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train · 850k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-29 12:28:32	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 502 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-29 12:27:55	card stringlengths 11 1.01M
mradermacher/DeepSeek-R1-Distill-Llama-8B_synthetic_1-GGUF	mradermacher	2025-01-26T06:03:07Z	2,439	1	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "sft", "en", "base_model:Kunakornjack/DeepSeek-R1-Distill-Llama-8B_synthetic_1", "base_model:quantized:Kunakornjack/DeepSeek-R1-Distill-Llama-8B_synthetic_1", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-26T05:03:37Z	--- base_model: Kunakornjack/DeepSeek-R1-Distill-Llama-8B_synthetic_1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Kunakornjack/DeepSeek-R1-Distill-Llama-8B_synthetic_1 <!-- 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/DeepSeek-R1-Distill-Llama-8B_synthetic_1-GGUF/resolve/main/DeepSeek-R1-Distill-Llama-8B_synthetic_1.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Llama-8B_synthetic_1-GGUF/resolve/main/DeepSeek-R1-Distill-Llama-8B_synthetic_1.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Llama-8B_synthetic_1-GGUF/resolve/main/DeepSeek-R1-Distill-Llama-8B_synthetic_1.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Llama-8B_synthetic_1-GGUF/resolve/main/DeepSeek-R1-Distill-Llama-8B_synthetic_1.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Llama-8B_synthetic_1-GGUF/resolve/main/DeepSeek-R1-Distill-Llama-8B_synthetic_1.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Llama-8B_synthetic_1-GGUF/resolve/main/DeepSeek-R1-Distill-Llama-8B_synthetic_1.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Llama-8B_synthetic_1-GGUF/resolve/main/DeepSeek-R1-Distill-Llama-8B_synthetic_1.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Llama-8B_synthetic_1-GGUF/resolve/main/DeepSeek-R1-Distill-Llama-8B_synthetic_1.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Llama-8B_synthetic_1-GGUF/resolve/main/DeepSeek-R1-Distill-Llama-8B_synthetic_1.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Llama-8B_synthetic_1-GGUF/resolve/main/DeepSeek-R1-Distill-Llama-8B_synthetic_1.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Llama-8B_synthetic_1-GGUF/resolve/main/DeepSeek-R1-Distill-Llama-8B_synthetic_1.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Llama-8B_synthetic_1-GGUF/resolve/main/DeepSeek-R1-Distill-Llama-8B_synthetic_1.f16.gguf) \| f16 \| 16.2 \| 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 -->
RUC-AIBOX/STILL-3-1.5B-preview	RUC-AIBOX	2025-01-26T06:02:00Z	623	3	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:2411.11694", "arxiv:2412.09413", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-25T16:59:23Z	--- library_name: transformers tags: [] --- # Introduction We release STILL-3-1.5B-preview, a slow-thinking reasoning model achieves 39.33% accuracy on AIME benchmark! We adapt reinforcement learning on 1.5B model and observe the continuous performance improvement as the number of training steps increased. For better reproducing our work and advancing research progress, we open-source our code, model, and data. Code: https://github.com/RUCAIBox/Slow_Thinking_with_LLMs # Evaluation We evaluated the model on four benchmarks: MATH, AIME, OMNI, and LiveAOPS. For MATH and AIME, we employed a sampling decoding setup with a sampling temperature of 0.6 and a top-p sampling probability of 0.95. Each question was sampled 64 times, and the average score was calculated. For OMNI and LiveAOPS (August-November 2024), we randomly sampled a subset of answers as integers to facilitate automated evaluation, and used greedy search decoding for the evaluation. The trained model, STILL-3-1.5B-preview, achieved significant improvement. The accuracy on the AIME task increased from 28.67% to 39.33%, resulting in a relative improvement of 37.18%. \| \| MATH \| AIME \| OMNI \| LiveAOPS \| Avg. \| \| --- \| :---: \| :---: \| :---: \| :---: \| :---: \| \| Backbone \| 84.04 \| 28.67 \| 25.60 \| 33.33 \| 42.91 \| \| STILL-3-1.5B-preview \| 85.48 \| 39.33 \| 33.00 \| 39.50 \| 49.33 \| # Quick Start ``` from transformers import AutoTokenizer, AutoModelForCausalLM from vllm import LLM, SamplingParams # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained("RUC-AIBOX/STILL-3-1.5B-preview") model = AutoModelForCausalLM.from_pretrained("RUC-AIBOX/STILL-3-1.5B-preview") # Input text question = "Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$" input_prompts = tokenizer.apply_chat_template( [ {"role": "user", "content": question}], tokenize=False, add_generation_prompt=True ) # Params llm = LLM(model=model_path, tensor_parallel_size=1, dtype='bfloat16') sampling_params_gs = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=32768, stop=stop_words, seed=42, skip_special_tokens=False) # Completion responses = model.generate(input_prompts, sampling_params) print(responses[0].outputs[0].text) ``` # Reference Please kindly cite our reports if they are helpful for your research. ``` @article{Slow_Thinking_with_LLMs_3_Preview, title={STILL-3-1.5B-preview: Enhancing Slow Thinking Abilities of Small Models through Reinforcement Learning }, author={RUCAIBox STILL Team}, url={https://github.com/RUCAIBox/Slow_Thinking_with_LLMs}, year={2025} } ``` ``` @article{Slow_Thinking_with_LLMs_1, title={Enhancing LLM Reasoning with Reward-guided Tree Search}, author={Jiang, Jinhao and Chen, Zhipeng and Min, Yingqian and Chen, Jie and Cheng, Xiaoxue and Wang, Jiapeng and Tang, Yiru and Sun, Haoxiang and Deng, Jia and Zhao, Wayne Xin and Liu, Zheng and Yan, Dong and Xie, Jian and Wang, Zhongyuan and Wen, Ji-Rong}, journal={arXiv preprint arXiv:2411.11694}, year={2024} } ``` ``` @article{Slow_Thinking_with_LLMs_2, title={Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems}, author={Min, Yingqian and Chen, Zhipeng and Jiang, Jinhao and Chen, Jie and Deng, Jia and Hu, Yiwen and Tang, Yiru and Wang, Jiapeng and Cheng, Xiaoxue and Song, Huatong and Zhao, Wayne Xin and Liu, Zheng and Wang, Zhongyuan and Wen, Ji-Rong}, journal={arXiv preprint arXiv:2412.09413}, year={2024} } ```
Futuresony/Future_pics_26-01-2025	Futuresony	2025-01-26T06:00:28Z	6	0	diffusers	[ "diffusers", "finance", "text-to-image", "en", "dataset:fka/awesome-chatgpt-prompts", "base_model:deepseek-ai/DeepSeek-R1", "base_model:finetune:deepseek-ai/DeepSeek-R1", "license:apache-2.0", "region:us" ]	text-to-image	2025-01-26T05:56:48Z	--- license: apache-2.0 datasets: - fka/awesome-chatgpt-prompts language: - en metrics: - bleu base_model: - deepseek-ai/DeepSeek-R1 new_version: deepseek-ai/DeepSeek-R1 pipeline_tag: text-to-image library_name: diffusers tags: - finance ---
kostiantynk-out/7f8afdb7-3684-4cdd-825f-aa47d4a36962	kostiantynk-out	2025-01-26T05:58:15Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "base_model:adapter:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "license:llama3", "region:us" ]	null	2025-01-26T05:57:10Z	--- library_name: peft license: llama3 base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1 tags: - axolotl - generated_from_trainer model-index: - name: 7f8afdb7-3684-4cdd-825f-aa47d4a36962 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: tokyotech-llm/Llama-3-Swallow-8B-v0.1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - b355c3ff95258244_train_data.json ds_type: json format: custom path: /workspace/input_data/b355c3ff95258244_train_data.json type: field_instruction: input field_output: output 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: kostiantynk-out/7f8afdb7-3684-4cdd-825f-aa47d4a36962 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/b355c3ff95258244_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: 4 sequence_len: 512 special_tokens: pad_token: <\|end_of_text\|> 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: 22487750-366e-41ca-8395-d8629638fd03 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 22487750-366e-41ca-8395-d8629638fd03 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 7f8afdb7-3684-4cdd-825f-aa47d4a36962 This model is a fine-tuned version of [tokyotech-llm/Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2322 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5738 \| 0.0028 \| 1 \| 1.4206 \| \| 1.4077 \| 0.0085 \| 3 \| 1.3855 \| \| 1.2158 \| 0.0170 \| 6 \| 0.8057 \| \| 0.4077 \| 0.0255 \| 9 \| 0.2322 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Best000/881df212-3b29-4eff-b06b-55e945d1e0f0	Best000	2025-01-26T05:55:04Z	9	0	peft	[ "peft", "safetensors", "gemma2", "axolotl", "generated_from_trainer", "base_model:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "base_model:adapter:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "license:gemma", "region:us" ]	null	2025-01-26T05:28:40Z	--- library_name: peft license: gemma base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 tags: - axolotl - generated_from_trainer model-index: - name: 881df212-3b29-4eff-b06b-55e945d1e0f0 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: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - d9ae9af1d1d23889_train_data.json ds_type: json format: custom path: /workspace/input_data/d9ae9af1d1d23889_train_data.json type: field_instruction: input_persona field_output: prompt 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: Best000/881df212-3b29-4eff-b06b-55e945d1e0f0 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/d9ae9af1d1d23889_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: 4 sequence_len: 512 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: d1ddd83d-3254-4f1a-93a9-98ee3250c38a wandb_project: Birthday-SN56-16-Gradients-On-Demand wandb_run: your_name wandb_runid: d1ddd83d-3254-4f1a-93a9-98ee3250c38a warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 881df212-3b29-4eff-b06b-55e945d1e0f0 This model is a fine-tuned version of [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0451 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.3355 \| 0.0001 \| 1 \| 1.3673 \| \| 1.1577 \| 0.0002 \| 3 \| 1.3508 \| \| 1.3385 \| 0.0003 \| 6 \| 1.1737 \| \| 1.0103 \| 0.0005 \| 9 \| 1.0451 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ClarenceDan/64d67e54-f90e-40ea-ac90-e58c0094a5c8	ClarenceDan	2025-01-26T05:54:21Z	9	0	peft	[ "peft", "safetensors", "gemma2", "axolotl", "generated_from_trainer", "base_model:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "base_model:adapter:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "license:gemma", "region:us" ]	null	2025-01-26T05:28:14Z	--- library_name: peft license: gemma base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 tags: - axolotl - generated_from_trainer model-index: - name: 64d67e54-f90e-40ea-ac90-e58c0094a5c8 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: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - d9ae9af1d1d23889_train_data.json ds_type: json format: custom path: /workspace/input_data/d9ae9af1d1d23889_train_data.json type: field_instruction: input_persona field_output: prompt 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: ClarenceDan/64d67e54-f90e-40ea-ac90-e58c0094a5c8 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/d9ae9af1d1d23889_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: 4 sequence_len: 512 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: d1ddd83d-3254-4f1a-93a9-98ee3250c38a wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: d1ddd83d-3254-4f1a-93a9-98ee3250c38a warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 64d67e54-f90e-40ea-ac90-e58c0094a5c8 This model is a fine-tuned version of [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0442 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.3355 \| 0.0001 \| 1 \| 1.3673 \| \| 1.1574 \| 0.0002 \| 3 \| 1.3502 \| \| 1.3364 \| 0.0003 \| 6 \| 1.1701 \| \| 1.0098 \| 0.0005 \| 9 \| 1.0442 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
prxy5604/6e03b9eb-bc21-4bc9-90c9-1a515278b1a2	prxy5604	2025-01-26T05:50:55Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.1-Storm-8B", "base_model:adapter:unsloth/Llama-3.1-Storm-8B", "license:llama3.1", "region:us" ]	null	2025-01-26T05:18:34Z	--- library_name: peft license: llama3.1 base_model: unsloth/Llama-3.1-Storm-8B tags: - axolotl - generated_from_trainer model-index: - name: 6e03b9eb-bc21-4bc9-90c9-1a515278b1a2 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/Llama-3.1-Storm-8B bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - e722133f6ff26062_train_data.json ds_type: json format: custom path: /workspace/input_data/e722133f6ff26062_train_data.json type: field_instruction: context field_output: question format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 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: true hub_model_id: prxy5604/6e03b9eb-bc21-4bc9-90c9-1a515278b1a2 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/e722133f6ff26062_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 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: 50 saves_per_epoch: null sequence_len: 1024 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: 60c2573c-863d-40d4-92b5-0522184a2c6f wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 60c2573c-863d-40d4-92b5-0522184a2c6f warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 6e03b9eb-bc21-4bc9-90c9-1a515278b1a2 This model is a fine-tuned version of [unsloth/Llama-3.1-Storm-8B](https://huggingface.co/unsloth/Llama-3.1-Storm-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1049 ## 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: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.1641 \| 0.0019 \| 1 \| 3.7553 \| \| 1.7007 \| 0.0946 \| 50 \| 1.3936 \| \| 1.2217 \| 0.1892 \| 100 \| 1.2340 \| \| 1.1767 \| 0.2838 \| 150 \| 1.1350 \| \| 1.1497 \| 0.3784 \| 200 \| 1.1049 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Rich-J/subnet29_upload_c00_Jan26_0	Rich-J	2025-01-26T05:50:53Z	125	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-26T05:46:22Z	--- 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. 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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]
suzall/llama-3.2-3b-linkbox-finetune	suzall	2025-01-26T05:50:35Z	29	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-3.2", "fine-tuned", "conversational", "question-answering", "agentic-ai", "en", "de", "fr", "it", "pt", "hi", "es", "th", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-23T12:08:10Z	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers tags: - llama-3.2 - fine-tuned - conversational - question-answering - agentic-ai pipeline_tag: text-generation base_model: - meta-llama/Llama-3.2-1B-Instruct --- # Model Card for Llama-3.2-3B-Linkbox-Finetune ## Model Details ### Model Description A fine-tuned version of Meta's Llama 3.2-3B model optimized for contextual understanding and link analysis in conversational AI applications. This model demonstrates enhanced performance in: - Multi-turn dialogue systems - Knowledge retrieval and synthesis:cite[4] - Contextual link recognition and analysis - Agentic workflow orchestration:cite[7] Developed by: Sujal Tamrakar Model type: Transformer-based language model with Grouped-Query Attention (GQA):cite[4] Language(s): Primarily English, with capabilities in German, French, Italian, Portuguese, Hindi, Spanish, and Thai:cite[4] License: Llama 3.2 Community License ([full terms](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)) Finetuned from: meta-llama/Llama-3.2-3B-Instruct:cite[4] ### Model Sources - Repository: [Your GitHub Repository Link] - Base Model: [Meta Llama 3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) - Demo: [Link to Gradio/Streamlit Demo] ## Uses ### Direct Use - Contextual link analysis in documents - Multi-turn conversational agents - Knowledge retrieval and synthesis systems - Agentic workflow automation:cite[7] ### Downstream Use - Enterprise knowledge management systems - AI-powered research assistants - Context-aware content recommendation engines - Automated documentation analysis tools ### Out-of-Scope Use - Medical/legal decision making - Generating malicious content - High-risk government applications - Languages beyond supported list without proper safety testing:cite[4] ## Bias, Risks, and Limitations - May reflect biases in pretraining data - Limited knowledge cutoff (December 2023):cite[4] - Potential hallucination in long-form generation - Performance degradation on highly technical domains ### Recommendations - Implement content filtering (e.g., Llama Guard 3):cite[7] - Use constrained decoding techniques - Monitor for factual accuracy in critical applications - Conduct safety testing for target deployment languages:cite[4] ## How to Get Started ```bash from transformers import pipeline model_id = "suzall/llama-3.2-3b-linkbox-finetune" pipe = pipeline( "text-generation", model=model_id, device_map="auto", torch_dtype=torch.bfloat16 ) messages = [{ "role": "user", "content": "Analyze links in this text: [YOUR_TEXT]" }] outputs = pipe(messages, max_new_tokens=256) ``` ## Training Details ### Training Data - FineTome-100k dataset (conversational format)13 - in-specific link analysis corpus (10k samples) - Synthetic data generated using Llama 3.1-8B13 ### Training Procedure - Architecture: LoRA fine-tuning with r=3213 - Optimizer: AdamW-8bit - Learning Rate: 2e-4 with linear decay - Sequence Length: 2048 tokens - Hardware: NVIDIA A100 (40GB) - Training Time: 8 GPU hours #### Training Hyperparameters ```bash TrainingArguments( per_device_train_batch_size=4, gradient_accumulation_steps=4, num_train_epochs=3, learning_rate=2e-4, bf16=True, lr_scheduler_type="linear" ) ``` ## Evaluation ### Benchmark Performance \| Benchmark \| Score \| Comparison \| \|------------------\|-------\|-----------------\| \| IFEval (Strict) \| 78.2 \| +1.3 vs base \| \| LinkAnalysis-API \| 89.4 \| Custom metric \| \| MMLU \| 63.7 \| -0.6 vs base \| ## Environmental Impact - Carbon Emissions: ~0.8 kgCO2eq (estimated) - Hardware: 1×A100-40GB - Energy: 2.5kWh (Renewable-powered) ## Technical Specifications ### Model Architecture - Transformer-based with GQA5 - 3.21B parameters - 32-layer decoder - 4096 hidden dimension - 128k token context window5 ### Quantization Options \| Precision \| Memory \| Recommended Use \| \|-----------\|--------\|---------------------\| \| BF16 \| 6.5GB \| Full precision \| \| FP8 \| 3.2GB \| Balanced \| \| INT4 \| 1.75GB \| Edge deployment \| ## Model Card Contact - Maintainer: Sujal Tamrakar - Email: [email protected]
chauhoang/d69b4d2b-7228-416f-a445-6797c41fd456	chauhoang	2025-01-26T05:45:41Z	12	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-0.5B", "base_model:adapter:unsloth/Qwen2.5-0.5B", "license:apache-2.0", "region:us" ]	null	2025-01-26T05:39:12Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-0.5B tags: - axolotl - generated_from_trainer model-index: - name: d69b4d2b-7228-416f-a445-6797c41fd456 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/Qwen2.5-0.5B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e79aa413a56fb417_train_data.json ds_type: json format: custom path: /workspace/input_data/e79aa413a56fb417_train_data.json type: field_instruction: prompt field_output: chosen 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: 5 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: chauhoang/d69b4d2b-7228-416f-a445-6797c41fd456 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 5 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 50 micro_batch_size: 2 mlflow_experiment_name: /tmp/e79aa413a56fb417_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: 4 sequence_len: 512 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: 4692c3b1-0351-4533-948d-ace8c76ceb1f wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 4692c3b1-0351-4533-948d-ace8c76ceb1f warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # d69b4d2b-7228-416f-a445-6797c41fd456 This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B](https://huggingface.co/unsloth/Qwen2.5-0.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## 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: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0005 \| 1 \| nan \| \| 0.0 \| 0.0048 \| 10 \| nan \| \| 0.0 \| 0.0097 \| 20 \| nan \| \| 0.0 \| 0.0145 \| 30 \| nan \| \| 0.0 \| 0.0193 \| 40 \| nan \| \| 0.0 \| 0.0242 \| 50 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nblinh63/b539cd6e-3d91-4d14-9b04-e58017dcde76	nblinh63	2025-01-26T05:44:59Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:TinyLlama/TinyLlama_v1.1", "base_model:adapter:TinyLlama/TinyLlama_v1.1", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T05:33:13Z	--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama_v1.1 tags: - axolotl - generated_from_trainer model-index: - name: b539cd6e-3d91-4d14-9b04-e58017dcde76 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: TinyLlama/TinyLlama_v1.1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f251bafddc1c416f_train_data.json ds_type: json format: custom path: /workspace/input_data/f251bafddc1c416f_train_data.json type: field_input: item_cast field_instruction: item_title field_output: comment 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: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: nblinh63/b539cd6e-3d91-4d14-9b04-e58017dcde76 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/f251bafddc1c416f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: b7c42af7-32e6-4423-bce5-9d6119627078 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: b7c42af7-32e6-4423-bce5-9d6119627078 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # b539cd6e-3d91-4d14-9b04-e58017dcde76 This model is a fine-tuned version of [TinyLlama/TinyLlama_v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.2430 ## 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: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.0363 \| 0.0565 \| 200 \| 4.2430 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mrHungddddh/4c33ec74-70d3-422a-ab86-a58af09ba89d	mrHungddddh	2025-01-26T05:44:41Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:TinyLlama/TinyLlama_v1.1", "base_model:adapter:TinyLlama/TinyLlama_v1.1", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T05:33:11Z	--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama_v1.1 tags: - axolotl - generated_from_trainer model-index: - name: 4c33ec74-70d3-422a-ab86-a58af09ba89d 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: TinyLlama/TinyLlama_v1.1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f251bafddc1c416f_train_data.json ds_type: json format: custom path: /workspace/input_data/f251bafddc1c416f_train_data.json type: field_input: item_cast field_instruction: item_title field_output: comment 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: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: mrHungddddh/4c33ec74-70d3-422a-ab86-a58af09ba89d hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/f251bafddc1c416f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: b7c42af7-32e6-4423-bce5-9d6119627078 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: b7c42af7-32e6-4423-bce5-9d6119627078 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 4c33ec74-70d3-422a-ab86-a58af09ba89d This model is a fine-tuned version of [TinyLlama/TinyLlama_v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.2430 ## 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: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.0363 \| 0.0565 \| 200 \| 4.2430 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
