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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-08-03 00:49:08	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 549 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-03 00:44:12	card stringlengths 11 1.01M
lesso04/de71102e-2e4e-44ca-93c5-21443af184fd	lesso04	2025-03-06T04:02:06Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-7B-Instruct", "base_model:adapter:unsloth/Qwen2-7B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-03-06T01:39:56Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: de71102e-2e4e-44ca-93c5-21443af184fd 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) <br> # de71102e-2e4e-44ca-93c5-21443af184fd This model is a fine-tuned version of [unsloth/Qwen2-7B-Instruct](https://huggingface.co/unsloth/Qwen2-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0386 ## 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: 0.000204 - train_batch_size: 4 - eval_batch_size: 4 - seed: 40 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0009 \| 1 \| 0.7943 \| \| 0.0389 \| 0.4663 \| 500 \| 0.0386 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso04/760397d1-b3aa-48ba-92c9-8cf78908233c	lesso04	2025-03-06T04:02:04Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "custom_code", "base_model:NousResearch/Yarn-Llama-2-7b-128k", "base_model:adapter:NousResearch/Yarn-Llama-2-7b-128k", "region:us" ]	null	2025-03-05T23:19:35Z	--- library_name: peft base_model: NousResearch/Yarn-Llama-2-7b-128k tags: - axolotl - generated_from_trainer model-index: - name: 760397d1-b3aa-48ba-92c9-8cf78908233c 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) <br> # 760397d1-b3aa-48ba-92c9-8cf78908233c This model is a fine-tuned version of [NousResearch/Yarn-Llama-2-7b-128k](https://huggingface.co/NousResearch/Yarn-Llama-2-7b-128k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0002 ## 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: 0.000204 - train_batch_size: 4 - eval_batch_size: 4 - seed: 40 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0008 \| 1 \| 0.4513 \| \| 0.0041 \| 0.3885 \| 500 \| 0.0002 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso04/a3193b94-0f53-471b-89ac-2a2fb8f539ce	lesso04	2025-03-06T04:02:02Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:01-ai/Yi-1.5-9B-Chat-16K", "base_model:adapter:01-ai/Yi-1.5-9B-Chat-16K", "license:apache-2.0", "region:us" ]	null	2025-03-05T20:14:54Z	--- library_name: peft license: apache-2.0 base_model: 01-ai/Yi-1.5-9B-Chat-16K tags: - axolotl - generated_from_trainer model-index: - name: a3193b94-0f53-471b-89ac-2a2fb8f539ce 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) <br> # a3193b94-0f53-471b-89ac-2a2fb8f539ce This model is a fine-tuned version of [01-ai/Yi-1.5-9B-Chat-16K](https://huggingface.co/01-ai/Yi-1.5-9B-Chat-16K) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2566 ## 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: 0.000204 - train_batch_size: 4 - eval_batch_size: 4 - seed: 40 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| 0.5238 \| \| 0.2874 \| 0.0729 \| 500 \| 0.2566 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso04/0322f0be-3616-4a07-8cc3-156ffe562b53	lesso04	2025-03-06T04:02:00Z	0	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	2025-03-05T18:44:50Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 0322f0be-3616-4a07-8cc3-156ffe562b53 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) <br> # 0322f0be-3616-4a07-8cc3-156ffe562b53 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: 0.1947 ## 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: 0.000204 - train_batch_size: 4 - eval_batch_size: 4 - seed: 40 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0009 \| 1 \| 1.3569 \| \| 0.2098 \| 0.4460 \| 500 \| 0.1947 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso04/6fcc5ad2-4434-419b-859a-ad0d23cf6349	lesso04	2025-03-06T04:01:58Z	0	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	2025-03-05T13:35:30Z	--- library_name: peft license: llama3 base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1 tags: - axolotl - generated_from_trainer model-index: - name: 6fcc5ad2-4434-419b-859a-ad0d23cf6349 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) <br> # 6fcc5ad2-4434-419b-859a-ad0d23cf6349 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: 0.4223 ## 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: 0.000204 - train_batch_size: 4 - eval_batch_size: 4 - seed: 40 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 1500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0009 \| 1 \| 1.7147 \| \| 0.6137 \| 0.4655 \| 500 \| 0.6154 \| \| 0.4502 \| 0.9311 \| 1000 \| 0.4428 \| \| 0.1818 \| 1.3966 \| 1500 \| 0.4223 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
takedakoji00/Llama-3.1-8B-Instruct-custom-qg-7th_random_300_val_val_edit_distance	takedakoji00	2025-03-06T04:01:58Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-05T18:27:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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lesso04/aa3b906f-f484-4edd-8009-7c09a73ff3ce	lesso04	2025-03-06T04:01:56Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-14B-Instruct", "base_model:adapter:unsloth/Qwen2.5-14B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-03-05T09:55:38Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-14B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: aa3b906f-f484-4edd-8009-7c09a73ff3ce 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) <br> # aa3b906f-f484-4edd-8009-7c09a73ff3ce This model is a fine-tuned version of [unsloth/Qwen2.5-14B-Instruct](https://huggingface.co/unsloth/Qwen2.5-14B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3578 ## 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: 0.000204 - train_batch_size: 4 - eval_batch_size: 4 - seed: 40 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0008 \| 1 \| 1.5336 \| \| 0.3665 \| 0.3806 \| 500 \| 0.3578 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso03/8682f5c9-d5b4-4781-9e47-8685b4ce87b8	lesso03	2025-03-06T04:01:40Z	0	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:unsloth/gemma-1.1-2b-it", "base_model:adapter:unsloth/gemma-1.1-2b-it", "license:apache-2.0", "region:us" ]	null	2025-03-05T20:38:22Z	--- library_name: peft license: apache-2.0 base_model: unsloth/gemma-1.1-2b-it tags: - axolotl - generated_from_trainer model-index: - name: 8682f5c9-d5b4-4781-9e47-8685b4ce87b8 