| \| No log \| 24.0 \| 120 \| 0.5104 \| 0.5909 \| 0.5104 \| 0.7144 \| \| No log \| 25.0 \| 125 \| 0.5223 \| 0.5781 \| 0.5223 \| 0.7227 \| \| No log \| 26.0 \| 130 \| 0.5991 \| 0.5468 \| 0.5991 \| 0.7740 \| \| No log \| 27.0 \| 135 \| 0.5744 \| 0.5574 \| 0.5744 \| 0.7579 \| \| No log \| 28.0 \| 140 \| 0.5720 \| 0.5672 \| 0.5720 \| 0.7563 \| \| No log \| 29.0 \| 145 \| 0.5213 \| 0.5593 \| 0.5213 \| 0.7220 \| \| No log \| 30.0 \| 150 \| 0.6727 \| 0.5252 \| 0.6727 \| 0.8202 \| \| No log \| 31.0 \| 155 \| 0.5432 \| 0.5692 \| 0.5432 \| 0.7370 \| \| No log \| 32.0 \| 160 \| 0.5245 \| 0.5905 \| 0.5245 \| 0.7242 \| \| No log \| 33.0 \| 165 \| 0.5201 \| 0.5338 \| 0.5201 \| 0.7212 \| \| No log \| 34.0 \| 170 \| 0.5244 \| 0.5561 \| 0.5244 \| 0.7242 \| \| No log \| 35.0 \| 175 \| 0.5202 \| 0.5556 \| 0.5202 \| 0.7212 \| \| No log \| 36.0 \| 180 \| 0.5320 \| 0.5544 \| 0.5320 \| 0.7294 \| \| No log \| 37.0 \| 185 \| 0.5401 \| 0.5909 \| 0.5401 \| 0.7349 \| \| No log \| 38.0 \| 190 \| 0.6913 \| 0.5194 \| 0.6913 \| 0.8314 \| \| No log \| 39.0 \| 195 \| 0.5447 \| 0.5519 \| 0.5447 \| 0.7380 \| \| No log \| 40.0 \| 200 \| 0.5087 \| 0.5540 \| 0.5087 \| 0.7132 \| \| No log \| 41.0 \| 205 \| 0.5323 \| 0.5580 \| 0.5323 \| 0.7296 \| \| No log \| 42.0 \| 210 \| 0.5400 \| 0.5569 \| 0.5400 \| 0.7349 \| \| No log \| 43.0 \| 215 \| 0.5427 \| 0.5669 \| 0.5427 \| 0.7367 \| ### Framework versions - Transformers 4.51.1 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0
Volko76/Qwen2.5-1.5B-Instruct-Q5_K_M-GGUF	Volko76	2025-04-28T08:37:20Z	3	0	transformers	[ "transformers", "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-10-30T22:09:00Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara pipeline_tag: text-generation base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - chat - llama-cpp - gguf-my-repo library_name: transformers --- # Volko76/Qwen2.5-1.5B-Instruct-Q5_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Volko76/Qwen2.5-1.5B-Instruct-Q5_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Volko76/Qwen2.5-1.5B-Instruct-Q5_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Volko76/Qwen2.5-1.5B-Instruct-Q5_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Volko76/Qwen2.5-1.5B-Instruct-Q5_K_M-GGUF --hf-file qwen2.5-1.5b-instruct-q5_k_m.gguf -c 2048 ```
ggbaobao/medc_llm_based_on_qwen2.5	ggbaobao	2025-04-28T08:15:32Z	22	3	null	[ "safetensors", "qwen2", "medical", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:mit", "region:us" ]	null	2025-04-21T08:04:18Z	--- license: mit language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara base_model: - Qwen/Qwen2.5-7B-Instruct tags: - medical --- ## Model Details This model has been LoRA‑fine‑tuned on Qwen2.5‑7B‑Instruct. In the future, reinforcement learning training may be carried out based on this model, such as DPRO algorithm, etc. ### Base Model Sources [optional] https://huggingface.co/Qwen/Qwen2.5-7B-Instruct ## How to Get Started with the Model ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "ggbaobao/medc_llm_based_on_qwen2.5" model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", torch_dtype=torch.bfloat16 ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "猩红热多在发热后多久出现皮疹，请从以下选项中选择：12小时之内, 12～48小时, 60～72小时, 84～96小时, 大于96小时" messages = [ {"role": "system", "content": "You are Qwen, You are a helpful assistant."}, {"role": "user", "content": prompt}, ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( *model_inputs, max_new_tokens=512, do_sample=True ) generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` ## Training Details ```python lora_config = LoraConfig( r=16, lora_alpha=32, target_modules=["q_proj", "v_proj"], lora_dropout=0.1 ) training_args = TrainingArguments( output_dir="./results_final1", learning_rate=7e-5, per_device_train_batch_size=2, per_device_eval_batch_size=2, gradient_accumulation_steps=1, # 梯度累积 num_train_epochs=2, evaluation_strategy="steps", # evaluate_steps=1, save_strategy="steps", save_steps=10, logging_steps=10, logging_dir="./logs1", bf16=True, # 混合精度训练 ``` ### Training Data The training data comes from https://github.com/SupritYoung/Zhongjing If you want to know more details about the above github project, you can also read their paper: Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue The data includes about one-seventh of the multi-round medical consultation data and six-sevenths of the single medical consultation data. #### Hardware vGPU-32GB 6 #### Software use peft and deepspeed