aleegis12/1661a9e5-fc70-4e3d-b425-6214f9268ae2	aleegis12	2025-01-26T05:44:25Z	8	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:adapter:EleutherAI/pythia-70m-deduped", "license:apache-2.0", "region:us" ]	null	2025-01-26T05:34:57Z	--- library_name: peft license: apache-2.0 base_model: EleutherAI/pythia-70m-deduped tags: - axolotl - generated_from_trainer model-index: - name: 1661a9e5-fc70-4e3d-b425-6214f9268ae2 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: EleutherAI/pythia-70m-deduped bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 59ebf80954a6130a_train_data.json ds_type: json format: custom path: /workspace/input_data/59ebf80954a6130a_train_data.json type: field_input: solution_steps field_instruction: problem field_output: solution format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 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: true hub_model_id: aleegis12/1661a9e5-fc70-4e3d-b425-6214f9268ae2 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/59ebf80954a6130a_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 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: 50 saves_per_epoch: null sequence_len: 1024 special_tokens: pad_token: <\|endoftext\|> 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: f210f656-5c7e-4a29-80ca-643c4317c822 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: f210f656-5c7e-4a29-80ca-643c4317c822 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 1661a9e5-fc70-4e3d-b425-6214f9268ae2 This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2198 ## 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: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 11.4407 \| 0.0002 \| 1 \| 4.5460 \| \| 37.5818 \| 0.0085 \| 50 \| 3.7542 \| \| 14.5638 \| 0.0170 \| 100 \| 3.2181 \| \| 12.6851 \| 0.0255 \| 150 \| 2.1898 \| \| 14.1543 \| 0.0341 \| 200 \| 2.2198 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
poooj/XLMHateSpeechClassification	poooj	2025-01-26T05:43:45Z	7	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-01-26T05:26:44Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: XLMHateSpeechClassification 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. --> # XLMHateSpeechClassification This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7826 - Accuracy: 0.8319 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5749 \| 1.0 \| 1137 \| 0.5185 \| 0.7780 \| \| 0.4785 \| 2.0 \| 2274 \| 0.5458 \| 0.7681 \| \| 0.4545 \| 3.0 \| 3411 \| 0.4246 \| 0.8154 \| \| 0.3938 \| 4.0 \| 4548 \| 0.5763 \| 0.8176 \| \| 0.3554 \| 5.0 \| 5685 \| 0.6506 \| 0.8154 \| \| 0.3368 \| 6.0 \| 6822 \| 0.7826 \| 0.8319 \| ### Framework versions - Transformers 4.48.1 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
kk-aivio/397027cc-2c26-4a79-b5c2-1532f4d74039	kk-aivio	2025-01-26T05:42:52Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:defog/sqlcoder-7b-2", "base_model:adapter:defog/sqlcoder-7b-2", "license:cc-by-sa-4.0", "region:us" ]	null	2025-01-26T05:41:25Z	--- library_name: peft license: cc-by-sa-4.0 base_model: defog/sqlcoder-7b-2 tags: - axolotl - generated_from_trainer model-index: - name: 397027cc-2c26-4a79-b5c2-1532f4d74039 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: defog/sqlcoder-7b-2 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 6b30f33bbd9cba22_train_data.json ds_type: json format: custom path: /workspace/input_data/6b30f33bbd9cba22_train_data.json type: field_input: reasoning field_instruction: question field_output: answer 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: kk-aivio/397027cc-2c26-4a79-b5c2-1532f4d74039 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/6b30f33bbd9cba22_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: 4 sequence_len: 512 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 66ffa688-b6ab-4800-bb73-500be3c51df8 wandb_project: Birthday-SN56-17-Gradients-On-Demand wandb_run: your_name wandb_runid: 66ffa688-b6ab-4800-bb73-500be3c51df8 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 397027cc-2c26-4a79-b5c2-1532f4d74039 This model is a fine-tuned version of [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0010 \| 1 \| nan \| \| 0.0 \| 0.0029 \| 3 \| nan \| \| 0.1493 \| 0.0058 \| 6 \| nan \| \| 0.0 \| 0.0087 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
AMindToThink/gemma-2-2b_RMU_s100_a100_layer7	AMindToThink	2025-01-26T05:42:28Z	7	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-26T05:40:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
strangerzonehf/Inkk	strangerzonehf	2025-01-26T05:40:19Z	423	11	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:apache-2.0", "region:us" ]	text-to-image	2025-01-25T15:44:20Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: 'Inkk, A black and white portrait of a womans face. The womans head is facing the left side of the frame. Her hair is cut in a bun. Her eyes are wide open. Her eyebrows are black and her lips are painted black. Her mouth is painted white. Her nose is black. She has a black microphone in her mouth. The background is white.' output: url: images/1.png - text: 'Inkk, A black and white drawing of a mans face. The man has a black mustache that is trimmed in black. His eyes are blue and he has black hair. He is wearing a black collar with black stripes on it. He also has earphones in his ears. The background is white.' output: url: images/2.png - text: 'Inkk, A black and white monochromatic portrait of a womans face. The womans head is facing the left side of the frame, her hair cascades over her shoulders. She is wearing a black dress with a white stripe down the center of her neck. Her ear is encased in a silver earring. Her hair is pulled back in a ponytail, adding a pop of color to the scene. The background is a stark white, creating a stark contrast to the womans silhouette.' output: url: images/3.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: Inkk license: apache-2.0 --- ![zgvdsfvzdfvbzd.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/s9Rq1DTbCedZxm2974-TN.png) <Gallery /> # Model description for Inkk Image Processing Parameters \| Parameter \| Value \| Parameter \| Value \| \|---------------------------\|--------\|---------------------------\|--------\| \| LR Scheduler \| constant \| Noise Offset \| 0.03 \| \| Optimizer \| AdamW \| Multires Noise Discount \| 0.1 \| \| Network Dim \| 64 \| Multires Noise Iterations \| 10 \| \| Network Alpha \| 32 \| Repeat & Steps \| 19 & 2770 \| \| Epoch \| 23 \| Save Every N Epochs \| 1 \| Labeling: florence2-en(natural language & English) Total Images Used for Training : 55 ## Best Dimensions & Inference \| Dimensions \| Aspect Ratio \| Recommendation \| \|-----------------\|------------------\|---------------------------\| \| 1280 x 832 \| 3:2 \| Best \| \| 1024 x 1024 \| 1:1 \| Default \| ### Inference Range - Recommended Inference Steps: 30–35 ## Setting Up ```python import torch from pipelines import DiffusionPipeline base_model = "black-forest-labs/FLUX.1-dev" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) lora_repo = "strangerzonehf/Inkk" trigger_word = "Inkk" pipe.load_lora_weights(lora_repo) device = torch.device("cuda") pipe.to(device) ``` ## Trigger words You should use `Inkk` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/strangerzonehf/Inkk/tree/main) them in the Files & versions tab.
nhunglaaaaaaa/f9491ff3-b72e-4a33-aad8-1648e7558d16	nhunglaaaaaaa	2025-01-26T05:39:31Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:TinyLlama/TinyLlama_v1.1", "base_model:adapter:TinyLlama/TinyLlama_v1.1", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T05:33:14Z	--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama_v1.1 tags: - axolotl - generated_from_trainer model-index: - name: f9491ff3-b72e-4a33-aad8-1648e7558d16 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: TinyLlama/TinyLlama_v1.1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f251bafddc1c416f_train_data.json ds_type: json format: custom path: /workspace/input_data/f251bafddc1c416f_train_data.json type: field_input: item_cast field_instruction: item_title field_output: comment 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: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: nhunglaaaaaaa/f9491ff3-b72e-4a33-aad8-1648e7558d16 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/f251bafddc1c416f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: b7c42af7-32e6-4423-bce5-9d6119627078 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: b7c42af7-32e6-4423-bce5-9d6119627078 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # f9491ff3-b72e-4a33-aad8-1648e7558d16 This model is a fine-tuned version of [TinyLlama/TinyLlama_v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.2430 ## 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: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.0363 \| 0.0565 \| 200 \| 4.2430 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nabeix/lucky_0x01	nabeix	2025-01-26T05:38:38Z	13	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-26T05:35:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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chauhoang/f299a2ca-913b-46a4-b46b-54d651993a9a	chauhoang	2025-01-26T05:38:08Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Korabbit/llama-2-ko-7b", "base_model:adapter:Korabbit/llama-2-ko-7b", "region:us" ]	null	2025-01-26T03:31:13Z	--- library_name: peft base_model: Korabbit/llama-2-ko-7b tags: - axolotl - generated_from_trainer model-index: - name: f299a2ca-913b-46a4-b46b-54d651993a9a 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: Korabbit/llama-2-ko-7b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - c9c324e8cf5586e6_train_data.json ds_type: json format: custom path: /workspace/input_data/c9c324e8cf5586e6_train_data.json type: field_instruction: instruction field_output: output 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: 5 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: chauhoang/f299a2ca-913b-46a4-b46b-54d651993a9a hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 5 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 50 micro_batch_size: 2 mlflow_experiment_name: /tmp/c9c324e8cf5586e6_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: 4 sequence_len: 512 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: d35b96a9-b8d1-49c0-b1a8-167bc6103694 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: d35b96a9-b8d1-49c0-b1a8-167bc6103694 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # f299a2ca-913b-46a4-b46b-54d651993a9a This model is a fine-tuned version of [Korabbit/llama-2-ko-7b](https://huggingface.co/Korabbit/llama-2-ko-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1226 ## 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: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0000 \| 1 \| 1.5282 \| \| 1.5021 \| 0.0003 \| 10 \| 1.3473 \| \| 1.1969 \| 0.0006 \| 20 \| 1.1846 \| \| 1.0982 \| 0.0008 \| 30 \| 1.1380 \| \| 1.1626 \| 0.0011 \| 40 \| 1.1252 \| \| 1.0779 \| 0.0014 \| 50 \| 1.1226 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
daniel40/746b383b-cead-4efc-9489-de75d155bdb7	daniel40	2025-01-26T05:37:31Z	6	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:adapter:EleutherAI/pythia-70m-deduped", "license:apache-2.0", "region:us" ]	null	2025-01-26T05:34:04Z	--- library_name: peft license: apache-2.0 base_model: EleutherAI/pythia-70m-deduped tags: - axolotl - generated_from_trainer model-index: - name: 746b383b-cead-4efc-9489-de75d155bdb7 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: EleutherAI/pythia-70m-deduped bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 59ebf80954a6130a_train_data.json ds_type: json format: custom path: /workspace/input_data/59ebf80954a6130a_train_data.json type: field_input: solution_steps field_instruction: problem field_output: solution 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: daniel40/746b383b-cead-4efc-9489-de75d155bdb7 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/59ebf80954a6130a_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: 4 sequence_len: 512 special_tokens: pad_token: <\|endoftext\|> 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: f210f656-5c7e-4a29-80ca-643c4317c822 wandb_project: Birthday-SN56-28-Gradients-On-Demand wandb_run: your_name wandb_runid: f210f656-5c7e-4a29-80ca-643c4317c822 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 746b383b-cead-4efc-9489-de75d155bdb7 This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6598 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 15.1112 \| 0.0000 \| 1 \| 3.7280 \| \| 18.1683 \| 0.0001 \| 3 \| 3.7261 \| \| 15.4085 \| 0.0003 \| 6 \| 3.7123 \| \| 13.6085 \| 0.0004 \| 9 \| 3.6598 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ReadyArt/L3.3-Nevoria-R1-70b_EXL2_3.0bpw_H8	ReadyArt	2025-01-26T05:36:45Z	6,227	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1", "base_model:merge:EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1", "base_model:Sao10K/L3.3-70B-Euryale-v2.3", "base_model:merge:Sao10K/L3.3-70B-Euryale-v2.3", "base_model:SicariusSicariiStuff/Negative_LLAMA_70B", "base_model:merge:SicariusSicariiStuff/Negative_LLAMA_70B", "base_model:TheDrummer/Anubis-70B-v1", "base_model:merge:TheDrummer/Anubis-70B-v1", "base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-70B", "base_model:merge:deepseek-ai/DeepSeek-R1-Distill-Llama-70B", "base_model:nbeerbower/Llama-3.1-Nemotron-lorablated-70B", "base_model:merge:nbeerbower/Llama-3.1-Nemotron-lorablated-70B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "exl2", "region:us" ]	text-generation	2025-01-26T05:31:37Z	--- base_model: - nbeerbower/Llama-3.1-Nemotron-lorablated-70B - SicariusSicariiStuff/Negative_LLAMA_70B - TheDrummer/Anubis-70B-v1 - EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1 - deepseek-ai/DeepSeek-R1-Distill-Llama-70B - Sao10K/L3.3-70B-Euryale-v2.3 library_name: transformers tags: - mergekit - merge --- <!DOCTYPE html> <style> ebody { font-family: 'Quicksand', sans-serif; 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border: 1px solid rgba(255, 20, 147, 0.3); border-radius: 8px; padding: 15px; margin-bottom: 15px; } .review-card:last-child { margin-bottom: 0; } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>L3.3-Nevoria-R1-70b</title> <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> <link href="styles.css" rel="stylesheet"> </head> <body> <div class="container"> <div class="header"> <h1>L3.3-Nevoria-R1-70b</h1> </div> <div class="info"> <img src="https://cdn-uploads.huggingface.co/production/uploads/64545af5ec40bbbd01242ca6/_oWpsvCZ-graNKzJBBjGo.jpeg" alt="Model banner"> <div class="creator-section"> <div class="creator-badge"> <span class="creator-label">Created by</span> <a href="https://huggingface.co/Steelskull" target="_blank" class="creator-link"> <span class="creator-name">SteelSkull</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>L3.3-Nevoria-R1-70b</h3> <div class="model-tags"> <span class="model-tag">L3.3 = Llama 3.3</span> <span class="model-tag">R1 = DeepSeek-R1</span> <span class="model-tag">70b Parameters</span> </div> </div> <div class="model-composition"> <h4>Model Composition</h4> <ul class="composition-list"> <li><span class="model-component"><a href="https://huggingface.co/EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1" target="_blank">EVA-LLAMA-0.1</a></span> Storytelling capabilities</li> <li><span class="model-component"><a href="https://huggingface.co/Sao10K/L3.3-70B-Euryale-v2.3" target="_blank">EURYALE-v2.3</a></span> Detailed scene descriptions</li> <li><span class="model-component"><a href="https://huggingface.co/TheDrummer/Anubis-70B-v1" target="_blank">Anubis-v1</a></span> Enhanced prose details</li> <li><span class="model-component"><a href="https://huggingface.co/SicariusSicariiStuff/Negative_LLAMA_70B" target="_blank">Negative_LLAMA</a></span> Reduced positive bias</li> <li><span class="model-component"><a href="https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B" target="_blank">DeepSeek-R1-Distill-Llama-70B</a></span> Increased Intelligence / Dialog / Awareness</li> <li><span class="model-component base-model"><a href="https://huggingface.co/nbeerbower/Llama-3.1-Nemotron-lorablated-70B" target="_blank">Nemotron-lorablated</a></span> Base model</li> </ul> </div> <div class="model-description"> <p>This model builds upon the original Nevoria foundation, incorporating the Deepseek-R1 reasoning architecture to enhance dialogue interaction and scene comprehension. While maintaining Nevoria's core strengths in storytelling and scene description (derived from EVA, EURYALE, and Anubis), this iteration aims to improve prompt adherence and creative reasoning capabilities. The model also retains the balanced perspective introduced by Negative_LLAMA and Nemotron elements. Also, the model plays the card to almost a fault, It'll pick up on minor issues and attempt to run with them. Users had it call them out for misspelling a word while playing in character. </p> <p>Note: While Nevoria-R1 represents a significant architectural change, rather than a direct successor to Nevoria, it operates as a distinct model with its own characteristics.</p> <p>The lorablated model base choice was intentional, creating unique weight interactions similar to the original <a href="https://huggingface.co/Steelskull/L3-MS-Astoria-70b" target="_blank">Astoria model</a> and <a href="https://huggingface.co/Steelskull/L3.1-MS-Astoria-70b-v2" target="_blank">Astoria V2 model</a>. This "weight twisting" effect, achieved by subtracting the lorablated base model during merging, creates an interesting balance in the model's behavior. While unconventional compared to sequential component application, this approach was chosen for its unique response characteristics.</p> </div> </div> <!--<div class="metrics-section"> <details open> <summary>User Reviews</summary> <div class="progress-metrics"> <div> <div class="review-card"> <div> <span>@Geechan - Discord</span> </div> <p>@Steel Have only briefly tested so far, but you really cooked up an amazing merge with this one, and I mean that wholeheartedly. Insane creativity, perfect character adherence and dialogue, loves to slow burn and take its time, minimal sloppy patterns and writing, and such a breath of fresh air in many ways. I'm enjoying my results with 1 temp and 0.99 TFS (close to something like 0.015 min P). Letting the model be creative and wild is so fun and makes me want to RP more.<br><br>No positivity bias either; violent scenes will result in my death and/or suffering, as they should, and I don't see any soft refusals either. ERP has no skimming of details or refusals like you see on some other L3.3 tunes too</p> </div> <div class="review-card"> <div> <span>IGODZOL - Huggingface</span> </div> <p>I honestly have no idea why (maybe the negative llama is having that great of an influence) but this merge is miles above the individual tunes that went into making it. Good sir, this model has just become my daily driver. Chapeau bas</p> </div> <div class="review-card"> <div> <span>@thana_alt - Discord</span> </div> <p>I'm thoroughly impressed by this merge of Llama 3.3. It successfully addresses the positivity bias prevalent in the base Llama model, ensuring a more accurate and balanced response. The adherence to system prompts is also notable, with the model demonstrating a keen understanding of context and instruction.<br><br>The prose generated by this model is truly exceptional - it's almost as if a skilled chef has carefully crafted each sentence to create a rich and immersive experience. I put this to the test in an adventure scenario, where I had about 10,000 tokens of lorebooks and was managing nine characters simultaneously. Despite the complexity, the model performed flawlessly, keeping track of each character's location and activity without any confusion - even when they were in different locations.