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) <br> # 8682f5c9-d5b4-4781-9e47-8685b4ce87b8 This model is a fine-tuned version of [unsloth/gemma-1.1-2b-it](https://huggingface.co/unsloth/gemma-1.1-2b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9060 ## 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: 0.000203 - train_batch_size: 4 - eval_batch_size: 4 - seed: 30 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0002 \| 1 \| 5.4800 \| \| 1.9148 \| 0.0987 \| 500 \| 1.9060 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso03/03d2a8ae-8158-4634-b4f7-03e596bf377a	lesso03	2025-03-06T04:01:38Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Capybara-7B-V1", "base_model:adapter:NousResearch/Nous-Capybara-7B-V1", "license:mit", "region:us" ]	null	2025-03-05T18:27:14Z	--- library_name: peft license: mit base_model: NousResearch/Nous-Capybara-7B-V1 tags: - axolotl - generated_from_trainer model-index: - name: 03d2a8ae-8158-4634-b4f7-03e596bf377a 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) <br> # 03d2a8ae-8158-4634-b4f7-03e596bf377a This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7553 ## 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: 0.000203 - train_batch_size: 4 - eval_batch_size: 4 - seed: 30 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0006 \| 1 \| 1.2831 \| \| 0.7544 \| 0.3043 \| 500 \| 0.7553 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso03/fddad2f5-50b1-423c-8d59-e61f14b08db8	lesso03	2025-03-06T04:01:35Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B", "base_model:adapter:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B", "region:us" ]	null	2025-03-05T15:15:07Z	--- library_name: peft base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B tags: - axolotl - generated_from_trainer model-index: - name: fddad2f5-50b1-423c-8d59-e61f14b08db8 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) <br> # fddad2f5-50b1-423c-8d59-e61f14b08db8 This model is a fine-tuned version of [Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5154 ## 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: 0.000203 - train_batch_size: 4 - eval_batch_size: 4 - seed: 30 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| 0.8766 \| \| 0.5187 \| 0.0585 \| 500 \| 0.5154 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso02/b6da6e58-400d-4d05-82fd-99df31685aa6	lesso02	2025-03-06T04:01:19Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-7B-Instruct", "base_model:adapter:unsloth/Qwen2-7B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-03-06T01:40:01Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: b6da6e58-400d-4d05-82fd-99df31685aa6 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) <br> # b6da6e58-400d-4d05-82fd-99df31685aa6 This model is a fine-tuned version of [unsloth/Qwen2-7B-Instruct](https://huggingface.co/unsloth/Qwen2-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0401 ## 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: 0.000202 - train_batch_size: 4 - eval_batch_size: 4 - seed: 20 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0009 \| 1 \| 0.7945 \| \| 0.0419 \| 0.4663 \| 500 \| 0.0401 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso02/af1244bf-9e22-4425-83a4-9ff753c4b5a7	lesso02	2025-03-06T04:01:06Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B", "base_model:adapter:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B", "region:us" ]	null	2025-03-05T15:15:56Z	--- library_name: peft base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B tags: - axolotl - generated_from_trainer model-index: - name: af1244bf-9e22-4425-83a4-9ff753c4b5a7 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) <br> # af1244bf-9e22-4425-83a4-9ff753c4b5a7 This model is a fine-tuned version of [Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5161 ## 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: 0.000202 - train_batch_size: 4 - eval_batch_size: 4 - seed: 20 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| 0.8768 \| \| 0.5175 \| 0.0585 \| 500 \| 0.5161 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso02/013addca-8225-4d70-af18-ef197d7fdf0e	lesso02	2025-03-06T04:00:58Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:lcw99/zephykor-ko-7b-chang", "base_model:adapter:lcw99/zephykor-ko-7b-chang", "region:us" ]	null	2025-03-05T12:51:52Z	--- library_name: peft base_model: lcw99/zephykor-ko-7b-chang tags: - axolotl - generated_from_trainer model-index: - name: 013addca-8225-4d70-af18-ef197d7fdf0e 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) <br> # 013addca-8225-4d70-af18-ef197d7fdf0e This model is a fine-tuned version of [lcw99/zephykor-ko-7b-chang](https://huggingface.co/lcw99/zephykor-ko-7b-chang) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2344 ## 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: 0.000202 - train_batch_size: 4 - eval_batch_size: 4 - seed: 20 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0011 \| 1 \| 3.2873 \| \| 1.9358 \| 0.5268 \| 500 \| 0.2344 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
texanrangee/dacdd58d-ee74-474f-9f64-1e82f56acc3e	texanrangee	2025-03-06T04:00:57Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-06T02:30:27Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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lesso02/1cc21e3b-88bd-471e-832c-65c9a75403dd	lesso02	2025-03-06T04:00:40Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:unsloth/mistral-7b-v0.3", "base_model:adapter:unsloth/mistral-7b-v0.3", "license:apache-2.0", "region:us" ]	null	2025-03-05T10:55:26Z	--- library_name: peft license: apache-2.0 base_model: unsloth/mistral-7b-v0.3 tags: - axolotl - generated_from_trainer model-index: - name: 1cc21e3b-88bd-471e-832c-65c9a75403dd 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) <br> # 1cc21e3b-88bd-471e-832c-65c9a75403dd This model is a fine-tuned version of [unsloth/mistral-7b-v0.3](https://huggingface.co/unsloth/mistral-7b-v0.3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1793 ## 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: 0.000202 - train_batch_size: 4 - eval_batch_size: 4 - seed: 20 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0008 \| 1 \| 0.4856 \| \| 1.5353 \| 0.3806 \| 500 \| 0.1793 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso01/5b93413b-7a81-4dca-a6b8-a94e7e50fce7	lesso01	2025-03-06T04:00:20Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-135M", "base_model:adapter:unsloth/SmolLM-135M", "license:apache-2.0", "region:us" ]	