AhmedLet/Qwen_0.5_python_codes_mbpp	AhmedLet	2025-04-28T07:37:20Z	0	1	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "gguf", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:google-research-datasets/mbpp", "dataset:mlabonne/FineTome-100k", "dataset:MohamedSaeed-dev/python_dataset_codes", "base_model:Qwen/Qwen2.5-0.5B", "base_model:finetune:Qwen/Qwen2.5-0.5B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-02-28T08:37:32Z	--- base_model: - Qwen/Qwen2.5-0.5B tags: - text-generation-inference - transformers - unsloth - qwen2 - gguf license: apache-2.0 language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara datasets: - google-research-datasets/mbpp - mlabonne/FineTome-100k - MohamedSaeed-dev/python_dataset_codes --- # Uploaded model - Developed by: AhmedLet - License: apache-2.0
Triangle104/Qwen2.5-7B-Instruct-Q8_0-GGUF	Triangle104	2025-04-28T05:24:57Z	1	0	null	[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:quantized:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-19T15:46:53Z	--- base_model: Qwen/Qwen2.5-7B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Triangle104/Qwen2.5-7B-Instruct-Q8_0-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen2.5-7B-Instruct-Q8_0-GGUF --hf-file qwen2.5-7b-instruct-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen2.5-7B-Instruct-Q8_0-GGUF --hf-file qwen2.5-7b-instruct-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Qwen2.5-7B-Instruct-Q8_0-GGUF --hf-file qwen2.5-7b-instruct-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen2.5-7B-Instruct-Q8_0-GGUF --hf-file qwen2.5-7b-instruct-q8_0.gguf -c 2048 ```
vermoney/581a182e-8e0f-4e40-a116-4ae667a9d44d	vermoney	2025-04-28T05:08:33Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T05:01:50Z	--- library_name: peft license: apache-2.0 base_model: teknium/OpenHermes-2.5-Mistral-7B tags: - axolotl - generated_from_trainer model-index: - name: 581a182e-8e0f-4e40-a116-4ae667a9d44d 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: teknium/OpenHermes-2.5-Mistral-7B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 0117447d3950c946_train_data.json ds_type: json format: custom path: /workspace/input_data/0117447d3950c946_train_data.json type: field_instruction: first_message field_output: first_answer format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vermoney/581a182e-8e0f-4e40-a116-4ae667a9d44d hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/0117447d3950c946_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: <\|im_end\|> 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: dace43b8-8ffb-4c18-baa0-ebd02df71793 wandb_project: s56-9 wandb_run: your_name wandb_runid: dace43b8-8ffb-4c18-baa0-ebd02df71793 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 581a182e-8e0f-4e40-a116-4ae667a9d44d This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3681 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0605 \| 0.0756 \| 200 \| 1.3681 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF	mradermacher	2025-04-28T04:59:19Z	235	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:LyraNovaHeart/Celestial-Harmony-14b-v1.0-Experimental-1016", "base_model:quantized:LyraNovaHeart/Celestial-Harmony-14b-v1.0-Experimental-1016", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-13T14:48:56Z	--- base_model: LyraNovaHeart/Celestial-Harmony-14b-v1.0-Experimental-1016 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/LyraNovaHeart/Celestial-Harmony-14b-v1.0-Experimental-1016 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Celestial-Harmony-14b-v1.0-Experimental-1016-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/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF/resolve/main/Celestial-Harmony-14b-v1.0-Experimental-1016.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF/resolve/main/Celestial-Harmony-14b-v1.0-Experimental-1016.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF/resolve/main/Celestial-Harmony-14b-v1.0-Experimental-1016.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF/resolve/main/Celestial-Harmony-14b-v1.0-Experimental-1016.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF/resolve/main/Celestial-Harmony-14b-v1.0-Experimental-1016.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF/resolve/main/Celestial-Harmony-14b-v1.0-Experimental-1016.