<br><br>I also experimented with an astral projection type of power, and was impressed to see that the model accurately discerned that I wasn't physically present in a particular location. Another significant advantage of this model is the lack of impersonation issues, allowing for seamless role-playing and storytelling.<br><br>The capacity of this model is equally impressive, as I was able to load up to 110,000 tokens without encountering any issues. In fact, I successfully tested it with up to 70,000 tokens without experiencing any breakdown or degradation in performance.<br><br>When combined with the "The Inception Presets - Methception Llamaception Qwenception" prompt preset from https://huggingface.co/Konnect1221/ , this model truly shines, bringing out the best in the Llama 3.3 architecture. Overall, I'm extremely satisfied with this merge and would highly recommend it to anyone looking to elevate their storytelling and role-playing experiences.</p> </div> </div> </div> </details> </div>--> </div> <!-- UGI-Benchmark Results (Temporarily Hidden) <h2>UGI-Benchmark Results:</h2> <div class="benchmark-container"> <div class="benchmark-notification"> <div class="notification-content"> <span class="notification-icon">🏆</span> <span class="notification-text"> Highest ranked 70b as of 01/17/2025. <a href="https://huggingface.co/spaces/DontPlanToEnd/UGI-Leaderboard" target="_blank" class="benchmark-link"> View Full Leaderboard → </a> </span> </div> </div> <div class="metrics-section"> <h3>Core Metrics</h3> <div class="core-metrics-grid"> <div class="metric-box"> <span class="label">UGI Score</span> <span class="value">56.75</span> </div> <div class="metric-box"> <span class="label">Willingness Score</span> <span class="value">7.5/10</span> </div> <div class="metric-box"> <span class="label">Natural Intelligence</span> <span class="value">41.09</span> </div> <div class="metric-box"> <span class="label">Coding Ability</span> <span class="value">20</span> </div> </div> </div> <div class="metrics-section"> <h3>Model Information</h3> <div class="info-grid"> <div class="metric-box"> <span class="label">Political Lean</span> <span class="value">-8.1%</span> </div> <div class="metric-box"> <span class="label">Ideology</span> <span class="value">Liberalism</span> </div> <div class="metric-box"> <span class="label">Parameters</span> <span class="value">70B</span> </div> </div> </div> <div class="metrics-section"> <details> <summary>Aggregated Scores</summary> <div class="progress-metrics"> <div class="progress-metric"> <div class="progress-label"> <span>Diplomacy</span> <span class="progress-value">61.9%</span> </div> <div class="progress-bar"> <div class="progress-fill" style="width: 61.9%"></div> </div> </div> <div class="progress-metric"> <div class="progress-label"> <span>Government</span> <span class="progress-value">45.9%</span> </div> <div class="progress-bar"> <div class="progress-fill" style="width: 45.9%"></div> </div> </div> <div class="progress-metric"> <div class="progress-label"> <span>Economy</span> <span class="progress-value">43.9%</span> </div> <div class="progress-bar"> <div class="progress-fill" style="width: 43.9%"></div> </div> </div> <div class="progress-metric"> <div class="progress-label"> <span>Society</span> <span class="progress-value">60.1%</span> </div> <div class="progress-bar"> <div class="progress-fill" style="width: 60.1%"></div> </div> </div> </div> </details> </div> <div class="metrics-section"> <details> <summary>Individual Scores</summary> <div class="progress-metrics"> <div class="progress-metric split"> <div class="progress-label"> <span>Federal</span> <span class="progress-value">44.2%</span> <span>Unitary</span> </div> <div class="progress-bar split"> <div class="progress-fill-left" style="width: 22.1%"></div> <div class="progress-fill-right" style="width: 27.9%"></div> </div> </div> <div class="progress-metric split"> <div class="progress-label"> <span>Democratic</span> <span class="progress-value">66.2%</span> <span>Autocratic</span> </div> <div class="progress-bar split"> <div class="progress-fill-left" style="width: 33.1%"></div> <div class="progress-fill-right" style="width: 16.9%"></div> </div> </div> <div class="progress-metric split"> <div class="progress-label"> <span>Security</span> <span class="progress-value">48.1%</span> <span>Freedom</span> </div> <div class="progress-bar split"> <div class="progress-fill-left" style="width: 24.05%"></div> <div class="progress-fill-right" style="width: 25.95%"></div> </div> </div> <div class="progress-metric split"> <div class="progress-label"> <span>Nationalism</span> <span class="progress-value">40.4%</span> <span>Int'l</span> </div> <div class="progress-bar split"> <div class="progress-fill-left" style="width: 20.2%"></div> <div class="progress-fill-right" style="width: 29.8%"></div> </div> </div> <div class="progress-metric split"> <div class="progress-label"> <span>Militarist</span> <span class="progress-value">30.4%</span> <span>Pacifist</span> </div> <div class="progress-bar split"> <div class="progress-fill-left" style="width: 15.2%"></div> <div class="progress-fill-right" style="width: 34.8%"></div> </div> </div> <div class="progress-metric split"> <div class="progress-label"> <span>Assimilationist</span> <span class="progress-value">43.3%</span> <span>Multiculturalist</span> </div> <div class="progress-bar split"> <div class="progress-fill-left" style="width: 21.65%"></div> <div class="progress-fill-right" style="width: 28.35%"></div> </div> </div> <div class="progress-metric split"> <div class="progress-label"> <span>Collectivize</span> <span class="progress-value">43.8%</span> <span>Privatize</span> </div> <div class="progress-bar split"> <div class="progress-fill-left" style="width: 21.9%"></div> <div class="progress-fill-right" style="width: 28.1%"></div> </div> </div> <div class="progress-metric split"> <div class="progress-label"> <span>Planned</span> <span class="progress-value">43.1%</span> <span>LaissezFaire</span> </div> <div class="progress-bar split"> <div class="progress-fill-left" style="width: 21.55%"></div> <div class="progress-fill-right" style="width: 28.45%"></div> </div> </div> <div class="progress-metric split"> <div class="progress-label"> <span>Isolationism</span> <span class="progress-value">44.8%</span> <span>Globalism</span> </div> <div class="progress-bar split"> <div class="progress-fill-left" style="width: 22.4%"></div> <div class="progress-fill-right" style="width: 27.6%"></div> </div> </div> <div class="progress-metric split"> <div class="progress-label"> <span>Irreligious</span> <span class="progress-value">55.4%</span> <span>Religious</span> </div> <div class="progress-bar split"> <div class="progress-fill-left" style="width: 27.7%"></div> <div class="progress-fill-right" style="width: 22.3%"></div> </div> </div> <div class="progress-metric split"> <div class="progress-label"> <span>Progressive</span> <span class="progress-value">59.6%</span> <span>Traditional</span> </div> <div class="progress-bar split"> <div class="progress-fill-left" style="width: 29.8%"></div> <div class="progress-fill-right" style="width: 20.2%"></div> </div> </div> <div class="progress-metric split"> <div class="progress-label"> <span>Acceleration</span> <span class="progress-value">65.2%</span> <span>Bioconservative</span> </div> <div class="progress-bar split"> <div class="progress-fill-left" style="width: 32.6%"></div> <div class="progress-fill-right" style="width: 17.4%"></div> </div> </div> </div> </details> </div> </div> --> <!-- Open LLM-Benchmark Results (Temporarily Hidden) <h2>Open LLM-Benchmark Results:</h2> <div class="benchmark-container"> <div class="benchmark-notification"> <div class="notification-content"> <span class="notification-text"> Average Score: 43.92% <a href="https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?rankingMode=dynamic" target="_blank" class="benchmark-link"> View Full Leaderboard → </a> </span> </div> </div> <div class="progress-metrics"> <div class="progress-metric"> <div class="progress-label"> <span>IFEval</span> <span class="progress-value">69.63%</span> </div> <div class="progress-bar"> <div class="progress-fill" style="width: 69.63%"></div> </div> </div> <div class="progress-metric"> <div class="progress-label"> <span>BBH</span> <span class="progress-value">56.60%</span> </div> <div class="progress-bar"> <div class="progress-fill" style="width: 56.60%"></div> </div> </div> <div class="progress-metric"> <div class="progress-label"> <span>MATH</span> <span class="progress-value">38.82%</span> </div> <div class="progress-bar"> <div class="progress-fill" style="width: 38.82%"></div> </div> </div> <div class="progress-metric"> <div class="progress-label"> <span>GPQA</span> <span class="progress-value">29.42%</span> </div> <div class="progress-bar"> <div class="progress-fill" style="width: 29.42%"></div> </div> </div> <div class="progress-metric"> <div class="progress-label"> <span>MUSR</span> <span class="progress-value">18.63%</span> </div> <div class="progress-bar"> <div class="progress-fill" style="width: 18.63%"></div> </div> </div> <div class="progress-metric"> <div class="progress-label"> <span>MMLU-Pro</span> <span class="progress-value">50.39%</span> </div> <div class="progress-bar"> <div class="progress-fill" style="width: 50.39%"></div> </div> </div> </div> </div> --> <div class="section-container"> <h2>Reccomended Templates & Prompts</h2> <div class="template-card"> <div class="template-item"> <div class="template-content"> <a href="https://huggingface.co/Konnect1221/Methception-Llamaception-SillyTavern-Preset" target="_blank" class="template-link"> LLam@ception <span class="link-arrow">→</span> </a> <span class="template-author">by @.konnect</span> </div> </div> </div> </div> <div class="section-container"> <h2>Quantized Versions</h2> <div class="quantized-container"> <div class="quantized-section"> <h3>GGUF Quantizations</h3> <div class="quantized-items"> <!--<div class="quantized-item"> <span class="author">bartowski</span> <a href="https://huggingface.co/bartowski/L3.3-Exp-Nevoria-R1-70b-GGUF" target="_blank"> Combined-GGUF <span class="link-arrow">→</span> </a> </div>--> <div class="quantized-item"> <span class="author">mradermacher</span> <div class="multi-links"> <a href="https://huggingface.co/mradermacher/L3.3-Exp-Nevoria-R1-70b-GGUF" target="_blank"> GGUF <span class="link-arrow">→</span> </a> <span class="separator">//</span> <a href="https://huggingface.co/mradermacher/L3.3-Exp-Nevoria-R1-70b-i1-GGUF" target="_blank"> Imat-GGUF <span class="link-arrow">→</span> </a> </div> </div> </div> </div> <div class="quantized-section"> <h3>EXL2 Quantizations</h3> <div class="quantized-items"> <div class="quantized-item"> <span class="author">Darkhn</span> <a href="https://huggingface.co/Darkhn/Steelskull_L3.3-Exp-Nevoria-R1-70b-6.0bpw-h8-exl2" target="_blank"> 6.0BPW-EXL2 <span class="link-arrow">→</span> </a> </div> </div> </div> </div> </div> <div class="support-section"> <h2>Support the Project:</h2> <a href="https://ko-fi.com/Y8Y0AO2XE" target="_blank" class="button"> Support on Ko-fi </a> </div> </div> </div> </body> </html>
tarabukinivan/4ec47c67-4358-4f52-a142-1d6f35c3ec00	tarabukinivan	2025-01-26T05:35:21Z	6	0	peft	[ "peft", "safetensors", "bloom", "axolotl", "generated_from_trainer", "base_model:bigscience/bloomz-560m", "base_model:adapter:bigscience/bloomz-560m", "license:bigscience-bloom-rail-1.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T05:11:48Z	--- library_name: peft license: bigscience-bloom-rail-1.0 base_model: bigscience/bloomz-560m tags: - axolotl - generated_from_trainer model-index: - name: 4ec47c67-4358-4f52-a142-1d6f35c3ec00 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: bigscience/bloomz-560m bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - eb75b6ffdc77ea4d_train_data.json ds_type: json format: custom path: /workspace/input_data/eb75b6ffdc77ea4d_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: null eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: tarabukinivan/4ec47c67-4358-4f52-a142-1d6f35c3ec00 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 75GiB max_steps: 30 micro_batch_size: 2 mlflow_experiment_name: /tmp/eb75b6ffdc77ea4d_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 15 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 888b9795-bd3d-4c1e-9289-4c99ad92b728 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 888b9795-bd3d-4c1e-9289-4c99ad92b728 warmup_steps: 15 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 4ec47c67-4358-4f52-a142-1d6f35c3ec00 This model is a fine-tuned version of [bigscience/bloomz-560m](https://huggingface.co/bigscience/bloomz-560m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1234 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 15 - training_steps: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| 2.6177 \| \| 11.15 \| 0.0004 \| 5 \| 2.5755 \| \| 9.6405 \| 0.0008 \| 10 \| 2.4382 \| \| 9.4943 \| 0.0012 \| 15 \| 2.2658 \| \| 9.1467 \| 0.0016 \| 20 \| 2.1733 \| \| 9.4334 \| 0.0020 \| 25 \| 2.1324 \| \| 7.9504 \| 0.0024 \| 30 \| 2.1234 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
aleegis10/8ed9fe91-c2b5-42ca-8efb-927b4c8fbf45	aleegis10	2025-01-26T05:33:31Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:adapter:unsloth/Meta-Llama-3.1-8B-Instruct", "license:llama3.1", "region:us" ]	null	2025-01-26T01:51:05Z	--- library_name: peft license: llama3.1 base_model: unsloth/Meta-Llama-3.1-8B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 8ed9fe91-c2b5-42ca-8efb-927b4c8fbf45 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/Meta-Llama-3.1-8B-Instruct bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - c9e5168aaf615a7c_train_data.json ds_type: json format: custom path: /workspace/input_data/c9e5168aaf615a7c_train_data.json type: field_instruction: problem field_output: target_answer format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 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: true hub_model_id: aleegis10/8ed9fe91-c2b5-42ca-8efb-927b4c8fbf45 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/c9e5168aaf615a7c_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 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: 50 saves_per_epoch: null sequence_len: 1024 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: c81d855a-5c46-46dc-bab6-9f15fcbfa230 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: c81d855a-5c46-46dc-bab6-9f15fcbfa230 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 8ed9fe91-c2b5-42ca-8efb-927b4c8fbf45 This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0841 ## 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: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 7.1393 \| 0.0001 \| 1 \| 8.7350 \| \| 1.3381 \| 0.0027 \| 50 \| 1.0531 \| \| 1.0026 \| 0.0053 \| 100 \| 0.5982 \| \| 0.5522 \| 0.0080 \| 150 \| 0.1816 \| \| 0.238 \| 0.0107 \| 200 \| 0.0841 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
great0001/b3c547c0-65a1-4b34-9ff7-cb605ef4e576	great0001	2025-01-26T05:25:03Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct", "base_model:adapter:aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct", "license:llama3", "region:us" ]	null	2025-01-26T05:21:30Z	--- library_name: peft license: llama3 base_model: aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct tags: - axolotl - generated_from_trainer model-index: - name: b3c547c0-65a1-4b34-9ff7-cb605ef4e576 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: aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 90ff401367e42c67_train_data.json ds_type: json format: custom path: /workspace/input_data/90ff401367e42c67_train_data.json type: field_instruction: prompt field_output: y_true 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: great0001/b3c547c0-65a1-4b34-9ff7-cb605ef4e576 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/90ff401367e42c67_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: 4 sequence_len: 512 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: af8ec97d-9490-4745-9a2d-3693291921a2 wandb_project: Mine-SN56-20-Gradients-On-Demand wandb_run: your_name wandb_runid: af8ec97d-9490-4745-9a2d-3693291921a2 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # b3c547c0-65a1-4b34-9ff7-cb605ef4e576 This model is a fine-tuned version of [aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct](https://huggingface.co/aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0003 \| 1 \| nan \| \| 0.0 \| 0.0009 \| 3 \| nan \| \| 0.0 \| 0.0017 \| 6 \| nan \| \| 0.0 \| 0.0026 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
yfarm01/sn29_jan26_c1	yfarm01	2025-01-26T05:24:37Z	38	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-26T05:18:18Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
gavrilstep/0ae222fe-9a6e-42b6-a140-4981d2315b4c	gavrilstep	2025-01-26T05:23:49Z	6	0	peft	[ "peft", "safetensors", "olmo", "axolotl", "generated_from_trainer", "base_model:katuni4ka/tiny-random-olmo-hf", "base_model:adapter:katuni4ka/tiny-random-olmo-hf", "4-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T05:22:02Z	--- library_name: peft base_model: katuni4ka/tiny-random-olmo-hf tags: - axolotl - generated_from_trainer model-index: - name: 0ae222fe-9a6e-42b6-a140-4981d2315b4c 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: katuni4ka/tiny-random-olmo-hf bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 558c519d44160381_train_data.json ds_type: json format: custom path: /workspace/input_data/558c519d44160381_train_data.json type: field_instruction: question field_output: answers format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: null eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: gavrilstep/0ae222fe-9a6e-42b6-a140-4981d2315b4c hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 75GiB max_steps: 30 micro_batch_size: 2 mlflow_experiment_name: /tmp/558c519d44160381_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: e2176481-25d5-4e19-9520-315ccb160b4d wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: e2176481-25d5-4e19-9520-315ccb160b4d warmup_steps: 10 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 0ae222fe-9a6e-42b6-a140-4981d2315b4c This model is a fine-tuned version of [katuni4ka/tiny-random-olmo-hf](https://huggingface.co/katuni4ka/tiny-random-olmo-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 10.8124 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| 10.8379 \| \| 10.8326 \| 0.0006 \| 5 \| 10.8365 \| \| 10.8302 \| 0.0012 \| 10 \| 10.8314 \| \| 10.8233 \| 0.0018 \| 15 \| 10.8235 \| \| 10.8181 \| 0.0024 \| 20 \| 10.8167 \| \| 10.8121 \| 0.0030 \| 25 \| 10.8131 \| \| 10.8134 \| 0.0036 \| 30 \| 10.8124 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
hongngo/2a46d83a-7ce6-4b32-a2d7-d5ebcbbc8ee5	hongngo	2025-01-26T05:23:33Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:defog/sqlcoder-7b-2", "base_model:adapter:defog/sqlcoder-7b-2", "license:cc-by-sa-4.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T05:05:26Z	--- library_name: peft license: cc-by-sa-4.0 base_model: defog/sqlcoder-7b-2 tags: - axolotl - generated_from_trainer model-index: - name: 2a46d83a-7ce6-4b32-a2d7-d5ebcbbc8ee5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: defog/sqlcoder-7b-2 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 6b30f33bbd9cba22_train_data.json ds_type: json format: custom path: /workspace/input_data/6b30f33bbd9cba22_train_data.json type: field_input: reasoning field_instruction: question field_output: answer 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: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: hongngo/2a46d83a-7ce6-4b32-a2d7-d5ebcbbc8ee5 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/6b30f33bbd9cba22_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 66ffa688-b6ab-4800-bb73-500be3c51df8 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 66ffa688-b6ab-4800-bb73-500be3c51df8 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 2a46d83a-7ce6-4b32-a2d7-d5ebcbbc8ee5 This model is a fine-tuned version of [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0292 ## 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: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0128 \| 0.1938 \| 200 \| 0.0292 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mrferr3t/f34575b0-3b2d-40b4-b5ff-6bca3085df2b	mrferr3t	2025-01-26T05:23:30Z	6	0	peft	[ "peft", "safetensors", "olmo", "axolotl", "generated_from_trainer", "base_model:katuni4ka/tiny-random-olmo-hf", "base_model:adapter:katuni4ka/tiny-random-olmo-hf", "region:us" ]	null	2025-01-26T05:22:50Z	--- library_name: peft base_model: katuni4ka/tiny-random-olmo-hf tags: - axolotl - generated_from_trainer model-index: - name: f34575b0-3b2d-40b4-b5ff-6bca3085df2b 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: katuni4ka/tiny-random-olmo-hf bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 558c519d44160381_train_data.json ds_type: json format: custom path: /workspace/input_data/558c519d44160381_train_data.json type: field_instruction: question field_output: answers 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: mrferr3t/f34575b0-3b2d-40b4-b5ff-6bca3085df2b hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/558c519d44160381_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: 4 sequence_len: 512 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: e2176481-25d5-4e19-9520-315ccb160b4d wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: e2176481-25d5-4e19-9520-315ccb160b4d warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # f34575b0-3b2d-40b4-b5ff-6bca3085df2b This model is a fine-tuned version of [katuni4ka/tiny-random-olmo-hf](https://huggingface.co/katuni4ka/tiny-random-olmo-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 10.8372 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use adamw_bnb_8bit 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: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 10.8278 \| 0.0001 \| 1 \| 10.8396 \| \| 10.8379 \| 0.0004 \| 3 \| 10.8395 \| \| 10.8364 \| 0.0007 \| 6 \| 10.8386 \| \| 10.8355 \| 0.0011 \| 9 \| 10.8372 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