null	2025-03-05T21:46:25Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-135M tags: - axolotl - generated_from_trainer model-index: - name: 5b93413b-7a81-4dca-a6b8-a94e7e50fce7 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) <br> # 5b93413b-7a81-4dca-a6b8-a94e7e50fce7 This model is a fine-tuned version of [unsloth/SmolLM-135M](https://huggingface.co/unsloth/SmolLM-135M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7737 ## 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: 0.000201 - train_batch_size: 4 - eval_batch_size: 4 - seed: 10 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 7000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0007 \| 1 \| 1.2054 \| \| 0.9425 \| 0.3430 \| 500 \| 0.9315 \| \| 0.9202 \| 0.6860 \| 1000 \| 0.8849 \| \| 0.8687 \| 1.0291 \| 1500 \| 0.8518 \| \| 0.8354 \| 1.3721 \| 2000 \| 0.8317 \| \| 0.8244 \| 1.7151 \| 2500 \| 0.8181 \| \| 0.7854 \| 2.0581 \| 3000 \| 0.8035 \| \| 0.7883 \| 2.4011 \| 3500 \| 0.7945 \| \| 0.7697 \| 2.7441 \| 4000 \| 0.7866 \| \| 0.7577 \| 3.0872 \| 4500 \| 0.7821 \| \| 0.7594 \| 3.4302 \| 5000 \| 0.7772 \| \| 0.7453 \| 3.7732 \| 5500 \| 0.7731 \| \| 0.7461 \| 4.1163 \| 6000 \| 0.7755 \| \| 0.7604 \| 4.4593 \| 6500 \| 0.7726 \| \| 0.7341 \| 4.8023 \| 7000 \| 0.7737 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso01/f93efdee-b2f8-4324-ad9f-a7b75ceece9b	lesso01	2025-03-06T04:00:11Z	0	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:unsloth/gemma-1.1-2b-it", "base_model:adapter:unsloth/gemma-1.1-2b-it", "license:apache-2.0", "region:us" ]	null	2025-03-05T20:38:29Z	--- library_name: peft license: apache-2.0 base_model: unsloth/gemma-1.1-2b-it tags: - axolotl - generated_from_trainer model-index: - name: f93efdee-b2f8-4324-ad9f-a7b75ceece9b 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) <br> # f93efdee-b2f8-4324-ad9f-a7b75ceece9b This model is a fine-tuned version of [unsloth/gemma-1.1-2b-it](https://huggingface.co/unsloth/gemma-1.1-2b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9344 ## 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: 0.000201 - train_batch_size: 4 - eval_batch_size: 4 - seed: 10 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0002 \| 1 \| 5.4791 \| \| 1.9431 \| 0.0987 \| 500 \| 1.9344 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/Haphazardv1-i1-GGUF	mradermacher	2025-03-06T04:00:06Z	0	2	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Yoesph/Haphazardv1", "base_model:quantized:Yoesph/Haphazardv1", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-03-05T22:08:21Z	--- base_model: Yoesph/Haphazardv1 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/Yoesph/Haphazardv1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Haphazardv1-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/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 5.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 5.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 7.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 7.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 8.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-Q2_K.gguf) \| i1-Q2_K \| 9.0 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 9.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 10.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 10.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 10.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 10.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 11.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 12.5 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 12.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-Q4_0.gguf) \| i1-Q4_0 \| 13.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 13.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-Q4_1.gguf) \| i1-Q4_1 \| 15.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 16.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Haphazardv1-i1-GGUF/resolve/main/Haphazardv1.i1-Q6_K.gguf) \| i1-Q6_K \| 19.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
lesso01/4096dfb1-3a41-43c1-9d4e-fc6d98cbbadc	lesso01	2025-03-06T04:00:02Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B", "base_model:adapter:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B", "region:us" ]	null	2025-03-05T15:16:28Z	--- library_name: peft base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B tags: - axolotl - generated_from_trainer model-index: - name: 4096dfb1-3a41-43c1-9d4e-fc6d98cbbadc 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) <br> # 4096dfb1-3a41-43c1-9d4e-fc6d98cbbadc This model is a fine-tuned version of [Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5155 ## 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: 0.000201 - train_batch_size: 4 - eval_batch_size: 4 - seed: 10 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| 0.8768 \| \| 0.5133 \| 0.0585 \| 500 \| 0.5155 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso01/5da7de8d-3f88-401a-a526-c1ec5d5911df	lesso01	2025-03-06T03:59:55Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-14B-Instruct", "base_model:adapter:unsloth/Qwen2.5-14B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-03-05T09:55:40Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-14B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 5da7de8d-3f88-401a-a526-c1ec5d5911df 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) <br> # 5da7de8d-3f88-401a-a526-c1ec5d5911df This model is a fine-tuned version of [unsloth/Qwen2.5-14B-Instruct](https://huggingface.co/unsloth/Qwen2.5-14B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3598 ## 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: 0.000201 - train_batch_size: 4 - eval_batch_size: 4 - seed: 10 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0008 \| 1 \| 1.5343 \| \| 0.3809 \| 0.3806 \| 500 \| 0.3598 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ASpiderSteeped/egirl	ASpiderSteeped	2025-03-06T03:59:30Z	0	0	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", "region:us" ]	text-to-image	2025-03-06T03:57:29Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/df0r49d-9e3d71f5-ecbb-4009-bcd2-0429aab52308.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: egirl, ahegao --- # egirl <Gallery /> ## Trigger words You should use `egirl` to trigger the image generation. You should use `ahegao` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/ASpiderSteeped/egirl/tree/main) them in the Files & versions tab.