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF/resolve/main/Celestial-Harmony-14b-v1.0-Experimental-1016.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF/resolve/main/Celestial-Harmony-14b-v1.0-Experimental-1016.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF/resolve/main/Celestial-Harmony-14b-v1.0-Experimental-1016.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF/resolve/main/Celestial-Harmony-14b-v1.0-Experimental-1016.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF/resolve/main/Celestial-Harmony-14b-v1.0-Experimental-1016.Q8_0.gguf) \| Q8_0 \| 15.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Qwen2.6-14B-Instruct-GGUF	mradermacher	2025-04-28T03:24:41Z	170	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:qingy2024/Qwen2.6-14B-Instruct", "base_model:quantized:qingy2024/Qwen2.6-14B-Instruct", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-06T17:20:05Z	--- base_model: qingy2024/Qwen2.6-14B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/qingy2024/Qwen2.6-14B-Instruct <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.6-14B-Instruct-GGUF/resolve/main/Qwen2.6-14B-Instruct.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.6-14B-Instruct-GGUF/resolve/main/Qwen2.6-14B-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.6-14B-Instruct-GGUF/resolve/main/Qwen2.6-14B-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.6-14B-Instruct-GGUF/resolve/main/Qwen2.6-14B-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.6-14B-Instruct-GGUF/resolve/main/Qwen2.6-14B-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.6-14B-Instruct-GGUF/resolve/main/Qwen2.6-14B-Instruct.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 8.6 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.6-14B-Instruct-GGUF/resolve/main/Qwen2.6-14B-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.6-14B-Instruct-GGUF/resolve/main/Qwen2.6-14B-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.6-14B-Instruct-GGUF/resolve/main/Qwen2.6-14B-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.6-14B-Instruct-GGUF/resolve/main/Qwen2.6-14B-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.6-14B-Instruct-GGUF/resolve/main/Qwen2.6-14B-Instruct.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.6-14B-Instruct-GGUF/resolve/main/Qwen2.6-14B-Instruct.Q8_0.gguf) \| Q8_0 \| 15.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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 -->
airhaohan/Meta-Llama-3-8B-Instruct-Q8_0-GGUF	airhaohan	2025-04-28T02:49:07Z	0	0	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:quantized:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-28T02:48:31Z	--- base_model: meta-llama/Meta-Llama-3-8B-Instruct language: - en license: llama3 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 - llama-cpp - gguf-my-repo new_version: meta-llama/Llama-3.1-8B-Instruct extra_gated_prompt: "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version\ \ Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\ \"Licensee\" or \"you\" means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf), of\ \ the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama\ \ 3\" means the foundational large language models and software and algorithms,\ \ including machine-learning model code, trained model weights, inference-enabling\ \ code, training-enabling code, fine-tuning enabling code and other elements of\ \ the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\ \"Llama Materials\" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation\ \ (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"\ we\" means Meta Platforms Ireland Limited (if you are located in or, if you are\ \ an entity, your principal place of business is in the EEA or Switzerland) and\ \ Meta Platforms, Inc. 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No trademark licenses are granted under this Agreement,\ \ and in connection with the Llama Materials, neither Meta nor Licensee may use\ \ any name or mark owned by or associated with the other or any of its affiliates,\ \ except as required for reasonable and customary use in describing and redistributing\ \ the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you\ \ a license to use “Llama 3” (the “Mark”) solely as required to comply with the\ \ last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently\ \ accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All\ \ goodwill arising out of your use of the Mark will inure to the benefit of Meta.