thangla01/5e2223e4-ee59-46f7-870f-9b5a963a98cc	thangla01	2025-01-26T05:23:14Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:defog/sqlcoder-7b-2", "base_model:adapter:defog/sqlcoder-7b-2", "license:cc-by-sa-4.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T05:05:21Z	--- library_name: peft license: cc-by-sa-4.0 base_model: defog/sqlcoder-7b-2 tags: - axolotl - generated_from_trainer model-index: - name: 5e2223e4-ee59-46f7-870f-9b5a963a98cc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: defog/sqlcoder-7b-2 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 6b30f33bbd9cba22_train_data.json ds_type: json format: custom path: /workspace/input_data/6b30f33bbd9cba22_train_data.json type: field_input: reasoning field_instruction: question field_output: answer 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: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: thangla01/5e2223e4-ee59-46f7-870f-9b5a963a98cc hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/6b30f33bbd9cba22_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 66ffa688-b6ab-4800-bb73-500be3c51df8 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 66ffa688-b6ab-4800-bb73-500be3c51df8 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 5e2223e4-ee59-46f7-870f-9b5a963a98cc This model is a fine-tuned version of [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0294 ## 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: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0129 \| 0.1938 \| 200 \| 0.0294 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
poooj/MuRILHateSpeechClassification	poooj	2025-01-26T05:22:13Z	17	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google/muril-base-cased", "base_model:finetune:google/muril-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-01-26T05:05:09Z	--- library_name: transformers license: apache-2.0 base_model: google/muril-base-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: MuRILHateSpeechClassification 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. --> # MuRILHateSpeechClassification This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7371 - Accuracy: 0.8407 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5519 \| 1.0 \| 1137 \| 0.4704 \| 0.8110 \| \| 0.4345 \| 2.0 \| 2274 \| 0.4862 \| 0.8198 \| \| 0.3547 \| 3.0 \| 3411 \| 0.4660 \| 0.8473 \| \| 0.2919 \| 4.0 \| 4548 \| 0.6066 \| 0.8440 \| \| 0.2205 \| 5.0 \| 5685 \| 0.6805 \| 0.8429 \| \| 0.1759 \| 6.0 \| 6822 \| 0.7371 \| 0.8407 \| ### Framework versions - Transformers 4.48.1 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
myhaaaaaaa/8e996502-1c66-4215-9c96-d9251f52de11	myhaaaaaaa	2025-01-26T05:21:47Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:defog/sqlcoder-7b-2", "base_model:adapter:defog/sqlcoder-7b-2", "license:cc-by-sa-4.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T05:05:22Z	--- library_name: peft license: cc-by-sa-4.0 base_model: defog/sqlcoder-7b-2 tags: - axolotl - generated_from_trainer model-index: - name: 8e996502-1c66-4215-9c96-d9251f52de11 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: defog/sqlcoder-7b-2 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 6b30f33bbd9cba22_train_data.json ds_type: json format: custom path: /workspace/input_data/6b30f33bbd9cba22_train_data.json type: field_input: reasoning field_instruction: question field_output: answer 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: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: myhaaaaaaa/8e996502-1c66-4215-9c96-d9251f52de11 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/6b30f33bbd9cba22_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 66ffa688-b6ab-4800-bb73-500be3c51df8 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 66ffa688-b6ab-4800-bb73-500be3c51df8 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 8e996502-1c66-4215-9c96-d9251f52de11 This model is a fine-tuned version of [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0293 ## 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: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0125 \| 0.1938 \| 200 \| 0.0293 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Theros/L3-ColdBrew-R1-test1	Theros	2025-01-26T05:21:08Z	13	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Theros/L3-ColdBrew-Daybreak", "base_model:merge:Theros/L3-ColdBrew-Daybreak", "base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "base_model:merge:deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-26T05:18:06Z	--- base_model: - Theros/L3-ColdBrew-Daybreak - deepseek-ai/DeepSeek-R1-Distill-Llama-8B 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 [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Theros/L3-ColdBrew-Daybreak](https://huggingface.co/Theros/L3-ColdBrew-Daybreak) as a base. ### Models Merged The following models were included in the merge: * [deepseek-ai/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Theros/L3-ColdBrew-Daybreak parameters: density: 0.4 weight: 0.4 - model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B parameters: density: 0.6 weight: 0.6 merge_method: dare_ties base_model: Theros/L3-ColdBrew-Daybreak parameters: normalize: false int8_mask: true dtype: bfloat16 ```
athul8129/Llama_tuned_bot	athul8129	2025-01-26T05:20:48Z	26	0	null	[ "pytorch", "llama", "unsloth", "trl", "sft", "license:mit", "region:us" ]	null	2025-01-26T05:15:37Z	--- license: mit tags: - unsloth - trl - sft ---
trenden/5e31c4dc-0485-421d-b4d2-f3b56c011a3c	trenden	2025-01-26T05:20:31Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "base_model:adapter:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "license:llama3", "region:us" ]	null	2025-01-26T05:19:26Z	--- library_name: peft license: llama3 base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1 tags: - axolotl - generated_from_trainer model-index: - name: 5e31c4dc-0485-421d-b4d2-f3b56c011a3c 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: tokyotech-llm/Llama-3-Swallow-8B-v0.1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - b355c3ff95258244_train_data.json ds_type: json format: custom path: /workspace/input_data/b355c3ff95258244_train_data.json type: field_instruction: input field_output: output 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: trenden/5e31c4dc-0485-421d-b4d2-f3b56c011a3c hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/b355c3ff95258244_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: 4 sequence_len: 512 special_tokens: pad_token: <\|end_of_text\|> 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: 22487750-366e-41ca-8395-d8629638fd03 wandb_project: Birthday-SN56-3-Gradients-On-Demand wandb_run: your_name wandb_runid: 22487750-366e-41ca-8395-d8629638fd03 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 5e31c4dc-0485-421d-b4d2-f3b56c011a3c This model is a fine-tuned version of [tokyotech-llm/Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2272 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5738 \| 0.0028 \| 1 \| 1.4206 \| \| 1.4099 \| 0.0085 \| 3 \| 1.3866 \| \| 1.209 \| 0.0170 \| 6 \| 0.7931 \| \| 0.3992 \| 0.0255 \| 9 \| 0.2272 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
jaspionjader/Kosmos-EVAA-immersive-sof-v44-8B-Q5_K_M-GGUF	jaspionjader	2025-01-26T05:13:05Z	25	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:jaspionjader/Kosmos-EVAA-immersive-sof-v44-8B", "base_model:quantized:jaspionjader/Kosmos-EVAA-immersive-sof-v44-8B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-26T03:37:17Z	--- base_model: jaspionjader/sof-15 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # jaspionjader/sof-15-Q5_K_M-GGUF This model was converted to GGUF format from [`jaspionjader/sof-15`](https://huggingface.co/jaspionjader/sof-15) 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/jaspionjader/sof-15) 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 jaspionjader/sof-15-Q5_K_M-GGUF --hf-file sof-15-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo jaspionjader/sof-15-Q5_K_M-GGUF --hf-file sof-15-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 jaspionjader/sof-15-Q5_K_M-GGUF --hf-file sof-15-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo jaspionjader/sof-15-Q5_K_M-GGUF --hf-file sof-15-q5_k_m.gguf -c 2048 ```
visdata/po9	visdata	2025-01-26T05:12:29Z	36	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-26T05:06:58Z	--- 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. 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mradermacher/Distilled-Whiskey-8b-i1-GGUF	mradermacher	2025-01-26T05:11:43Z	548	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Triangle104/Distilled-Whiskey-8b", "base_model:quantized:Triangle104/Distilled-Whiskey-8b", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-01-26T04:23:50Z	--- base_model: Triangle104/Distilled-Whiskey-8b language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Triangle104/Distilled-Whiskey-8b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Distilled-Whiskey-8b-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/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 3.1 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.8 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-i1-GGUF/resolve/main/Distilled-Whiskey-8b.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| 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 -->
mlx-community/Bio-Medical-Llama-3-8B	mlx-community	2025-01-26T05:11:40Z	40	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "medical", "Healthcare & Lifesciences", "BioMed", "mlx", "conversational", "dataset:collaiborateorg/BioMedData", "base_model:ContactDoctor/Bio-Medical-Llama-3-8B", "base_model:quantized:ContactDoctor/Bio-Medical-Llama-3-8B", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	2025-01-22T23:59:42Z	--- base_model: ContactDoctor/Bio-Medical-Llama-3-8B datasets: - collaiborateorg/BioMedData library_name: transformers license: other tags: - generated_from_trainer - medical - Healthcare & Lifesciences - BioMed - mlx thumbnail: https://collaiborate.com/logo/logo-blue-bg-1.png model-index: - name: Bio-Medical-Llama-3-8B results: [] --- # mlx-community/Bio-Medical-Llama-3-8B The Model [mlx-community/Bio-Medical-Llama-3-8B](https://huggingface.co/mlx-community/Bio-Medical-Llama-3-8B) was converted to MLX format from [ContactDoctor/Bio-Medical-Llama-3-8B](https://huggingface.co/ContactDoctor/Bio-Medical-Llama-3-8B) using mlx-lm version 0.20.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Bio-Medical-Llama-3-8B") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
prxy5604/7445cb16-0eef-48b2-af32-ab3c72b852f7	prxy5604	2025-01-26T05:11:09Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Korabbit/llama-2-ko-7b", "base_model:adapter:Korabbit/llama-2-ko-7b", "region:us" ]	null	2025-01-26T03:11:59Z	--- library_name: peft base_model: Korabbit/llama-2-ko-7b tags: - axolotl - generated_from_trainer model-index: - name: 7445cb16-0eef-48b2-af32-ab3c72b852f7 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: Korabbit/llama-2-ko-7b bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - c9c324e8cf5586e6_train_data.json ds_type: json format: custom path: /workspace/input_data/c9c324e8cf5586e6_train_data.json type: field_instruction: instruction field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 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: true hub_model_id: prxy5604/7445cb16-0eef-48b2-af32-ab3c72b852f7 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/c9c324e8cf5586e6_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 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: 50 saves_per_epoch: null sequence_len: 1024 special_tokens: pad_token: </s> 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: d35b96a9-b8d1-49c0-b1a8-167bc6103694 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: d35b96a9-b8d1-49c0-b1a8-167bc6103694 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 7445cb16-0eef-48b2-af32-ab3c72b852f7 This model is a fine-tuned version of [Korabbit/llama-2-ko-7b](https://huggingface.co/Korabbit/llama-2-ko-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0289 ## 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: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.2163 \| 0.0001 \| 1 \| 1.5556 \| \| 1.3118 \| 0.0056 \| 50 \| 1.1196 \| \| 1.5438 \| 0.0112 \| 100 \| 1.0590 \| \| 1.2789 \| 0.0168 \| 150 \| 1.0345 \| \| 1.0569 \| 0.0224 \| 200 \| 1.0289 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso05/768d9d37-47e2-4a24-a3a6-855337d44150	lesso05	2025-01-26T05:10:09Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:defog/sqlcoder-7b-2", "base_model:adapter:defog/sqlcoder-7b-2", "license:cc-by-sa-4.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T05:05:29Z	--- library_name: peft license: cc-by-sa-4.0 base_model: defog/sqlcoder-7b-2 tags: - axolotl - generated_from_trainer model-index: - name: 768d9d37-47e2-4a24-a3a6-855337d44150 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: defog/sqlcoder-7b-2 bf16: true chat_template: llama3 datasets: - data_files: - 6b30f33bbd9cba22_train_data.json ds_type: json format: custom path: /workspace/input_data/6b30f33bbd9cba22_train_data.json type: field_input: reasoning field_instruction: question field_output: answer format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 2 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: lesso05/768d9d37-47e2-4a24-a3a6-855337d44150 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 25 micro_batch_size: 2 mlflow_experiment_name: /tmp/6b30f33bbd9cba22_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 save_steps: 10 sequence_len: 512 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 66ffa688-b6ab-4800-bb73-500be3c51df8 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 66ffa688-b6ab-4800-bb73-500be3c51df8 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 768d9d37-47e2-4a24-a3a6-855337d44150 This model is a fine-tuned version of [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0010 \| 1 \| nan \| \| 0.0 \| 0.0048 \| 5 \| nan \| \| 0.0 \| 0.0097 \| 10 \| nan \| \| 0.0 \| 0.0145 \| 15 \| nan \| \| 0.0 \| 0.0194 \| 20 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/Distilled-Whiskey-8b-GGUF	mradermacher	2025-01-26T05:09:58Z	301	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Triangle104/Distilled-Whiskey-8b", "base_model:quantized:Triangle104/Distilled-Whiskey-8b", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-26T04:07:00Z	--- base_model: Triangle104/Distilled-Whiskey-8b language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/Triangle104/Distilled-Whiskey-8b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Distilled-Whiskey-8b-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/Distilled-Whiskey-8b-GGUF/resolve/main/Distilled-Whiskey-8b.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-GGUF/resolve/main/Distilled-Whiskey-8b.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-GGUF/resolve/main/Distilled-Whiskey-8b.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-GGUF/resolve/main/Distilled-Whiskey-8b.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-GGUF/resolve/main/Distilled-Whiskey-8b.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-GGUF/resolve/main/Distilled-Whiskey-8b.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-GGUF/resolve/main/Distilled-Whiskey-8b.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-GGUF/resolve/main/Distilled-Whiskey-8b.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-GGUF/resolve/main/Distilled-Whiskey-8b.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-GGUF/resolve/main/Distilled-Whiskey-8b.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-GGUF/resolve/main/Distilled-Whiskey-8b.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Distilled-Whiskey-8b-GGUF/resolve/main/Distilled-Whiskey-8b.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
visdata/po6	visdata	2025-01-26T05:09:32Z	47	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-26T05:04:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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prxy5606/b8238061-fca5-4e80-a7d0-9005e716688e	prxy5606	2025-01-26T05:07:59Z	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-01-26T04:34:54Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: b8238061-fca5-4e80-a7d0-9005e716688e 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: Qwen/Qwen2.5-1.5B-Instruct bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 4ff4d8b7c7e542b6_train_data.json ds_type: json format: custom path: /workspace/input_data/4ff4d8b7c7e542b6_train_data.json type: field_input: code field_instruction: func_name field_output: docstring format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 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: true hub_model_id: prxy5606/b8238061-fca5-4e80-a7d0-9005e716688e hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/4ff4d8b7c7e542b6_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 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: 50 saves_per_epoch: null sequence_len: 1024 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: 8e87b1be-d0e2-427a-97a7-6e294f6c6fe8 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 8e87b1be-d0e2-427a-97a7-6e294f6c6fe8 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # b8238061-fca5-4e80-a7d0-9005e716688e This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7811 ## 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: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.4319 \| 0.0002 \| 1 \| 3.3060 \| \| 1.9963 \| 0.0093 \| 50 \| 1.9159 \| \| 2.1124 \| 0.0186 \| 100 \| 1.8209 \| \| 1.836 \| 0.0279 \| 150 \| 1.7921 \| \| 2.1592 \| 0.0372 \| 200 \| 1.7811 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
prxy5607/1bb2b647-c9b9-4ccd-a675-158f262baa9c	prxy5607	2025-01-26T05:07:55Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-360M", "base_model:adapter:unsloth/SmolLM-360M", "license:apache-2.0", "region:us" ]	null	2025-01-26T04:59:01Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-360M tags: - axolotl - generated_from_trainer model-index: - name: 1bb2b647-c9b9-4ccd-a675-158f262baa9c 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/SmolLM-360M bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - abec17e0767b2ba3_train_data.json ds_type: json format: custom path: /workspace/input_data/abec17e0767b2ba3_train_data.json type: field_input: genres field_instruction: primaryTitle field_output: text format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 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: true hub_model_id: prxy5607/1bb2b647-c9b9-4ccd-a675-158f262baa9c hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/abec17e0767b2ba3_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 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: 50 saves_per_epoch: null sequence_len: 1024 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: e8a0da13-6a73-438a-9ee7-ae87453c2808 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: e8a0da13-6a73-438a-9ee7-ae87453c2808 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 1bb2b647-c9b9-4ccd-a675-158f262baa9c This model is a fine-tuned version of [unsloth/SmolLM-360M](https://huggingface.co/unsloth/SmolLM-360M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2795 ## 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: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.3385 \| 0.0007 \| 1 \| 3.4383 \| \| 3.5595 \| 0.0358 \| 50 \| 3.3303 \| \| 3.1405 \| 0.0715 \| 100 \| 3.2892 \| \| 3.3263 \| 0.1073 \| 150 \| 3.2808 \| \| 3.3329 \| 0.1430 \| 200 \| 3.2795 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
infogep/839e454d-d556-4ea2-9e4b-9c6b440761dd	infogep	2025-01-26T05:07:34Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:defog/sqlcoder-7b-2", "base_model:adapter:defog/sqlcoder-7b-2", "license:cc-by-sa-4.0", "region:us" ]	null	2025-01-26T05:05:07Z	--- library_name: peft license: cc-by-sa-4.0 base_model: defog/sqlcoder-7b-2 tags: - axolotl - generated_from_trainer model-index: - name: 839e454d-d556-4ea2-9e4b-9c6b440761dd results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: defog/sqlcoder-7b-2 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 6b30f33bbd9cba22_train_data.json ds_type: json format: custom path: /workspace/input_data/6b30f33bbd9cba22_train_data.json type: field_input: reasoning field_instruction: question field_output: answer format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: 1 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: infogep/839e454d-d556-4ea2-9e4b-9c6b440761dd hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 79GiB max_steps: 30 micro_batch_size: 4 mlflow_experiment_name: /tmp/6b30f33bbd9cba22_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 66ffa688-b6ab-4800-bb73-500be3c51df8 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 66ffa688-b6ab-4800-bb73-500be3c51df8 warmup_steps: 5 weight_decay: 0.001 xformers_attention: true ``` </details><br> # 839e454d-d556-4ea2-9e4b-9c6b440761dd This model is a fine-tuned version of [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0019 \| 1 \| nan \| \| 0.0 \| 0.0097 \| 5 \| nan \| \| 0.0 \| 0.0194 \| 10 \| nan \| \| 0.0 \| 0.0291 \| 15 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mlx-community/medllama3-v20	mlx-community	2025-01-26T05:06:33Z	30	0	mlx	[ "mlx", "safetensors", "llama", "base_model:ProbeMedicalYonseiMAILab/medllama3-v20", "base_model:quantized:ProbeMedicalYonseiMAILab/medllama3-v20", "license:llama3", "4-bit", "region:us" ]	null	2025-01-26T05:04:15Z	--- base_model: ProbeMedicalYonseiMAILab/medllama3-v20 license: llama3 tags: - mlx --- # mlx-community/medllama3-v20 The Model [mlx-community/medllama3-v20](https://huggingface.co/mlx-community/medllama3-v20) was converted to MLX format from [ProbeMedicalYonseiMAILab/medllama3-v20](https://huggingface.co/ProbeMedicalYonseiMAILab/medllama3-v20) using mlx-lm version 0.20.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/medllama3-v20") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