rohinm/model_top_p_5	rohinm	2025-03-06T03:57:32Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-06T03:55:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Jonjew/ShuQi	Jonjew	2025-03-06T03:54:01Z	0	0	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:unknown", "region:us" ]	text-to-image	2025-03-06T03:53:25Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- Breathtaking over the shoulder shot photography of ohwx looking at viewer, imperfections, necklace, looking over shoulders, eyelashes, fine hair detail, entire hairstyle visible, perfect eyes with iris pattern, sensual lips, nose, (perfectly sharp:1.3), realistic textures, (deep focus, focus on background:1.5), 8k uhd, dslr, ultra high quality image, film grain, Fujifilm XT3 parameters: negative_prompt: ShuQi_flux_lora_v1_000001500_Weight-1.1 output: url: >- images/ShuQi_flux_lora_v1_000001500_Weight-1.1_2024-12-06_2024-12-06-032508_0.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: ohwx license: unknown --- # Shu Qi (Flux) <Gallery /> ## Model description FROM https://civitai.com/models/1131170/shu-qi-flux?modelVersionId=1271648 Trigger ohwx Strength 1 Guidance: 2.2-3 Steps (dev): 30-40 👍 * If you love it, like it! *👍 workflow: https://civitai.com/models/1088678 👑 Shu Qi 🎬 About my celebrities loras 90% of the dataset used to build my loras only use head images. That really help the blend with other lora or model as there is no hands, feet, that may or will interfere in the final image render. When you get distorted hands with a person lora, it's because there is info on hands in the dataset used to train the lora, but that will not happen with my loras. I've trained on Flux.1 Dev so other merged or trained checkpoint may not work well with my loras. The drawback side of that is that the body may not be reflecting the reality. It may not be a drawback tho. This is a lora for Flux.1 Dev. Work with other model but you must drop some simple bloc (good start 19-32). Trained with ai-toolkit, so merging it is not easy. To get the best result Guidance: 2.2-3 Steps (dev): 30-40 daemon detailer (lying sigma sampler): factor: -0.02, start 0.06, end 0.75 Resolution: Upscale the latent by 1.25 or 1.5 you'll get awsome result. (take longer time but worth it) Trigger word is (may work better in certain context): ohwx Enjoy! ## Trigger words You should use `ohwx` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/ShuQi/tree/main) them in the Files & versions tab.
John6666/alice-illustrious-v13-sdxl	John6666	2025-03-06T03:53:43Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "ntrmix", "animagine", "animagine4", "illustrious", "en", "base_model:Laxhar/noobai-XL-1.1", "base_model:finetune:Laxhar/noobai-XL-1.1", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-03-06T03:44:40Z	--- 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 - ntrmix - animagine - animagine4 - illustrious base_model: - OnomaAIResearch/Illustrious-xl-early-release-v0 - Laxhar/noobai-XL-1.1 - cagliostrolab/animagine-xl-4.0 --- Original model is [here](https://civitai.com/models/1211538?modelVersionId=1495614). This model created by [jmeswin10](https://civitai.com/user/jmeswin10).