\n\ b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for\ \ Meta, with respect to any derivative works and modifications of the Llama Materials\ \ that are made by you, as between you and Meta, you are and will be the owner of\ \ such derivative works and modifications.\nc. If you institute litigation or other\ \ proceedings against Meta or any entity (including a cross-claim or counterclaim\ \ in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results,\ \ or any portion of any of the foregoing, constitutes infringement of intellectual\ \ property or other rights owned or licensable by you, then any licenses granted\ \ to you under this Agreement shall terminate as of the date such litigation or\ \ claim is filed or instituted. You will indemnify and hold harmless Meta from and\ \ against any claim by any third party arising out of or related to your use or\ \ distribution of the Llama Materials.\n6. Term and Termination. The term of this\ \ Agreement will commence upon your acceptance of this Agreement or access to the\ \ Llama Materials and will continue in full force and effect until terminated in\ \ accordance with the terms and conditions herein. Meta may terminate this Agreement\ \ if you are in breach of any term or condition of this Agreement. Upon termination\ \ of this Agreement, you shall delete and cease use of the Llama Materials. Sections\ \ 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law\ \ and Jurisdiction. This Agreement will be governed and construed under the laws\ \ of the State of California without regard to choice of law principles, and the\ \ UN Convention on Contracts for the International Sale of Goods does not apply\ \ to this Agreement. The courts of California shall have exclusive jurisdiction\ \ of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use\ \ Policy\nMeta is committed to promoting safe and fair use of its tools and features,\ \ including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable\ \ Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n\ #### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly.\ \ You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate\ \ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\ \ contribute to, encourage, plan, incite, or further illegal or unlawful activity\ \ or content, such as:\n 1. Violence or terrorism\n 2. Exploitation\ \ or harm to children, including the solicitation, creation, acquisition, or dissemination\ \ of child exploitative content or failure to report Child Sexual Abuse Material\n\ \ 3. Human trafficking, exploitation, and sexual violence\n 4. The\ \ illegal distribution of information or materials to minors, including obscene\ \ materials, or failure to employ legally required age-gating in connection with\ \ such information or materials.\n 5. Sexual solicitation\n 6. Any\ \ other criminal activity\n 2. Engage in, promote, incite, or facilitate the\ \ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\ \ 3. Engage in, promote, incite, or facilitate discrimination or other unlawful\ \ or harmful conduct in the provision of employment, employment benefits, credit,\ \ housing, other economic benefits, or other essential goods and services\n 4.\ \ Engage in the unauthorized or unlicensed practice of any profession including,\ \ but not limited to, financial, legal, medical/health, or related professional\ \ practices\n 5. Collect, process, disclose, generate, or infer health, demographic,\ \ or other sensitive personal or private information about individuals without rights\ \ and consents required by applicable laws\n 6. Engage in or facilitate any action\ \ or generate any content that infringes, misappropriates, or otherwise violates\ \ any third-party rights, including the outputs or results of any products or services\ \ using the Llama Materials\n 7. Create, generate, or facilitate the creation\ \ of malicious code, malware, computer viruses or do anything else that could disable,\ \ overburden, interfere with or impair the proper working, integrity, operation\ \ or appearance of a website or computer system\n2. Engage in, promote, incite,\ \ facilitate, or assist in the planning or development of activities that present\ \ a risk of death or bodily harm to individuals, including use of Meta Llama 3 related\ \ to the following:\n 1. Military, warfare, nuclear industries or applications,\ \ espionage, use for materials or activities that are subject to the International\ \ Traffic Arms Regulations (ITAR) maintained by the United States Department of\ \ State\n 2. Guns and illegal weapons (including weapon development)\n 3.