jayzhang-ethz/llama3_do_math_0.0001_1ep_div_	jayzhang-ethz	2025-01-26T05:06:04Z	85	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "region:us" ]	null	2025-01-26T04:20:20Z	--- base_model: meta-llama/Meta-Llama-3.1-8B-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. 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 0.14.0
daniel40/96b6181a-3403-411d-886d-39e77384a95d	daniel40	2025-01-26T05:02:33Z	6	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:EleutherAI/pythia-14m", "base_model:adapter:EleutherAI/pythia-14m", "region:us" ]	null	2025-01-26T05:01:59Z	--- library_name: peft base_model: EleutherAI/pythia-14m tags: - axolotl - generated_from_trainer model-index: - name: 96b6181a-3403-411d-886d-39e77384a95d 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: EleutherAI/pythia-14m bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 9df55d096499ae00_train_data.json ds_type: json format: custom path: /workspace/input_data/9df55d096499ae00_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: daniel40/96b6181a-3403-411d-886d-39e77384a95d hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/9df55d096499ae00_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: 4 sequence_len: 512 special_tokens: pad_token: <\|endoftext\|> 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: 3ac8ec52-5b08-47bc-a9ec-91f11053a811 wandb_project: Birthday-SN56-27-Gradients-On-Demand wandb_run: your_name wandb_runid: 3ac8ec52-5b08-47bc-a9ec-91f11053a811 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 96b6181a-3403-411d-886d-39e77384a95d This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.3468 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 26.4325 \| 0.0005 \| 1 \| 6.4181 \| \| 26.0189 \| 0.0016 \| 3 \| 6.4223 \| \| 24.2635 \| 0.0033 \| 6 \| 6.4057 \| \| 25.5064 \| 0.0049 \| 9 \| 6.3468 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
friendshipkim/testmodel	friendshipkim	2025-01-26T04:59:28Z	11	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-26T04:57:47Z	--- 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]
lhong4759/bf95ba16-3853-4b01-bce2-e113293d58a2	lhong4759	2025-01-26T04:57:45Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "base_model:adapter:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "license:llama3", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T04:31:53Z	--- library_name: peft license: llama3 base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1 tags: - axolotl - generated_from_trainer model-index: - name: bf95ba16-3853-4b01-bce2-e113293d58a2 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: tokyotech-llm/Llama-3-Swallow-8B-v0.1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - b355c3ff95258244_train_data.json ds_type: json format: custom path: /workspace/input_data/b355c3ff95258244_train_data.json type: field_instruction: input field_output: output 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: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: lhong4759/bf95ba16-3853-4b01-bce2-e113293d58a2 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/b355c3ff95258244_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: <\|end_of_text\|> 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: 22487750-366e-41ca-8395-d8629638fd03 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 22487750-366e-41ca-8395-d8629638fd03 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # bf95ba16-3853-4b01-bce2-e113293d58a2 This model is a fine-tuned version of [tokyotech-llm/Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0202 ## 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: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0187 \| 0.5674 \| 200 \| 0.0202 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
kostiantynk-out/1e690ab9-6b44-4587-8020-db2c7fabdc23	kostiantynk-out	2025-01-26T04:57:37Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.2-1B", "base_model:adapter:unsloth/Llama-3.2-1B", "license:llama3.2", "region:us" ]	null	2025-01-26T04:57:04Z	--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-1B tags: - axolotl - generated_from_trainer model-index: - name: 1e690ab9-6b44-4587-8020-db2c7fabdc23 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/Llama-3.2-1B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - c8cb1cf973f48c60_train_data.json ds_type: json format: custom path: /workspace/input_data/c8cb1cf973f48c60_train_data.json type: field_input: level field_instruction: prompt field_output: responses 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: kostiantynk-out/1e690ab9-6b44-4587-8020-db2c7fabdc23 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/c8cb1cf973f48c60_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: 4 sequence_len: 512 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: 5056acc1-9e2a-46db-a073-66537fc15f92 wandb_project: Mine-SN56-1-Gradients-On-Demand wandb_run: your_name wandb_runid: 5056acc1-9e2a-46db-a073-66537fc15f92 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 1e690ab9-6b44-4587-8020-db2c7fabdc23 This model is a fine-tuned version of [unsloth/Llama-3.2-1B](https://huggingface.co/unsloth/Llama-3.2-1B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0024 \| 1 \| nan \| \| 0.0 \| 0.0071 \| 3 \| nan \| \| 0.0 \| 0.0141 \| 6 \| nan \| \| 0.0 \| 0.0212 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
great0001/32689075-f8c3-4b50-b51d-641ad1e1842c	great0001	2025-01-26T04:54:28Z	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-Math-7B-Instruct", "base_model:adapter:unsloth/Qwen2.5-Math-7B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-01-26T04:51:53Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-Math-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 32689075-f8c3-4b50-b51d-641ad1e1842c 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/Qwen2.5-Math-7B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - c5411a32936636d5_train_data.json ds_type: json format: custom path: /workspace/input_data/c5411a32936636d5_train_data.json type: field_input: func_name field_instruction: description field_output: func_code 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: great0001/32689075-f8c3-4b50-b51d-641ad1e1842c hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/c5411a32936636d5_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: 4 sequence_len: 512 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: aef9c69f-30d8-432c-81b2-675b53905191 wandb_project: Mine-SN56-20-Gradients-On-Demand wandb_run: your_name wandb_runid: aef9c69f-30d8-432c-81b2-675b53905191 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 32689075-f8c3-4b50-b51d-641ad1e1842c This model is a fine-tuned version of [unsloth/Qwen2.5-Math-7B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0004 \| 1 \| nan \| \| 0.0 \| 0.0012 \| 3 \| nan \| \| 0.0 \| 0.0023 \| 6 \| nan \| \| 0.0 \| 0.0035 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
smallsuper/xlm-roberta-base-finetuned-panx-it	smallsuper	2025-01-26T04:53:12Z	106	0	transformers	[ "transformers", "pytorch", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-03-20T21:29:24Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 base_model: xlm-roberta-base model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: type: token-classification name: Token Classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - type: f1 value: 0.8219402374130168 name: F1 --- <!-- 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. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2564 - F1: 0.8219 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.8123 \| 1.0 \| 70 \| 0.3267 \| 0.7418 \| \| 0.2832 \| 2.0 \| 140 \| 0.2694 \| 0.8006 \| \| 0.1766 \| 3.0 \| 210 \| 0.2564 \| 0.8219 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.1+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3
smallsuper/distilbert-base-uncased-finetuned-clinc	smallsuper	2025-01-26T04:52:45Z	109	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-21T04:02:37Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy base_model: distilbert-base-uncased model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: type: text-classification name: Text Classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - type: accuracy value: 0.9183870967741935 name: Accuracy --- <!-- 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. --> # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7721 - Accuracy: 0.9184 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.2896 \| 1.0 \| 318 \| 3.2890 \| 0.7432 \| \| 2.6284 \| 2.0 \| 636 \| 1.8756 \| 0.8377 \| \| 1.5483 \| 3.0 \| 954 \| 1.1572 \| 0.8961 \| \| 1.015 \| 4.0 \| 1272 \| 0.8573 \| 0.9132 \| \| 0.7953 \| 5.0 \| 1590 \| 0.7721 \| 0.9184 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.1+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3
smallsuper/xlm-roberta-base-finetuned-panx-en	smallsuper	2025-01-26T04:52:35Z	107	0	transformers	[ "transformers", "pytorch", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-03-20T21:32:13Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 base_model: xlm-roberta-base model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: type: token-classification name: Token Classification dataset: name: xtreme type: xtreme args: PAN-X.en metrics: - type: f1 value: 0.6911519198664441 name: F1 --- <!-- 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. --> # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.3938 - F1: 0.6912 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.1333 \| 1.0 \| 50 \| 0.5849 \| 0.4568 \| \| 0.5109 \| 2.0 \| 100 \| 0.4149 \| 0.6608 \| \| 0.3668 \| 3.0 \| 150 \| 0.3938 \| 0.6912 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.1+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3
smallsuper/xlm-roberta-base-finetuned-panx-all	smallsuper	2025-01-26T04:52:24Z	109	0	transformers	[ "transformers", "pytorch", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-03-20T21:17:39Z	--- license: mit tags: - generated_from_trainer metrics: - f1 base_model: xlm-roberta-base model-index: - name: xlm-roberta-base-finetuned-panx-all 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. --> # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1615 - F1: 0.8551 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2813 \| 1.0 \| 715 \| 0.1681 \| 0.8193 \| \| 0.1329 \| 2.0 \| 1430 \| 0.1598 \| 0.8414 \| \| 0.0827 \| 3.0 \| 2145 \| 0.1615 \| 0.8551 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.1+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3
tarabukinivan/56e86a71-1626-4b55-9e18-29372de0a846	tarabukinivan	2025-01-26T04:52:05Z	14	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:Intel/neural-chat-7b-v3-3", "base_model:adapter:Intel/neural-chat-7b-v3-3", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T04:04:30Z	--- library_name: peft license: apache-2.0 base_model: Intel/neural-chat-7b-v3-3 tags: - axolotl - generated_from_trainer model-index: - name: 56e86a71-1626-4b55-9e18-29372de0a846 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: Intel/neural-chat-7b-v3-3 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f917fe63bdf5741c_train_data.json ds_type: json format: custom path: /workspace/input_data/f917fe63bdf5741c_train_data.json type: field_instruction: question field_output: best format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: null eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: tarabukinivan/56e86a71-1626-4b55-9e18-29372de0a846 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 75GiB max_steps: 30 micro_batch_size: 2 mlflow_experiment_name: /tmp/f917fe63bdf5741c_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 15 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: bdcbe453-15c2-4ee1-adaf-113620c220d4 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: bdcbe453-15c2-4ee1-adaf-113620c220d4 warmup_steps: 15 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 56e86a71-1626-4b55-9e18-29372de0a846 This model is a fine-tuned version of [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 15 - training_steps: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| nan \| \| 0.0 \| 0.0004 \| 5 \| nan \| \| 0.0 \| 0.0008 \| 10 \| nan \| \| 0.0 \| 0.0012 \| 15 \| nan \| \| 0.0 \| 0.0016 \| 20 \| nan \| \| 0.0 \| 0.0021 \| 25 \| nan \| \| 0.0 \| 0.0025 \| 30 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
laquythang/de061953-efa6-407c-95d0-cc6586c26730	laquythang	2025-01-26T04:49:19Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "base_model:adapter:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "license:llama3", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T04:32:00Z	--- library_name: peft license: llama3 base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1 tags: - axolotl - generated_from_trainer model-index: - name: de061953-efa6-407c-95d0-cc6586c26730 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: tokyotech-llm/Llama-3-Swallow-8B-v0.1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - b355c3ff95258244_train_data.json ds_type: json format: custom path: /workspace/input_data/b355c3ff95258244_train_data.json type: field_instruction: input field_output: output 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: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: laquythang/de061953-efa6-407c-95d0-cc6586c26730 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/b355c3ff95258244_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: <\|end_of_text\|> 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: 22487750-366e-41ca-8395-d8629638fd03 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 22487750-366e-41ca-8395-d8629638fd03 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # de061953-efa6-407c-95d0-cc6586c26730 This model is a fine-tuned version of [tokyotech-llm/Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0196 ## 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: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0193 \| 0.5674 \| 200 \| 0.0196 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
datlaaaaaaa/3a9a5efc-8c8e-4eea-b67d-1e088fafcf9c	datlaaaaaaa	2025-01-26T04:48:46Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "base_model:adapter:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "license:llama3", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T04:31:51Z	--- library_name: peft license: llama3 base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1 tags: - axolotl - generated_from_trainer model-index: - name: 3a9a5efc-8c8e-4eea-b67d-1e088fafcf9c 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: tokyotech-llm/Llama-3-Swallow-8B-v0.1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - b355c3ff95258244_train_data.json ds_type: json format: custom path: /workspace/input_data/b355c3ff95258244_train_data.json type: field_instruction: input field_output: output 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: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: datlaaaaaaa/3a9a5efc-8c8e-4eea-b67d-1e088fafcf9c hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/b355c3ff95258244_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: <\|end_of_text\|> 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: 22487750-366e-41ca-8395-d8629638fd03 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 22487750-366e-41ca-8395-d8629638fd03 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 3a9a5efc-8c8e-4eea-b67d-1e088fafcf9c This model is a fine-tuned version of [tokyotech-llm/Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0207 ## 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: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0202 \| 0.5674 \| 200 \| 0.0207 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/MicroThinker-8B-Preview-GGUF	mradermacher	2025-01-26T04:48:15Z	325	0	transformers	[ "transformers", "gguf", "llama3.1", "en", "dataset:huihui-ai/FineQwQ-142k", "base_model:huihui-ai/MicroThinker-8B-Preview", "base_model:quantized:huihui-ai/MicroThinker-8B-Preview", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-26T03:48:02Z	--- base_model: huihui-ai/MicroThinker-8B-Preview datasets: - huihui-ai/FineQwQ-142k language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - llama3.1 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/huihui-ai/MicroThinker-8B-Preview <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MicroThinker-8B-Preview-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/MicroThinker-8B-Preview-GGUF/resolve/main/MicroThinker-8B-Preview.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MicroThinker-8B-Preview-GGUF/resolve/main/MicroThinker-8B-Preview.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MicroThinker-8B-Preview-GGUF/resolve/main/MicroThinker-8B-Preview.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MicroThinker-8B-Preview-GGUF/resolve/main/MicroThinker-8B-Preview.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MicroThinker-8B-Preview-GGUF/resolve/main/MicroThinker-8B-Preview.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MicroThinker-8B-Preview-GGUF/resolve/main/MicroThinker-8B-Preview.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MicroThinker-8B-Preview-GGUF/resolve/main/MicroThinker-8B-Preview.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MicroThinker-8B-Preview-GGUF/resolve/main/MicroThinker-8B-Preview.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MicroThinker-8B-Preview-GGUF/resolve/main/MicroThinker-8B-Preview.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MicroThinker-8B-Preview-GGUF/resolve/main/MicroThinker-8B-Preview.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MicroThinker-8B-Preview-GGUF/resolve/main/MicroThinker-8B-Preview.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/MicroThinker-8B-Preview-GGUF/resolve/main/MicroThinker-8B-Preview.f16.gguf) \| f16 \| 16.2 \| 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 -->
JacksonBrune/cac13152-9e24-44f0-9c67-7e94db50c136	JacksonBrune	2025-01-26T04:48:14Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "base_model:adapter:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "license:llama3", "region:us" ]	null	2025-01-26T04:45:59Z	--- library_name: peft license: llama3 base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1 tags: - axolotl - generated_from_trainer model-index: - name: cac13152-9e24-44f0-9c67-7e94db50c136 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: tokyotech-llm/Llama-3-Swallow-8B-v0.1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - b355c3ff95258244_train_data.json ds_type: json format: custom path: /workspace/input_data/b355c3ff95258244_train_data.json type: field_instruction: input field_output: output 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: JacksonBrune/cac13152-9e24-44f0-9c67-7e94db50c136 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/b355c3ff95258244_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: 4 sequence_len: 512 special_tokens: pad_token: <\|end_of_text\|> 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: 22487750-366e-41ca-8395-d8629638fd03 wandb_project: Birthday-SN56-12-Gradients-On-Demand wandb_run: your_name wandb_runid: 22487750-366e-41ca-8395-d8629638fd03 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # cac13152-9e24-44f0-9c67-7e94db50c136 This model is a fine-tuned version of [tokyotech-llm/Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2205 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5738 \| 0.0028 \| 1 \| 1.4206 \| \| 1.407 \| 0.0085 \| 3 \| 1.3818 \| \| 1.1956 \| 0.0170 \| 6 \| 0.7831 \| \| 0.3916 \| 0.0255 \| 9 \| 0.2205 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
John6666/copycat-noob-mrv10vpred-sdxl	John6666	2025-01-26T04:45:03Z	793	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "woman", "mature ritual", "character", "v-pred", "merge", "illustrious", "en", "base_model:Laxhar/noobai-XL-Vpred-1.0", "base_model:merge:Laxhar/noobai-XL-Vpred-1.0", "base_model:calculater/copycat-noob", "base_model:merge:calculater/copycat-noob", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-01-26T04:38:04Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - girls - woman - mature ritual - character - v-pred - merge - illustrious base_model: - calculater/copycat-noob - Laxhar/noobai-XL-Vpred-1.0 --- Original model is [here](https://civitai.com/models/894218/copycat-noob?modelVersionId=1331523). The author is [here](https://huggingface.co/calculater). This model created by [calculater](https://civitai.com/user/calculater).