fats-fme/a4050380-ed90-4d12-b757-d8c7da8595f0	fats-fme	2025-03-06T03:53:14Z	0	0	peft	[ "peft", "safetensors", "phi3", "axolotl", "generated_from_trainer", "custom_code", "base_model:numind/NuExtract-1.5", "base_model:adapter:numind/NuExtract-1.5", "license:mit", "region:us" ]	null	2025-03-06T03:14:07Z	--- library_name: peft license: mit base_model: numind/NuExtract-v1.5 tags: - axolotl - generated_from_trainer model-index: - name: a4050380-ed90-4d12-b757-d8c7da8595f0 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: numind/NuExtract-v1.5 bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 314e7b4648fe0f24_train_data.json ds_type: json format: custom path: /workspace/input_data/314e7b4648fe0f24_train_data.json type: field_input: init_response field_instruction: prompt field_output: revision_response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 100 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: fats-fme/a4050380-ed90-4d12-b757-d8c7da8595f0 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: 10 lora_alpha: 256 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 128 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 70GB max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/314e7b4648fe0f24_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 100 saves_per_epoch: null sequence_len: 2048 strict: false tf32: 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: 4b6eb12a-d3f7-4e8d-838a-6f7133f4d322 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 4b6eb12a-d3f7-4e8d-838a-6f7133f4d322 warmup_steps: 100 weight_decay: 0.05 xformers_attention: null ``` </details><br> # a4050380-ed90-4d12-b757-d8c7da8595f0 This model is a fine-tuned version of [numind/NuExtract-v1.5](https://huggingface.co/numind/NuExtract-v1.5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2271 ## 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: 100 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0002 \| 1 \| 0.5925 \| \| 0.9069 \| 0.0192 \| 100 \| 0.2619 \| \| 0.9986 \| 0.0383 \| 200 \| 0.2271 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
naimulislam/aurora-think3-GGUF	naimulislam	2025-03-06T03:52:56Z	0	0	transformers	[ "transformers", "gguf", "qwen2", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-03-06T03:17:59Z	--- base_model: unsloth/qwen2.5-0.5b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: naimulislam - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-0.5b-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mlfoundations-dev/instruction_filtering_scale_up_code_base_embedding_filter_mean_per_domain_1K	mlfoundations-dev	2025-03-06T03:51:27Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-06T02:52:15Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: instruction_filtering_scale_up_code_base_embedding_filter_mean_per_domain_1K results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # instruction_filtering_scale_up_code_base_embedding_filter_mean_per_domain_1K This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/instruction_filtering_scale_up_code_base_embedding_filter_mean_per_domain_1K dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 12 - total_train_batch_size: 96 - total_eval_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.3.0 - Datasets 3.1.0 - Tokenizers 0.20.3
xrt89/SIAXRT1	xrt89	2025-03-06T03:51:26Z	0	0	diffusers	[ "diffusers", "flux", "text-to-image", "lora", "fal", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-03-06T03:51:07Z	--- tags: - flux - text-to-image - lora - diffusers - fal base_model: black-forest-labs/FLUX.1-dev instance_prompt: SIAXRT 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 --- # SIAXRT1 <Gallery /> ## Model description ## Trigger words You should use `SIAXRT` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/xrt89/SIAXRT1/tree/main) them in the Files & versions tab. ## Training at fal.ai Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).
mlfoundations-dev/instruction_filtering_scale_up_code_base_embedding_filter_mean_1K	mlfoundations-dev	2025-03-06T03:51:22Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-06T02:51:31Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: instruction_filtering_scale_up_code_base_embedding_filter_mean_1K results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # instruction_filtering_scale_up_code_base_embedding_filter_mean_1K This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/instruction_filtering_scale_up_code_base_embedding_filter_mean_1K dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 12 - total_train_batch_size: 96 - total_eval_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.3.0 - Datasets 3.1.0 - Tokenizers 0.20.3
huggingkot/Llama-3-8B-Instruct-abliterated-v2-bnb-4bit	huggingkot	2025-03-06T03:51:11Z	0	0	null	[ "safetensors", "base_model:cognitivecomputations/Llama-3-8B-Instruct-abliterated-v2", "base_model:finetune:cognitivecomputations/Llama-3-8B-Instruct-abliterated-v2", "8-bit", "region:us" ]	null	2025-03-06T03:50:24Z	--- base_model: - cognitivecomputations/Llama-3-8B-Instruct-abliterated-v2 --- This is a converted weight from [Llama-3-8B-Instruct-abliterated-v2](https://huggingface.co/cognitivecomputations/Llama-3-8B-Instruct-abliterated-v2) model in [unsloth 4-bit dynamic quant](https://archive.is/EFz7P) using this [collab notebook](https://colab.research.google.com/drive/1P23C66j3ga49kBRnDNlmRce7R_l_-L5l?usp=sharing). ## About this Conversion This conversion uses Unsloth to load the model in 4-bit format and force-save it in the same 4-bit format. ### How 4-bit Quantization Works - The actual 4-bit quantization is handled by BitsAndBytes (bnb), which works under Torch via AutoGPTQ or BitsAndBytes. - Unsloth acts as a wrapper, simplifying and optimizing the process for better efficiency. This allows for reduced memory usage and faster inference while keeping the model compact.
naimulislam/aurora-think3	naimulislam	2025-03-06T03:49:20Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-03-06T03:14:03Z	--- base_model: unsloth/qwen2.5-0.5b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: naimulislam - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-0.5b-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
kenken6696/Llama-3.2-3B_3_mix_position_famous_unrecognized	kenken6696	2025-03-06T03:45:44Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-06T03:42:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
huggingkot/IceNalyvkaRP-7b-bnb-4bit	huggingkot	2025-03-06T03:40:05Z	0	0	null	[ "safetensors", "base_model:icefog72/IceNalyvkaRP-7b", "base_model:finetune:icefog72/IceNalyvkaRP-7b", "8-bit", "region:us" ]	null	2025-03-06T03:38:52Z	--- base_model: - icefog72/IceNalyvkaRP-7b --- This is a converted weight from [IceNalyvkaRP-7b](https://huggingface.co/icefog72/IceNalyvkaRP-7b) model in [unsloth 4-bit dynamic quant](https://archive.is/EFz7P) using this [collab notebook](https://colab.research.google.com/drive/1P23C66j3ga49kBRnDNlmRce7R_l_-L5l?usp=sharing). ## About this Conversion This conversion uses Unsloth to load the model in 4-bit format and force-save it in the same 4-bit format. ### How 4-bit Quantization Works - The actual 4-bit quantization is handled by BitsAndBytes (bnb), which works under Torch via AutoGPTQ or BitsAndBytes. - Unsloth acts as a wrapper, simplifying and optimizing the process for better efficiency. This allows for reduced memory usage and faster inference while keeping the model compact.