\ \ Illegal drugs and regulated/controlled substances\n 4. Operation of critical\ \ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\ \ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\ \ content intended to incite or promote violence, abuse, or any infliction of bodily\ \ harm to an individual\n3. Intentionally deceive or mislead others, including use\ \ of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering\ \ fraud or the creation or promotion of disinformation\n 2. Generating, promoting,\ \ or furthering defamatory content, including the creation of defamatory statements,\ \ images, or other content\n 3. Generating, promoting, or further distributing\ \ spam\n 4. Impersonating another individual without consent, authorization,\ \ or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are\ \ human-generated\n 6. Generating or facilitating false online engagement, including\ \ fake reviews and other means of fake online engagement\n4. Fail to appropriately\ \ disclose to end users any known dangers of your AI system\nPlease report any violation\ \ of this Policy, software “bug,” or other problems that could lead to a violation\ \ of this Policy through one of the following means:\n * Reporting issues with\ \ the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n\ \ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\ \ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\ \ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]" extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location ? By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit widget: - example_title: Hello messages: - role: user content: Hey my name is Julien! How are you? - example_title: Winter holidays messages: - role: system content: You are a helpful and honest assistant. Please, respond concisely and truthfully. - role: user content: Can you recommend a good destination for Winter holidays? - example_title: Programming assistant messages: - role: system content: You are a helpful and honest code and programming assistant. Please, respond concisely and truthfully. - role: user content: Write a function that computes the nth fibonacci number. inference: parameters: max_new_tokens: 300 stop: - <\|end_of_text\|> - <\|eot_id\|> --- # airhaohan/Meta-Llama-3-8B-Instruct-Q8_0-GGUF This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo airhaohan/Meta-Llama-3-8B-Instruct-Q8_0-GGUF --hf-file meta-llama-3-8b-instruct-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo airhaohan/Meta-Llama-3-8B-Instruct-Q8_0-GGUF --hf-file meta-llama-3-8b-instruct-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo airhaohan/Meta-Llama-3-8B-Instruct-Q8_0-GGUF --hf-file meta-llama-3-8b-instruct-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo airhaohan/Meta-Llama-3-8B-Instruct-Q8_0-GGUF --hf-file meta-llama-3-8b-instruct-q8_0.gguf -c 2048 ```
TOMFORD79/TF_o1.4	TOMFORD79	2025-04-27T18:11:50Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-27T17:53:10Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
POPULAR-VIDEO-Mathira-Khan-Viral-Video/NEW.EXCLUSIVE.Mathira.Khan.Viral.Video.Link	POPULAR-VIDEO-Mathira-Khan-Viral-Video	2025-04-27T17:41:31Z	0	0	null	[ "region:us" ]	null	2025-04-27T17:40:10Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/2rkvnsdr?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> It’s not a good time to be an influencer in Pakistan now. Currently the influencer community in the country is in a crisis, as several prominent TikTok stars’ private videos were leaked online. Weeks after Minahil Malik and Imsha Rehman’s explicit videos went viral, another Pakistani influencer named Mathira Khan, has fallen prey to the disturbing trend.
Blessedmccall/REGULAR	Blessedmccall	2025-04-26T02:29:35Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-26T02:29:35Z	--- license: apache-2.0 ---

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