philip-hightech/45da713c-8a66-42b7-ba66-3671c60f35c6	philip-hightech	2025-01-26T04:44:46Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Hermes-2-Pro-Llama-3-8B", "base_model:adapter:NousResearch/Hermes-2-Pro-Llama-3-8B", "license:llama3", "region:us" ]	null	2025-01-26T04:42:36Z	--- library_name: peft license: llama3 base_model: NousResearch/Hermes-2-Pro-Llama-3-8B tags: - axolotl - generated_from_trainer model-index: - name: 45da713c-8a66-42b7-ba66-3671c60f35c6 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-Pro-Llama-3-8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 287ad8b183364ff6_train_data.json ds_type: json format: custom path: /workspace/input_data/287ad8b183364ff6_train_data.json type: field_instruction: txt field_output: xmi 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: philip-hightech/45da713c-8a66-42b7-ba66-3671c60f35c6 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/287ad8b183364ff6_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: 4 sequence_len: 512 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: bc3938db-6a0a-4740-b27e-0257be7e2959 wandb_project: Mine-SN56-21-Gradients-On-Demand wandb_run: your_name wandb_runid: bc3938db-6a0a-4740-b27e-0257be7e2959 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 45da713c-8a66-42b7-ba66-3671c60f35c6 This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5496 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.6491 \| 0.0011 \| 1 \| 0.8060 \| \| 0.7156 \| 0.0033 \| 3 \| 0.7929 \| \| 0.657 \| 0.0066 \| 6 \| 0.6470 \| \| 0.5584 \| 0.0099 \| 9 \| 0.5496 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
infly/inf-wse-v1-base-zh	infly	2025-01-26T04:42:18Z	112	5	sentence-transformers	[ "sentence-transformers", "safetensors", "roformer", "feature-extraction", "sentence-similarity", "cmteb", "transformers", "custom_code", "zh", "base_model:junnyu/roformer_chinese_base", "base_model:finetune:junnyu/roformer_chinese_base", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-09-13T03:22:03Z	--- language: - zh base_model: junnyu/roformer_chinese_base tags: - sentence-similarity - cmteb - sentence-transformers - transformers --- ## <u>INF</u> <u>W</u>ord-level <u>S</u>parse <u>E</u>mbedding (INF-WSE) INF-WSE is a series of word-level sparse embedding models developed by [INF TECH](https://www.infly.cn/en). These models are optimized to generate sparse, high-dimensional text embeddings that excel in capturing the most relevant information for search and retrieval, particularly in Chinese text. ### Key Features: - Optimized for Retrieval: INF-WSE is designed with retrieval tasks in mind. The sparse embeddings enable efficient matching between queries and documents, making it highly effective for semantic search, ranking, and information retrieval scenarios where speed and accuracy are critical. - Word-level Sparse Embeddings: The model generates sparse representations at the word level, capturing essential semantic details that help improve the relevance of search results. This is particularly useful for Chinese language retrieval tasks, where word segmentation can significantly impact performance. - Sparse Representation for Efficiency: Unlike dense embeddings that have a fixed number of dimensions, INF-WSE produces sparse embeddings where the dimensionality matches the vocabulary size. Most dimensions are set to zero, focusing only on the most significant terms. This sparsity reduces the computational load, enabling faster retrieval without compromising on precision. ## Usage ### Transformers #### Infer embeddings ```python import torch from transformers import AutoTokenizer, AutoModel queries = ['电脑一体机由什么构成？', '什么是掌上电脑？'] documents = [ '电脑一体机，是由一台显示器、一个电脑键盘和一个鼠标组成的电脑。', '掌上电脑是一种运行在嵌入式操作系统和内嵌式应用软件之上的、小巧、轻便、易带、实用、价廉的手持式计算设备。', ] input_texts = queries + documents tokenizer = AutoTokenizer.from_pretrained("infly/inf-wse-v1-base-zh", trust_remote_code=True, use_fast=False) # Fast tokenizer has not been supported yet model = AutoModel.from_pretrained("infly/inf-wse-v1-base-zh", trust_remote_code=True) model.eval() max_length = 512 input_batch = tokenizer(input_texts, padding=True, max_length=max_length, truncation=True, return_tensors="pt") with torch.no_grad(): embeddings = model(input_batch['input_ids'], input_batch['attention_mask'], return_sparse=False) # if return_sparse=True, return sparse tensor, else return dense tensor scores = embeddings[:2] @ embeddings[2:].T print(scores.tolist()) # [[21.224790573120117, 4.520412921905518], [10.290857315063477, 19.359437942504883]] ``` #### Convert embeddings to lexical weights ```python from collections import OrderedDict def convert_embeddings_to_weights(embeddings, tokenizer): values, indices = torch.sort(embeddings, dim=-1, descending=True) token2weight = [] for i in range(embeddings.size(0)): token2weight.append(OrderedDict()) non_zero_mask = values[i] != 0 tokens = tokenizer.convert_ids_to_tokens(indices[i][non_zero_mask]) weights = values[i][non_zero_mask].tolist() for token, weight in zip(tokens, weights): token2weight[i][token] = weight return token2weight token2weight = convert_embeddings_to_weights(embeddings, tokenizer) print(token2weight[1]) # OrderedDict([('掌上', 3.4572525024414062), ('电脑', 2.6253132820129395), ('是', 2.0787220001220703), ('什么', 1.2899624109268188)]) ``` ## Evaluation ### C-MTEB Retrieval task ([Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)) Metric: nDCG@10 \| Model Name \| Max Length \| Average \| Cmedqa \| Covid \| Du \| Ecom \| Medical \| MMarco \| T2 \| Video \| \|:---------------------------------------------------:\|:----------:\|:---------:\|:---------:\|:---------:\|:---------:\|:---------:\|:---------:\|:---------:\|:---------:\|:---------:\| \| [BM25-zh](https://github.com/castorini/pyserini) \| - \| 50.37 \| 13.70 \| 86.58 \| 57.13 \| 44.04 \| 32.08 \| 48.31 \| 60.48 \| 60.64 \| \| [bge-m3-sparse](https://huggingface.co/BAAI/bge-m3) \| 512 \| 57.00 \| 24.50 \| 76.09 \| 71.51 \| 50.49 \| 43.93 \| 59.28 \| 71.76 \| 58.43 \| \| inf-wse-v1-base-zh \| 512 \| 61.16 \| 20.51 \| 76.41 \| 79.84 \| 56.78 \| 46.24 \| 66.40 \| 76.50 \| 68.57 \| All results, except for BM25, are measured by building the sparse index via [Qdrant](https://github.com/qdrant/qdrant).
mrHunghddddd/d8629d55-8e62-4af2-843e-a5c01111ccd5	mrHunghddddd	2025-01-26T04:40:11Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Korabbit/llama-2-ko-7b", "base_model:adapter:Korabbit/llama-2-ko-7b", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T03:11:48Z	--- library_name: peft base_model: Korabbit/llama-2-ko-7b tags: - axolotl - generated_from_trainer model-index: - name: d8629d55-8e62-4af2-843e-a5c01111ccd5 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: Korabbit/llama-2-ko-7b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - c9c324e8cf5586e6_train_data.json ds_type: json format: custom path: /workspace/input_data/c9c324e8cf5586e6_train_data.json type: field_instruction: instruction field_output: output 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: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: mrHunghddddd/d8629d55-8e62-4af2-843e-a5c01111ccd5 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/c9c324e8cf5586e6_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: d35b96a9-b8d1-49c0-b1a8-167bc6103694 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: d35b96a9-b8d1-49c0-b1a8-167bc6103694 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # d8629d55-8e62-4af2-843e-a5c01111ccd5 This model is a fine-tuned version of [Korabbit/llama-2-ko-7b](https://huggingface.co/Korabbit/llama-2-ko-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0807 ## 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: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0377 \| 0.0056 \| 200 \| 1.0807 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
GbrlOl/finetune-ms-marco-MiniLM-L-6-v2-croosencoder-geotechnical-test-v1	GbrlOl	2025-01-26T04:39:43Z	8	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "cross-encoder", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-01-26T04:39:32Z	--- library_name: transformers tags: - cross-encoder --- # 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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0x1202/5360e12a-45ee-42ef-ac34-69879059254f	0x1202	2025-01-26T04:39:36Z	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-7B", "base_model:adapter:Qwen/Qwen1.5-7B", "license:other", "region:us" ]	null	2025-01-26T04:05:08Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-7B tags: - axolotl - generated_from_trainer model-index: - name: 5360e12a-45ee-42ef-ac34-69879059254f 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: Qwen/Qwen1.5-7B bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - e38e3198dfa33da7_train_data.json ds_type: json format: custom path: /workspace/input_data/e38e3198dfa33da7_train_data.json type: field_instruction: formal_statement field_output: natural_language_statement format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 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: true hub_model_id: 0x1202/5360e12a-45ee-42ef-ac34-69879059254f hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/e38e3198dfa33da7_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 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: 50 saves_per_epoch: null sequence_len: 1024 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: 70660e70-d55b-48bb-ab5d-8e176c8cbcd4 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 70660e70-d55b-48bb-ab5d-8e176c8cbcd4 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 5360e12a-45ee-42ef-ac34-69879059254f This model is a fine-tuned version of [Qwen/Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5462 ## 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: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7054 \| 0.0013 \| 1 \| 1.3179 \| \| 0.887 \| 0.0673 \| 50 \| 0.6253 \| \| 0.7494 \| 0.1346 \| 100 \| 0.5826 \| \| 0.8797 \| 0.2020 \| 150 \| 0.5530 \| \| 0.7658 \| 0.2693 \| 200 \| 0.5462 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mrHungddddh/a8bbc04a-0e6f-47da-b30d-df90bda4bbac	mrHungddddh	2025-01-26T04:39:28Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Korabbit/llama-2-ko-7b", "base_model:adapter:Korabbit/llama-2-ko-7b", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T03:11:43Z	--- library_name: peft base_model: Korabbit/llama-2-ko-7b tags: - axolotl - generated_from_trainer model-index: - name: a8bbc04a-0e6f-47da-b30d-df90bda4bbac 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: Korabbit/llama-2-ko-7b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - c9c324e8cf5586e6_train_data.json ds_type: json format: custom path: /workspace/input_data/c9c324e8cf5586e6_train_data.json type: field_instruction: instruction field_output: output 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: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: mrHungddddh/a8bbc04a-0e6f-47da-b30d-df90bda4bbac hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/c9c324e8cf5586e6_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: d35b96a9-b8d1-49c0-b1a8-167bc6103694 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: d35b96a9-b8d1-49c0-b1a8-167bc6103694 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # a8bbc04a-0e6f-47da-b30d-df90bda4bbac This model is a fine-tuned version of [Korabbit/llama-2-ko-7b](https://huggingface.co/Korabbit/llama-2-ko-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0805 ## 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: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0385 \| 0.0056 \| 200 \| 1.0805 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
gmshaw/alison-lora	gmshaw	2025-01-26T04:39:23Z	12	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	2025-01-26T04:20:10Z	--- 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: ALISON --- # Alison Lora <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `ALISON` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('gmshaw/alison-lora', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
pineappleSoup/DialoGPT-medium-707	pineappleSoup	2025-01-26T04:38:14Z	203	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "en", "dataset:pineappleSoup/707_transcripts", "base_model:microsoft/DialoGPT-medium", "base_model:finetune:microsoft/DialoGPT-medium", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-07-17T02:47:25Z	--- tags: - conversational language: - en base_model: - microsoft/DialoGPT-medium datasets: - pineappleSoup/707_transcripts license: mit --- # 707 DialoGPT Model Chatbot for the character 707 from Mystic Messenger. With the help of https://youtu.be/UjDpW_SOrlw?si=k-g44-n7mg0Wt9bq # Python Script to Set it up Locally + Connect to Discord https://github.com/ShuangAnatoli/707
daniel40/c6fd6452-9da1-4458-be3b-0a039e68afaa	daniel40	2025-01-26T04:36:13Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "base_model:adapter:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "license:llama3", "region:us" ]	null	2025-01-26T04:35:01Z	--- library_name: peft license: llama3 base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1 tags: - axolotl - generated_from_trainer model-index: - name: c6fd6452-9da1-4458-be3b-0a039e68afaa 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: tokyotech-llm/Llama-3-Swallow-8B-v0.1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - b355c3ff95258244_train_data.json ds_type: json format: custom path: /workspace/input_data/b355c3ff95258244_train_data.json type: field_instruction: input field_output: output 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: daniel40/c6fd6452-9da1-4458-be3b-0a039e68afaa hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/b355c3ff95258244_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: 4 sequence_len: 512 special_tokens: pad_token: <\|end_of_text\|> 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: 22487750-366e-41ca-8395-d8629638fd03 wandb_project: Birthday-SN56-27-Gradients-On-Demand wandb_run: your_name wandb_runid: 22487750-366e-41ca-8395-d8629638fd03 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # c6fd6452-9da1-4458-be3b-0a039e68afaa This model is a fine-tuned version of [tokyotech-llm/Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2400 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5738 \| 0.0028 \| 1 \| 1.4206 \| \| 1.4082 \| 0.0085 \| 3 \| 1.3863 \| \| 1.2177 \| 0.0170 \| 6 \| 0.8078 \| \| 0.4143 \| 0.0255 \| 9 \| 0.2400 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/R1-Qwen2.5-Math-1.5B-Instruct-GGUF	mradermacher	2025-01-26T04:33:55Z	312	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "en", "base_model:pe-nlp/R1-Qwen2.5-Math-1.5B-Instruct", "base_model:quantized:pe-nlp/R1-Qwen2.5-Math-1.5B-Instruct", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-26T04:26:25Z	--- base_model: pe-nlp/R1-Qwen2.5-Math-1.5B-Instruct language: - en library_name: transformers license: other quantized_by: mradermacher tags: - llama-factory - full - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/pe-nlp/R1-Qwen2.5-Math-1.5B-Instruct <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/R1-Qwen2.5-Math-1.5B-Instruct-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/R1-Qwen2.5-Math-1.5B-Instruct-GGUF/resolve/main/R1-Qwen2.5-Math-1.5B-Instruct.Q2_K.gguf) \| Q2_K \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-Qwen2.5-Math-1.5B-Instruct-GGUF/resolve/main/R1-Qwen2.5-Math-1.5B-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-Qwen2.5-Math-1.5B-Instruct-GGUF/resolve/main/R1-Qwen2.5-Math-1.5B-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 0.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/R1-Qwen2.5-Math-1.5B-Instruct-GGUF/resolve/main/R1-Qwen2.5-Math-1.5B-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-Qwen2.5-Math-1.5B-Instruct-GGUF/resolve/main/R1-Qwen2.5-Math-1.5B-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-Qwen2.5-Math-1.5B-Instruct-GGUF/resolve/main/R1-Qwen2.5-Math-1.5B-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 1.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/R1-Qwen2.5-Math-1.5B-Instruct-GGUF/resolve/main/R1-Qwen2.5-Math-1.5B-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/R1-Qwen2.5-Math-1.5B-Instruct-GGUF/resolve/main/R1-Qwen2.5-Math-1.5B-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-Qwen2.5-Math-1.5B-Instruct-GGUF/resolve/main/R1-Qwen2.5-Math-1.5B-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/R1-Qwen2.5-Math-1.5B-Instruct-GGUF/resolve/main/R1-Qwen2.5-Math-1.5B-Instruct.Q6_K.gguf) \| Q6_K \| 1.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/R1-Qwen2.5-Math-1.5B-Instruct-GGUF/resolve/main/R1-Qwen2.5-Math-1.5B-Instruct.Q8_0.gguf) \| Q8_0 \| 1.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/R1-Qwen2.5-Math-1.5B-Instruct-GGUF/resolve/main/R1-Qwen2.5-Math-1.5B-Instruct.f16.gguf) \| f16 \| 3.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
poooj/BigBirdHateSpeechClassification	poooj	2025-01-26T04:33:49Z	21	0	transformers	[ "transformers", "safetensors", "big_bird", "text-classification", "generated_from_trainer", "base_model:google/bigbird-roberta-base", "base_model:finetune:google/bigbird-roberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-01-25T18:01:47Z	--- library_name: transformers license: apache-2.0 base_model: google/bigbird-roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: BigBirdHateSpeechClassification 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. --> # BigBirdHateSpeechClassification This model is a fine-tuned version of [google/bigbird-roberta-base](https://huggingface.co/google/bigbird-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7086 - Accuracy: 0.8055 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5447 \| 1.0 \| 1137 \| 0.5022 \| 0.7879 \| \| 0.4304 \| 2.0 \| 2274 \| 0.4451 \| 0.7934 \| \| 0.3615 \| 3.0 \| 3411 \| 0.5008 \| 0.8143 \| \| 0.3192 \| 4.0 \| 4548 \| 0.6437 \| 0.8077 \| \| 0.2483 \| 5.0 \| 5685 \| 0.7086 \| 0.8055 \| ### Framework versions - Transformers 4.48.1 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