xxhe/sft-Llama-2-13b-chat-hf	xxhe	2025-03-06T03:39:50Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-05T04:43:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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. 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xrt89/SIAXRT	xrt89	2025-03-06T03:36:16Z	0	0	diffusers	[ "diffusers", "flux", "text-to-image", "lora", "fal", "license:other", "region:us" ]	text-to-image	2025-03-06T03:36:06Z	--- tags: - flux - text-to-image - lora - diffusers - fal base_model: undefined instance_prompt: license: other --- # SIAXRT <Gallery /> ## Model description ## Trigger words You should use `` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/xrt89/SIAXRT/tree/main) them in the Files & versions tab. ## Training at fal.ai Training was done using [fal.ai/models/fal-ai/flux-lora-portrait-trainer](https://fal.ai/models/fal-ai/flux-lora-portrait-trainer).
mradermacher/jais-family-13b-chat-i1-GGUF	mradermacher	2025-03-06T03:34:34Z	0	0	transformers	[ "transformers", "gguf", "Arabic", "English", "LLM", "Decoder", "causal-lm", "jais-family", "ar", "en", "base_model:inceptionai/jais-family-13b-chat", "base_model:quantized:inceptionai/jais-family-13b-chat", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-03-06T02:25:41Z	--- base_model: inceptionai/jais-family-13b-chat language: - ar - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - Arabic - English - LLM - Decoder - causal-lm - jais-family --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/inceptionai/jais-family-13b-chat <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/jais-family-13b-chat-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/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-IQ1_S.gguf) \| i1-IQ1_S \| 4.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-IQ1_M.gguf) \| i1-IQ1_M \| 4.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-IQ2_S.gguf) \| i1-IQ2_S \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-IQ2_M.gguf) \| i1-IQ2_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 5.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-Q2_K.gguf) \| i1-Q2_K \| 5.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 6.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-IQ3_S.gguf) \| i1-IQ3_S \| 6.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 6.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-IQ3_M.gguf) \| i1-IQ3_M \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 7.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 7.7 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 7.8 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 8.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-Q4_1.gguf) \| i1-Q4_1 \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 9.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/jais-family-13b-chat-i1-GGUF/resolve/main/jais-family-13b-chat.i1-Q6_K.gguf) \| i1-Q6_K \| 11.8 \| 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 -->
huggingkot/Lumimaid-v0.2-8B-bnb-4bit	huggingkot	2025-03-06T03:34:28Z	0	0	null	[ "safetensors", "base_model:NeverSleep/Lumimaid-v0.2-8B", "base_model:finetune:NeverSleep/Lumimaid-v0.2-8B", "8-bit", "region:us" ]	null	2025-03-06T03:32:35Z	--- base_model: - NeverSleep/Lumimaid-v0.2-8B --- This is a converted weight from [Lumimaid-v0.2-8B](https://huggingface.co/NeverSleep/Lumimaid-v0.2-8B) model in [unsloth 4-bit dynamic quant](https://archive.is/EFz7P) using this [collab notebook](https://colab.research.google.com/drive/1P23C66j3ga49kBRnDNlmRce7R_l_-L5l?usp=sharing). ## About this Conversion This conversion uses Unsloth to load the model in 4-bit format and force-save it in the same 4-bit format. ### How 4-bit Quantization Works - The actual 4-bit quantization is handled by BitsAndBytes (bnb), which works under Torch via AutoGPTQ or BitsAndBytes. - Unsloth acts as a wrapper, simplifying and optimizing the process for better efficiency. This allows for reduced memory usage and faster inference while keeping the model compact.
EndersonPro/3nd3rs0nVizc4	EndersonPro	2025-03-06T03:34:20Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-03-06T03:22:23Z	--- 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: 3nd3rs0nVizc4 --- # 3Nd3Rs0Nvizc4 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `3nd3rs0nVizc4` 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('EndersonPro/3nd3rs0nVizc4', 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)
Dracones/QwQ-32B_exl2_6.0bpw	Dracones	2025-03-06T03:32:31Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "exl2", "conversational", "en", "base_model:Qwen/QwQ-32B", "base_model:quantized:Qwen/QwQ-32B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "region:us" ]	text-generation	2025-03-06T03:28:28Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/QWQ-32B/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: Qwen/QwQ-32B tags: - chat - exl2 library_name: transformers --- # QwQ-32B - EXL2 6.0bpw This is a 6.0bpw EXL2 quant of [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) Details about the model can be found at the above model page. ## Perplexity Scoring Below are the perplexity scores for the EXL2 models. A lower score is better. \| Quant Level \| Perplexity Score \| \|-------------\|------------------\| \| 8.0 \| 6.4393 \| \| 7.0 \| 6.4452 \| \| 6.0 \| 6.4693 \| \| 5.0 \| 6.4732 \| \| 4.5 \| 6.5417 \| \| 4.0 \| 6.6190 \|
jimbowyer123/OtterCountdown	jimbowyer123	2025-03-06T03:32:25Z	0	0	null	[ "safetensors", "unsloth", "license:mit", "region:us" ]	null	2025-02-04T23:15:31Z	--- license: mit tags: - unsloth ---