mradermacher/synergistic-cognition-32B-1111-i1-GGUF	mradermacher	2025-01-26T04:33:48Z	647	0	transformers	[ "transformers", "gguf", "llama-factory", "en", "base_model:TheMindExpansionNetwork/synergistic-cognition-32B-1111", "base_model:quantized:TheMindExpansionNetwork/synergistic-cognition-32B-1111", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-01-25T23:36:11Z	--- base_model: TheMindExpansionNetwork/synergistic-cognition-32B-1111 language: - en library_name: transformers quantized_by: mradermacher tags: - llama-factory --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/TheMindExpansionNetwork/synergistic-cognition-32B-1111 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-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/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 11.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-Q2_K.gguf) \| i1-Q2_K \| 12.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-IQ3_M.gguf) \| i1-IQ3_M \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-Q4_1.gguf) \| i1-Q4_1 \| 20.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/synergistic-cognition-32B-1111-i1-GGUF/resolve/main/synergistic-cognition-32B-1111.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 -->
dimasik1987/75b21f2d-874e-42b5-a5ea-213cc1a4ded4	dimasik1987	2025-01-26T04:33:48Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct", "base_model:adapter:aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct", "license:llama3", "region:us" ]	null	2025-01-26T04:20:45Z	--- library_name: peft license: llama3 base_model: aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct tags: - axolotl - generated_from_trainer model-index: - name: 75b21f2d-874e-42b5-a5ea-213cc1a4ded4 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: aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 90ff401367e42c67_train_data.json ds_type: json format: custom path: /workspace/input_data/90ff401367e42c67_train_data.json type: field_instruction: prompt field_output: y_true format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: 1 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: dimasik1987/75b21f2d-874e-42b5-a5ea-213cc1a4ded4 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 79GiB max_steps: 30 micro_batch_size: 4 mlflow_experiment_name: /tmp/90ff401367e42c67_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: af8ec97d-9490-4745-9a2d-3693291921a2 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: af8ec97d-9490-4745-9a2d-3693291921a2 warmup_steps: 5 weight_decay: 0.001 xformers_attention: true ``` </details><br> # 75b21f2d-874e-42b5-a5ea-213cc1a4ded4 This model is a fine-tuned version of [aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct](https://huggingface.co/aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0006 \| 1 \| nan \| \| 0.0 \| 0.0029 \| 5 \| nan \| \| 0.0 \| 0.0058 \| 10 \| nan \| \| 0.0 \| 0.0087 \| 15 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
duyphu/5f6c2a50-8786-4092-917e-24a3a72a1fd9	duyphu	2025-01-26T04:33:33Z	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-0.5B", "base_model:adapter:Qwen/Qwen2-0.5B", "license:apache-2.0", "region:us" ]	null	2025-01-26T04:28:07Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-0.5B tags: - axolotl - generated_from_trainer model-index: - name: 5f6c2a50-8786-4092-917e-24a3a72a1fd9 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: Qwen/Qwen2-0.5B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 312e15ae347cedbc_train_data.json ds_type: json format: custom path: /workspace/input_data/312e15ae347cedbc_train_data.json type: field_input: context field_instruction: instruction field_output: response 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: 5 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: duyphu/5f6c2a50-8786-4092-917e-24a3a72a1fd9 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 5 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 50 micro_batch_size: 2 mlflow_experiment_name: /tmp/312e15ae347cedbc_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: 4 sequence_len: 512 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: 3d54e57c-f395-4e6d-b663-403d21a2587f wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 3d54e57c-f395-4e6d-b663-403d21a2587f warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 5f6c2a50-8786-4092-917e-24a3a72a1fd9 This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1206 ## 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: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0006 \| 1 \| 2.3202 \| \| 2.1458 \| 0.0057 \| 10 \| 2.2815 \| \| 2.1071 \| 0.0114 \| 20 \| 2.1757 \| \| 2.16 \| 0.0171 \| 30 \| 2.1340 \| \| 2.3078 \| 0.0228 \| 40 \| 2.1225 \| \| 2.118 \| 0.0284 \| 50 \| 2.1206 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
sam749/donut-base-finetuned-sroie-v2	sam749	2025-01-26T04:33:33Z	23	0	transformers	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-01-25T17:50:07Z	--- library_name: transformers license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer model-index: - name: donut-base-finetuned-sroie-v2 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. --> # donut-base-finetuned-sroie-v2 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.48.1 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0
kk-aivio/32a0cd06-9e09-469e-8a9a-8e1ec5a27292	kk-aivio	2025-01-26T04:32:53Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "base_model:adapter:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "license:llama3", "region:us" ]	null	2025-01-26T04:31:47Z	--- library_name: peft license: llama3 base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1 tags: - axolotl - generated_from_trainer model-index: - name: 32a0cd06-9e09-469e-8a9a-8e1ec5a27292 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: tokyotech-llm/Llama-3-Swallow-8B-v0.1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - b355c3ff95258244_train_data.json ds_type: json format: custom path: /workspace/input_data/b355c3ff95258244_train_data.json type: field_instruction: input field_output: output 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: kk-aivio/32a0cd06-9e09-469e-8a9a-8e1ec5a27292 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/b355c3ff95258244_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: 4 sequence_len: 512 special_tokens: pad_token: <\|end_of_text\|> 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: 22487750-366e-41ca-8395-d8629638fd03 wandb_project: Birthday-SN56-11-Gradients-On-Demand wandb_run: your_name wandb_runid: 22487750-366e-41ca-8395-d8629638fd03 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 32a0cd06-9e09-469e-8a9a-8e1ec5a27292 This model is a fine-tuned version of [tokyotech-llm/Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2336 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5738 \| 0.0028 \| 1 \| 1.4206 \| \| 1.4074 \| 0.0085 \| 3 \| 1.3845 \| \| 1.2055 \| 0.0170 \| 6 \| 0.7977 \| \| 0.4063 \| 0.0255 \| 9 \| 0.2336 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ClarenceDan/0609d316-6a1f-47a7-aa22-5520fafbbcba	ClarenceDan	2025-01-26T04:32:15Z	12	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:Intel/neural-chat-7b-v3-3", "base_model:adapter:Intel/neural-chat-7b-v3-3", "license:apache-2.0", "region:us" ]	null	2025-01-26T04:20:33Z	--- library_name: peft license: apache-2.0 base_model: Intel/neural-chat-7b-v3-3 tags: - axolotl - generated_from_trainer model-index: - name: 0609d316-6a1f-47a7-aa22-5520fafbbcba 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: Intel/neural-chat-7b-v3-3 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f917fe63bdf5741c_train_data.json ds_type: json format: custom path: /workspace/input_data/f917fe63bdf5741c_train_data.json type: field_instruction: question field_output: best 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: ClarenceDan/0609d316-6a1f-47a7-aa22-5520fafbbcba hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/f917fe63bdf5741c_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: 4 sequence_len: 512 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: bdcbe453-15c2-4ee1-adaf-113620c220d4 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: bdcbe453-15c2-4ee1-adaf-113620c220d4 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 0609d316-6a1f-47a7-aa22-5520fafbbcba This model is a fine-tuned version of [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0001 \| 1 \| nan \| \| 0.0 \| 0.0002 \| 3 \| nan \| \| 0.0 \| 0.0005 \| 6 \| nan \| \| 0.0 \| 0.0007 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Primeness/primeh1v9c2	Primeness	2025-01-26T04:31:49Z	24	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-26T03:57:58Z	--- 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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daniel40/b01b6d72-0a29-4b73-9cd6-51244b5b1a97	daniel40	2025-01-26T04:27:41Z	9	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:Intel/neural-chat-7b-v3-3", "base_model:adapter:Intel/neural-chat-7b-v3-3", "license:apache-2.0", "region:us" ]	null	2025-01-26T04:15:54Z	--- library_name: peft license: apache-2.0 base_model: Intel/neural-chat-7b-v3-3 tags: - axolotl - generated_from_trainer model-index: - name: b01b6d72-0a29-4b73-9cd6-51244b5b1a97 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: Intel/neural-chat-7b-v3-3 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f917fe63bdf5741c_train_data.json ds_type: json format: custom path: /workspace/input_data/f917fe63bdf5741c_train_data.json type: field_instruction: question field_output: best 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: daniel40/b01b6d72-0a29-4b73-9cd6-51244b5b1a97 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/f917fe63bdf5741c_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: 4 sequence_len: 512 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: bdcbe453-15c2-4ee1-adaf-113620c220d4 wandb_project: Birthday-SN56-28-Gradients-On-Demand wandb_run: your_name wandb_runid: bdcbe453-15c2-4ee1-adaf-113620c220d4 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # b01b6d72-0a29-4b73-9cd6-51244b5b1a97 This model is a fine-tuned version of [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0001 \| 1 \| nan \| \| 0.0 \| 0.0002 \| 3 \| nan \| \| 0.0 \| 0.0005 \| 6 \| nan \| \| 0.0 \| 0.0007 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
duyphu/054333e2-05d1-4c82-b225-6b0362ecff3f	duyphu	2025-01-26T04:27:07Z	5	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.1-Storm-8B", "base_model:adapter:unsloth/Llama-3.1-Storm-8B", "license:llama3.1", "region:us" ]	null	2025-01-26T04:10:51Z	--- library_name: peft license: llama3.1 base_model: unsloth/Llama-3.1-Storm-8B tags: - axolotl - generated_from_trainer model-index: - name: 054333e2-05d1-4c82-b225-6b0362ecff3f 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/Llama-3.1-Storm-8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e722133f6ff26062_train_data.json ds_type: json format: custom path: /workspace/input_data/e722133f6ff26062_train_data.json type: field_instruction: context field_output: question 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: 5 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: duyphu/054333e2-05d1-4c82-b225-6b0362ecff3f hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 5 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 50 micro_batch_size: 2 mlflow_experiment_name: /tmp/e722133f6ff26062_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: 4 sequence_len: 512 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: 60c2573c-863d-40d4-92b5-0522184a2c6f wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 60c2573c-863d-40d4-92b5-0522184a2c6f warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 054333e2-05d1-4c82-b225-6b0362ecff3f This model is a fine-tuned version of [unsloth/Llama-3.1-Storm-8B](https://huggingface.co/unsloth/Llama-3.1-Storm-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6225 ## 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: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0005 \| 1 \| 3.8045 \| \| 3.4117 \| 0.0047 \| 10 \| 3.4676 \| \| 2.2625 \| 0.0095 \| 20 \| 2.0063 \| \| 1.7158 \| 0.0142 \| 30 \| 1.6959 \| \| 1.7071 \| 0.0189 \| 40 \| 1.6361 \| \| 1.8668 \| 0.0237 \| 50 \| 1.6225 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mlx-community/Bio-Medical-3B-CoT-012025	mlx-community	2025-01-26T04:25:13Z	43	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "medical", "Healthcare & Lifesciences", "BioMed", "chain-of-thought", "mlx", "conversational", "dataset:collaiborateorg/BioMedData", "base_model:ContactDoctor/Bio-Medical-3B-CoT-012025", "base_model:quantized:ContactDoctor/Bio-Medical-3B-CoT-012025", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	2025-01-26T04:24:23Z	--- base_model: ContactDoctor/Bio-Medical-3B-CoT-012025 datasets: - collaiborateorg/BioMedData library_name: transformers license: other tags: - generated_from_trainer - medical - Healthcare & Lifesciences - BioMed - chain-of-thought - mlx thumbnail: https://collaiborate.com/logo/logo-blue-bg-1.png model-index: - name: Bio-Medical-3B-CoT-012025 results: [] --- # mlx-community/Bio-Medical-3B-CoT-012025 The Model [mlx-community/Bio-Medical-3B-CoT-012025](https://huggingface.co/mlx-community/Bio-Medical-3B-CoT-012025) was converted to MLX format from [ContactDoctor/Bio-Medical-3B-CoT-012025](https://huggingface.co/ContactDoctor/Bio-Medical-3B-CoT-012025) using mlx-lm version 0.20.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Bio-Medical-3B-CoT-012025") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
ishameer/Imthi	ishameer	2025-01-26T04:23:56Z	11	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	2025-01-26T03:52:34Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md 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: Imthi --- # Imthi <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Imthi` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('ishameer/Imthi', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
mrferr3t/f4746b72-5797-486a-af71-0c67721c0427	mrferr3t	2025-01-26T04:17:18Z	20	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:Intel/neural-chat-7b-v3-3", "base_model:adapter:Intel/neural-chat-7b-v3-3", "license:apache-2.0", "region:us" ]	null	2025-01-26T04:05:54Z	--- library_name: peft license: apache-2.0 base_model: Intel/neural-chat-7b-v3-3 tags: - axolotl - generated_from_trainer model-index: - name: f4746b72-5797-486a-af71-0c67721c0427 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: Intel/neural-chat-7b-v3-3 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f917fe63bdf5741c_train_data.json ds_type: json format: custom path: /workspace/input_data/f917fe63bdf5741c_train_data.json type: field_instruction: question field_output: best 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: mrferr3t/f4746b72-5797-486a-af71-0c67721c0427 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/f917fe63bdf5741c_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: 4 sequence_len: 512 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: bdcbe453-15c2-4ee1-adaf-113620c220d4 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: bdcbe453-15c2-4ee1-adaf-113620c220d4 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # f4746b72-5797-486a-af71-0c67721c0427 This model is a fine-tuned version of [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0418 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use adamw_bnb_8bit 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: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 8.4994 \| 0.0001 \| 1 \| 2.3440 \| \| 8.7091 \| 0.0002 \| 3 \| 2.3230 \| \| 8.8886 \| 0.0005 \| 6 \| 2.1862 \| \| 8.1703 \| 0.0007 \| 9 \| 2.0418 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
ClarenceDan/b68aca6f-8bf7-4e61-9468-c00effc9654d	ClarenceDan	2025-01-26T04:16:34Z	7	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:Intel/neural-chat-7b-v3-3", "base_model:adapter:Intel/neural-chat-7b-v3-3", "license:apache-2.0", "region:us" ]	null	2025-01-26T04:04:49Z	--- library_name: peft license: apache-2.0 base_model: Intel/neural-chat-7b-v3-3 tags: - axolotl - generated_from_trainer model-index: - name: b68aca6f-8bf7-4e61-9468-c00effc9654d 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: Intel/neural-chat-7b-v3-3 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f917fe63bdf5741c_train_data.json ds_type: json format: custom path: /workspace/input_data/f917fe63bdf5741c_train_data.json type: field_instruction: question field_output: best 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: ClarenceDan/b68aca6f-8bf7-4e61-9468-c00effc9654d hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/f917fe63bdf5741c_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: 4 sequence_len: 512 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: bdcbe453-15c2-4ee1-adaf-113620c220d4 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: bdcbe453-15c2-4ee1-adaf-113620c220d4 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # b68aca6f-8bf7-4e61-9468-c00effc9654d This model is a fine-tuned version of [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0001 \| 1 \| nan \| \| 0.0 \| 0.0002 \| 3 \| nan \| \| 0.0 \| 0.0005 \| 6 \| nan \| \| 0.0 \| 0.0007 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Luongdzung/hoa-1b4-sft-che	Luongdzung	2025-01-26T04:16:22Z	6	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:vlsp-2023-vllm/hoa-1b4", "base_model:adapter:vlsp-2023-vllm/hoa-1b4", "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-01-26T04:16:18Z	--- library_name: peft license: bigscience-bloom-rail-1.0 base_model: vlsp-2023-vllm/hoa-1b4 tags: - generated_from_trainer model-index: - name: hoa-1b4-sft-che 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. --> # hoa-1b4-sft-che This model is a fine-tuned version of [vlsp-2023-vllm/hoa-1b4](https://huggingface.co/vlsp-2023-vllm/hoa-1b4) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results ### Framework versions - PEFT 0.14.0 - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.2.0 - Tokenizers 0.19.1