ClarenceDan/3763e69c-6347-467f-9f94-4a370c10951c	ClarenceDan	2025-03-06T03:31:56Z	0	0	peft	[ "peft", "safetensors", "phi3", "axolotl", "generated_from_trainer", "custom_code", "base_model:numind/NuExtract-1.5", "base_model:adapter:numind/NuExtract-1.5", "license:mit", "region:us" ]	null	2025-03-06T03:15:39Z	--- library_name: peft license: mit base_model: numind/NuExtract-v1.5 tags: - axolotl - generated_from_trainer model-index: - name: 3763e69c-6347-467f-9f94-4a370c10951c 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: numind/NuExtract-v1.5 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 314e7b4648fe0f24_train_data.json ds_type: json format: custom path: /workspace/input_data/314e7b4648fe0f24_train_data.json type: field_input: init_response field_instruction: prompt field_output: revision_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: 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/3763e69c-6347-467f-9f94-4a370c10951c 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/314e7b4648fe0f24_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: 4b6eb12a-d3f7-4e8d-838a-6f7133f4d322 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 4b6eb12a-d3f7-4e8d-838a-6f7133f4d322 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 3763e69c-6347-467f-9f94-4a370c10951c This model is a fine-tuned version of [numind/NuExtract-v1.5](https://huggingface.co/numind/NuExtract-v1.5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4484 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.7482 \| 0.0002 \| 1 \| 0.5930 \| \| 2.1758 \| 0.0006 \| 3 \| 0.5899 \| \| 2.115 \| 0.0011 \| 6 \| 0.5544 \| \| 2.0178 \| 0.0017 \| 9 \| 0.4484 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
d8912046/lora_model_test7	d8912046	2025-03-06T03:31:25Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-06T03:19:43Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: d8912046 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
zakariamtl/mustaphablidry	zakariamtl	2025-03-06T03:29:53Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-03-06T03:29:50Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Mustaphablidry <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` 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('zakariamtl/mustaphablidry', 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)
RRoy233/Qwen2.5-7B-Instruct-inter-gsm8k-0305_222603	RRoy233	2025-03-06T03:29:31Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "grpo", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-03-06T03:26:43Z	--- base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - grpo license: apache-2.0 language: - en --- # Uploaded model - Developed by: RRoy233 - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
casque/zy_Realism_Enhancer_v2	casque	2025-03-06T03:29:27Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-03-06T03:24:53Z	--- license: creativeml-openrail-m ---
zakariamtl/sihamezzerhouni	zakariamtl	2025-03-06T03:27:38Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-03-06T03:27:37Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Sihamezzerhouni <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` 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('zakariamtl/sihamezzerhouni', 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)
TareksLab/L3.3-TRP-BASE-50-70B	TareksLab	2025-03-06T03:26:54Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2408.07990", "base_model:Sao10K/L3-70B-Euryale-v2.1", "base_model:merge:Sao10K/L3-70B-Euryale-v2.1", "base_model:SicariusSicariiStuff/Negative_LLAMA_70B", "base_model:merge:SicariusSicariiStuff/Negative_LLAMA_70B", "base_model:TheDrummer/Fallen-Llama-3.3-R1-70B-v1", "base_model:merge:TheDrummer/Fallen-Llama-3.3-R1-70B-v1", "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", "region:us" ]	text-generation	2025-03-06T02:33:51Z	--- base_model: - SicariusSicariiStuff/Negative_LLAMA_70B - nbeerbower/Llama-3.1-Nemotron-lorablated-70B - Sao10K/L3-70B-Euryale-v2.1 - TheDrummer/Fallen-Llama-3.3-R1-70B-v1 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 [SCE](https://arxiv.org/abs/2408.07990) merge method using [nbeerbower/Llama-3.1-Nemotron-lorablated-70B](https://huggingface.co/nbeerbower/Llama-3.1-Nemotron-lorablated-70B) as a base. ### Models Merged The following models were included in the merge: * [SicariusSicariiStuff/Negative_LLAMA_70B](https://huggingface.co/SicariusSicariiStuff/Negative_LLAMA_70B) * [Sao10K/L3-70B-Euryale-v2.1](https://huggingface.co/Sao10K/L3-70B-Euryale-v2.1) * [TheDrummer/Fallen-Llama-3.3-R1-70B-v1](https://huggingface.co/TheDrummer/Fallen-Llama-3.3-R1-70B-v1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: TheDrummer/Fallen-Llama-3.3-R1-70B-v1 - model: Sao10K/L3-70B-Euryale-v2.1 - model: SicariusSicariiStuff/Negative_LLAMA_70B merge_method: sce base_model: nbeerbower/Llama-3.1-Nemotron-lorablated-70B parameters: select_topk: 0.50 dtype: float32 out_dtype: bfloat16 tokenizer: source: nbeerbower/Llama-3.1-Nemotron-lorablated-70B ```
pocodev/Survey-Gen-DSR1-8b-1-Merged	pocodev	2025-03-06T03:25:25Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-03-06T03:25:05Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: pocodev - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ontocord/wide_3b_sft_stage1.1-ss1-with_generics_intr_math_stories.no_issue	ontocord	2025-03-06T03:25:22Z	21	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-02T20:14:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Nerva1228/fengtu	Nerva1228	2025-03-06T03:24:39Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-03-06T03:24:38Z	--- 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: fengtu --- # Fengtu <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `fengtu` 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('Nerva1228/fengtu', 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)