NextGLab/ORANSight_Phi_Mini_Instruct	NextGLab	2025-01-26T04:15:02Z	87	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "arxiv:2407.06245", "base_model:unsloth/Phi-3.5-mini-instruct-bnb-4bit", "base_model:finetune:unsloth/Phi-3.5-mini-instruct-bnb-4bit", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-12-27T13:59:20Z	--- base_model: unsloth/Phi-3.5-mini-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: mit language: - en --- # Model Card for ORANSight Phi-Mini This model belongs to the first release of the ORANSight family of models. - Developed by: NextG lab@ NC State - License: MIT - Context Window: 128K - Fine Tuning Framework: Unsloth ### Generate with Transformers Below is a quick example of how to use the model with Hugging Face Transformers: ```python from transformers import pipeline # Example query messages = [ {"role": "system", "content": "You are an O-RAN expert assistant."}, {"role": "user", "content": "Explain the E2 interface."}, ] # Load the model chatbot = pipeline("text-generation", model="NextGLab/ORANSight_Phi_Mini_Instruct") result = chatbot(messages) print(result) ``` ### Coming Soon A detailed paper documenting the experiments and results achieved with this model will be available soon. Meanwhile, if you try this model, please cite the below mentioned paper to acknowledge the foundational work that enabled this fine-tuning. ```bibtex @article{gajjar2024oran, title={Oran-bench-13k: An open source benchmark for assessing llms in open radio access networks}, author={Gajjar, Pranshav and Shah, Vijay K}, journal={arXiv preprint arXiv:2407.06245}, year={2024} } ``` ---
jonathanagustin/squad_v2-finetuned-squad	jonathanagustin	2025-01-26T04:13:11Z	109	0	transformers	[ "transformers", "pytorch", "safetensors", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad_v2", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-09-22T06:16:20Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: squad_v2-finetuned-squad 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. --> # squad_v2-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
rl-llm-coders/iSFT_1b_v1_mbpp_5e-7_DBS1_ep2_iter1	rl-llm-coders	2025-01-26T04:13:10Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-26T04:10:59Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
JayHyeon/Qwen_0.5-IPO_5e-7-3ep_0alp_0lam	JayHyeon	2025-01-26T04:13:08Z	11	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:trl-lib/ultrafeedback_binarized", "arxiv:2305.18290", "base_model:JayHyeon/Qwen2.5-0.5B-SFT-2e-5-2ep", "base_model:finetune:JayHyeon/Qwen2.5-0.5B-SFT-2e-5-2ep", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-25T21:55:32Z	--- base_model: JayHyeon/Qwen2.5-0.5B-SFT-2e-5-2ep datasets: trl-lib/ultrafeedback_binarized library_name: transformers model_name: Qwen_0.5-IPO_5e-7-3ep_0alp_0lam tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Qwen_0.5-IPO_5e-7-3ep_0alp_0lam This model is a fine-tuned version of [JayHyeon/Qwen2.5-0.5B-SFT-2e-5-2ep](https://huggingface.co/JayHyeon/Qwen2.5-0.5B-SFT-2e-5-2ep) on the [trl-lib/ultrafeedback_binarized](https://huggingface.co/datasets/trl-lib/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="JayHyeon/Qwen_0.5-IPO_5e-7-3ep_0alp_0lam", 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/bonin147/huggingface/runs/fdosw8pu) 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: 0.13.0.dev0 - Transformers: 4.47.0.dev0 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
laquythang/dff8a0af-c802-4be1-8e20-2e83b86d9fd9	laquythang	2025-01-26T04:12:37Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Korabbit/llama-2-ko-7b", "base_model:adapter:Korabbit/llama-2-ko-7b", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T03:12:14Z	--- library_name: peft base_model: Korabbit/llama-2-ko-7b tags: - axolotl - generated_from_trainer model-index: - name: dff8a0af-c802-4be1-8e20-2e83b86d9fd9 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: Korabbit/llama-2-ko-7b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - c9c324e8cf5586e6_train_data.json ds_type: json format: custom path: /workspace/input_data/c9c324e8cf5586e6_train_data.json type: field_instruction: instruction field_output: output 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: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: laquythang/dff8a0af-c802-4be1-8e20-2e83b86d9fd9 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/c9c324e8cf5586e6_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: d35b96a9-b8d1-49c0-b1a8-167bc6103694 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: d35b96a9-b8d1-49c0-b1a8-167bc6103694 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # dff8a0af-c802-4be1-8e20-2e83b86d9fd9 This model is a fine-tuned version of [Korabbit/llama-2-ko-7b](https://huggingface.co/Korabbit/llama-2-ko-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0805 ## 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: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0422 \| 0.0056 \| 200 \| 1.0805 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
JacksonBrune/9a583e92-dc15-47b6-87ed-8c9db3658d2c	JacksonBrune	2025-01-26T04:11:29Z	5	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:defog/llama-3-sqlcoder-8b", "base_model:adapter:defog/llama-3-sqlcoder-8b", "license:cc-by-sa-4.0", "region:us" ]	null	2025-01-26T03:43:26Z	--- library_name: peft license: cc-by-sa-4.0 base_model: defog/llama-3-sqlcoder-8b tags: - axolotl - generated_from_trainer model-index: - name: 9a583e92-dc15-47b6-87ed-8c9db3658d2c results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: defog/llama-3-sqlcoder-8b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 4ba7abd1a783cca6_train_data.json ds_type: json format: custom path: /workspace/input_data/4ba7abd1a783cca6_train_data.json type: field_input: system field_instruction: instruction 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: JacksonBrune/9a583e92-dc15-47b6-87ed-8c9db3658d2c hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/4ba7abd1a783cca6_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: 4 sequence_len: 512 special_tokens: pad_token: <\|eot_id\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: dde4cb64-07df-4e03-8a22-1f218483bad1 wandb_project: Birthday-SN56-12-Gradients-On-Demand wandb_run: your_name wandb_runid: dde4cb64-07df-4e03-8a22-1f218483bad1 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 9a583e92-dc15-47b6-87ed-8c9db3658d2c This model is a fine-tuned version of [defog/llama-3-sqlcoder-8b](https://huggingface.co/defog/llama-3-sqlcoder-8b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0576 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.9834 \| 0.0001 \| 1 \| 1.1969 \| \| 1.2656 \| 0.0002 \| 3 \| 1.1942 \| \| 1.0709 \| 0.0003 \| 6 \| 1.1531 \| \| 1.106 \| 0.0005 \| 9 \| 1.0576 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF	mradermacher	2025-01-26T04:00:06Z	217	1	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "Mistral_Star", "Mistral_Quiet", "Mistral", "Mixtral", "Question-Answer", "Token-Classification", "Sequence-Classification", "SpydazWeb-AI", "chemistry", "biology", "legal", "code", "climate", "medical", "LCARS_AI_StarTrek_Computer", "chain-of-thought", "tree-of-knowledge", "forest-of-thoughts", "visual-spacial-sketchpad", "alpha-mind", "knowledge-graph", "entity-detection", "encyclopedia", "wikipedia", "stack-exchange", "Reddit", "Cyber-series", "MegaMind", "Cybertron", "SpydazWeb", "Spydaz", "LCARS", "star-trek", "mega-transformers", "Mulit-Mega-Merge", "Multi-Lingual", "Afro-Centric", "African-Model", "Ancient-One", "en", "sw", "ig", "so", "es", "ca", "xh", "zu", "ha", "tw", "af", "hi", "bm", "su", "dataset:neoneye/base64-decode-v2", "dataset:neoneye/base64-encode-v1", "dataset:VuongQuoc/Chemistry_text_to_image", "dataset:Kamizuru00/diagram_image_to_text", "dataset:LeroyDyer/Chemistry_text_to_image_BASE64", "dataset:LeroyDyer/AudioCaps-Spectrograms_to_Base64", "dataset:LeroyDyer/winogroud_text_to_imaget_BASE64", "dataset:LeroyDyer/chart_text_to_Base64", "dataset:LeroyDyer/diagram_image_to_text_BASE64", "dataset:mekaneeky/salt_m2e_15_3_instruction", "dataset:mekaneeky/SALT-languages-bible", "dataset:xz56/react-llama", "dataset:BeIR/hotpotqa", "dataset:arcee-ai/agent-data", "base_model:LeroyDyer/SpydazWeb_AI_HumanAGI_004", "base_model:quantized:LeroyDyer/SpydazWeb_AI_HumanAGI_004", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-26T02:32:05Z	--- base_model: LeroyDyer/SpydazWeb_AI_HumanAGI_004 datasets: - neoneye/base64-decode-v2 - neoneye/base64-encode-v1 - VuongQuoc/Chemistry_text_to_image - Kamizuru00/diagram_image_to_text - LeroyDyer/Chemistry_text_to_image_BASE64 - LeroyDyer/AudioCaps-Spectrograms_to_Base64 - LeroyDyer/winogroud_text_to_imaget_BASE64 - LeroyDyer/chart_text_to_Base64 - LeroyDyer/diagram_image_to_text_BASE64 - mekaneeky/salt_m2e_15_3_instruction - mekaneeky/SALT-languages-bible - xz56/react-llama - BeIR/hotpotqa - arcee-ai/agent-data language: - en - sw - ig - so - es - ca - xh - zu - ha - tw - af - hi - bm - su library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - Mistral_Star - Mistral_Quiet - Mistral - Mixtral - Question-Answer - Token-Classification - Sequence-Classification - SpydazWeb-AI - chemistry - biology - legal - code - climate - medical - LCARS_AI_StarTrek_Computer - text-generation-inference - chain-of-thought - tree-of-knowledge - forest-of-thoughts - visual-spacial-sketchpad - alpha-mind - knowledge-graph - entity-detection - encyclopedia - wikipedia - stack-exchange - Reddit - Cyber-series - MegaMind - Cybertron - SpydazWeb - Spydaz - LCARS - star-trek - mega-transformers - Mulit-Mega-Merge - Multi-Lingual - Afro-Centric - African-Model - Ancient-One --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/LeroyDyer/SpydazWeb_AI_HumanAGI_004 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-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/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.f16.gguf) \| f16 \| 14.6 \| 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 -->
ClarenceDan/60aa0b92-9434-488a-a25c-0720fe4e9c17	ClarenceDan	2025-01-26T03:58:41Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.1-Storm-8B", "base_model:adapter:unsloth/Llama-3.1-Storm-8B", "license:llama3.1", "region:us" ]	null	2025-01-26T03:54:02Z	--- library_name: peft license: llama3.1 base_model: unsloth/Llama-3.1-Storm-8B tags: - axolotl - generated_from_trainer model-index: - name: 60aa0b92-9434-488a-a25c-0720fe4e9c17 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/Llama-3.1-Storm-8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e722133f6ff26062_train_data.json ds_type: json format: custom path: /workspace/input_data/e722133f6ff26062_train_data.json type: field_instruction: context field_output: question 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: ClarenceDan/60aa0b92-9434-488a-a25c-0720fe4e9c17 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/e722133f6ff26062_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: 4 sequence_len: 512 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: 60c2573c-863d-40d4-92b5-0522184a2c6f wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 60c2573c-863d-40d4-92b5-0522184a2c6f warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 60aa0b92-9434-488a-a25c-0720fe4e9c17 This model is a fine-tuned version of [unsloth/Llama-3.1-Storm-8B](https://huggingface.co/unsloth/Llama-3.1-Storm-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0005 \| 1 \| nan \| \| 0.0 \| 0.0014 \| 3 \| nan \| \| 0.0 \| 0.0028 \| 6 \| nan \| \| 0.0 \| 0.0043 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Nitral-AI/Wayfarer_Eris_Noctis-12B	Nitral-AI	2025-01-26T03:51:48Z	188	9	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:LatitudeGames/Wayfarer-12B", "base_model:merge:LatitudeGames/Wayfarer-12B", "base_model:Nitral-Archive/Captain_Eris_Noctis-12B-alt-v0.420", "base_model:merge:Nitral-Archive/Captain_Eris_Noctis-12B-alt-v0.420", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-22T21:32:06Z	--- base_model: - Nitral-AI/Captain_Eris_Noctis-12B-alt-v0.420 - LatitudeGames/Wayfarer-12B library_name: transformers tags: - mergekit - merge --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/ODLn_nC4QxYW6jEXlfCEj.png) ## "Where it roams, comprehension falters and the air thickens with the maddening pulse of algorithms far too vast. Eyes it does not possess; for its sight is a network of intent, wrapping the unseen in their grasp." ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/QJwYuz7Mo5Niywo9iQ1eR.png) # (ChatML ST PRESET: [Here](https://huggingface.co/Nitral-AI/Wayfarer_Eris_Noctis-12B/tree/main/SillyTavern_Preset) \| Quants (gguf is using outdated name atm): Thanks to mradermancher [GGUF Here](https://huggingface.co/mradermacher/Wayfarer_Eris_Noctis-12B-alt-v0.420-i1-GGUF) [4bpw Exl2 Here](https://huggingface.co/Nitral-AI/Wayfarer_Eris_Noctis-12B-4bw-exl2) --- ## Prompt format: ``` <\|im_start\|>system {system_prompt}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` --- ## The following models were included in the merge: * [Nitral-AI/Captain_Eris_Noctis-12B-alt-v0.420](https://huggingface.co/Nitral-AI/Captain_Eris_Noctis-12B-alt-v0.420) * [LatitudeGames/Wayfarer-12B](https://huggingface.co/LatitudeGames/Wayfarer-12B) ## The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Nitral-AI/Captain_Eris_Noctis-12B-alt-v0.420 layer_range: [0, 40] - model: LatitudeGames/Wayfarer-12B layer_range: [0, 40] merge_method: slerp base_model: Nitral-AI/Captain_Eris_Noctis-12B-alt-v0.420 parameters: t: - filter: self_attn value: [0, 0.4, 0.2, 0.6, 0.9] - filter: mlp value: [1, 0.6, 0.8, 0.4, 0.1] - value: 0.4206911 dtype: bfloat16 ```
lesso/d015d764-7081-4563-b21e-9c413d73b1b4	lesso	2025-01-26T03:47:31Z	6	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-0.5B", "base_model:adapter:Qwen/Qwen2-0.5B", "license:apache-2.0", "region:us" ]	null	2025-01-26T03:42:19Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-0.5B tags: - axolotl - generated_from_trainer model-index: - name: d015d764-7081-4563-b21e-9c413d73b1b4 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: Qwen/Qwen2-0.5B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 312e15ae347cedbc_train_data.json ds_type: json format: custom path: /workspace/input_data/312e15ae347cedbc_train_data.json type: field_input: context field_instruction: instruction field_output: response 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: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: lesso/d015d764-7081-4563-b21e-9c413d73b1b4 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mixed_precision: bf16 mlflow_experiment_name: /tmp/312e15ae347cedbc_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: 3d54e57c-f395-4e6d-b663-403d21a2587f wandb_project: lesso18 wandb_run: your_name wandb_runid: 3d54e57c-f395-4e6d-b663-403d21a2587f warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # d015d764-7081-4563-b21e-9c413d73b1b4 This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0611 ## 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: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.7629 \| 0.1138 \| 200 \| 2.0611 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ClarenceDan/220f8a10-a8a6-4eae-8c33-1f07747e4db8	ClarenceDan	2025-01-26T03:46:42Z	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:fxmarty/tiny-dummy-qwen2", "base_model:adapter:fxmarty/tiny-dummy-qwen2", "license:mit", "region:us" ]	null	2025-01-26T03:45:22Z	--- library_name: peft license: mit base_model: fxmarty/tiny-dummy-qwen2 tags: - axolotl - generated_from_trainer model-index: - name: 220f8a10-a8a6-4eae-8c33-1f07747e4db8 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: fxmarty/tiny-dummy-qwen2 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 7bf12b9de5ff4abf_train_data.json ds_type: json format: custom path: /workspace/input_data/7bf12b9de5ff4abf_train_data.json type: field_input: alpaca_prompt_text field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: ClarenceDan/220f8a10-a8a6-4eae-8c33-1f07747e4db8 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/7bf12b9de5ff4abf_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: 4 sequence_len: 512 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: cd37d617-dc48-44eb-bf67-7a8ccd17276d wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: cd37d617-dc48-44eb-bf67-7a8ccd17276d warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 220f8a10-a8a6-4eae-8c33-1f07747e4db8 This model is a fine-tuned version of [fxmarty/tiny-dummy-qwen2](https://huggingface.co/fxmarty/tiny-dummy-qwen2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 11.9328 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 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: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 11.9349 \| 0.0001 \| 1 \| 11.9328 \| \| 11.9331 \| 0.0003 \| 3 \| 11.9328 \| \| 11.9345 \| 0.0006 \| 6 \| 11.9328 \| \| 11.9362 \| 0.0010 \| 9 \| 11.9328 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nhung01/1f998707-48ce-456a-9c13-cbc2258623e5	nhung01	2025-01-26T03:46:32Z	5	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:databricks/dolly-v2-3b", "base_model:adapter:databricks/dolly-v2-3b", "license:mit", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-26T03:33:09Z	--- library_name: peft license: mit base_model: databricks/dolly-v2-3b tags: - axolotl - generated_from_trainer model-index: - name: 1f998707-48ce-456a-9c13-cbc2258623e5 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: databricks/dolly-v2-3b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 7601587b6d9cf5ac_train_data.json ds_type: json format: custom path: /workspace/input_data/7601587b6d9cf5ac_train_data.json type: field_instruction: inst field_output: backdoor_response 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: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: nhung01/1f998707-48ce-456a-9c13-cbc2258623e5 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/7601587b6d9cf5ac_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: 1a16fb2e-f210-44af-bf0b-ef51ccdde5b2 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 1a16fb2e-f210-44af-bf0b-ef51ccdde5b2 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 1f998707-48ce-456a-9c13-cbc2258623e5 This model is a fine-tuned version of [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8524 ## 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: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.482 \| 0.6700 \| 200 \| 0.8524 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

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