ontocord/wide_3b_sft_stage1.1-ss1-with_generics_intr_math.no_issue	ontocord	2025-03-06T03:24:13Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-06T03:19:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Dracones/QwQ-32B_exl2_8.0bpw	Dracones	2025-03-06T03:23:45Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "exl2", "conversational", "en", "base_model:Qwen/QwQ-32B", "base_model:quantized:Qwen/QwQ-32B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "region:us" ]	text-generation	2025-03-06T03:19:11Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/QWQ-32B/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: Qwen/QwQ-32B tags: - chat - exl2 library_name: transformers --- # QwQ-32B - EXL2 8.0bpw This is a 8.0bpw EXL2 quant of [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) Details about the model can be found at the above model page. ## Perplexity Scoring Below are the perplexity scores for the EXL2 models. A lower score is better. \| Quant Level \| Perplexity Score \| \|-------------\|------------------\| \| 8.0 \| 6.4393 \| \| 7.0 \| 6.4452 \| \| 6.0 \| 6.4693 \| \| 5.0 \| 6.4732 \| \| 4.5 \| 6.5417 \| \| 4.0 \| 6.6190 \|
tona3738/qlora_test_model	tona3738	2025-03-06T03:23:15Z	0	0	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-03-06T03:20:17Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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. 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Yorkinjon/whisper-small-uzbek-ynv2	Yorkinjon	2025-03-06T03:21:56Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "uz", "dataset:mozilla-foundation/common_voice_17_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-03-05T04:38:37Z	--- library_name: transformers language: - uz license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_17_0 metrics: - wer model-index: - name: Whisper Small uz - Yorkerdev results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 17.0 type: mozilla-foundation/common_voice_17_0 config: uz split: test args: 'config: uz, split: test' metrics: - name: Wer type: wer value: 34.6134644666883 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small uz - Yorkerdev This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3621 - Wer: 34.6135 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - 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 - lr_scheduler_warmup_steps: 500 - training_steps: 6000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.6038 \| 0.2640 \| 1000 \| 0.5630 \| 48.7719 \| \| 0.4917 \| 0.5279 \| 2000 \| 0.4511 \| 40.7366 \| \| 0.4377 \| 0.7919 \| 3000 \| 0.4073 \| 37.9496 \| \| 0.3151 \| 1.0557 \| 4000 \| 0.3867 \| 38.4776 \| \| 0.2944 \| 1.3197 \| 5000 \| 0.3679 \| 35.5133 \| \| 0.275 \| 1.5836 \| 6000 \| 0.3621 \| 34.6135 \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.6.0+cu118 - Datasets 3.3.2 - Tokenizers 0.21.0
Dracones/EXL2_Measurements	Dracones	2025-03-06T03:21:12Z	0	4	null	[ "region:us" ]	null	2024-04-02T18:48:18Z	# Midnight Miqu EXL2 Measurement Files This repository contains EXL2 measurement files for quants made here. ## Contents \| Filename \| Description \| Model Link \| \|---------------------------------\|---------------------------------------------------------------------------------------------------------------------\|-------------------------------------------------------------------------------------------\| \| `midnight70b.json` \| EXL2 measurement file for Midnight Miqu 70b \| [sophosympatheia/Midnight-Miqu-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.0) \| \| `midnight103b.json` \| EXL2 measurement file for Midnight Miqu 103b \| [sophosympatheia/Midnight-Miqu-103B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Miqu-103B-v1.0) \| \| `midnight70b-v15.json` \| EXL2 measurement file for Midnight Miqu 70b v1.5 \| [sophosympatheia/Midnight-Miqu-70B-v1.5](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.5) \| \| `Merged-RP-Stew-V2-34B.json` \| EXL2 measurement file for Merged RP Stew V2 34B \| [ParasiticRogue/Merged-RP-Stew-V2-34B](https://huggingface.co/ParasiticRogue/Merged-RP-Stew-V2-34B) \| \| `perky70b.json` \| EXL2 measurement file for Perky 70b \| [Dracones/perky-70b-v0.1](https://huggingface.co/Dracones/perky-70b-v0.1) \| \| `perky103b.json` \| EXL2 measurement file for Perky 103b \| [Dracones/perky-103b-v0.1](https://huggingface.co/Dracones/perky-103b-v0.1) \| \| `miqu-1-70b-sf.json` \| EXL2 measurement file for miqu-1-70b-sf \| [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) \| \| `c4ai-command-r-v01.json` \| EXL2 measurement file for Cohere Command R \| [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) \| \| `c4ai-command-r-v01-rpcal.json` \| EXL2 measurement file for Cohere Command R, RP Calibrated again PIPPA-cleaned \| [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) \| \| `Mixtral-8x22B-v0.1.json` \| EXL2 measurement file for Mixtral 8x22 v0.1 \| [mistral-community/Mixtral-8x22B-v0.1](https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1) \| \| `mixtral-8x22b-instruct-oh.json` \| EXL2 measurement file for mixtral-8x22b-instruct-oh \| [fireworks-ai/mixtral-8x22b-instruct-oh](https://huggingface.co/fireworks-ai/mixtral-8x22b-instruct-oh) \| \| `WizardLM-2-8x22B.json` \| EXL2 measurement file for WizardLM-2-8x22B \| [microsoft/WizardLM-2-8x22B](https://huggingface.co/microsoft/WizardLM-2-8x22B) \| \| `CodeQwen1.5-7B-Chat.json` \| EXL2 measurement file for CodeQwen1.5-7B-Chat \| [Qwen/CodeQwen1.5-7B-Chat](https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat) \| \| `CodeQwen1.5-7B.json` \| EXL2 measurement file for CodeQwen1.5-7B \| [Qwen/CodeQwen1.5-7B](https://huggingface.co/Qwen/CodeQwen1.5-7B) \| \| `Mixtral-8x22B-Instruct-v0.1.json` \| EXL2 measurement file for Mixtral-8x22B-Instruct-v0.1 \| [mistralai/Mixtral-8x22B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1) \| \| `Llama-3-Lumimaid-70B-v0.1.json` \| EXL2 measurement file for Llama-3-Lumimaid-70B-v0.1 \| [NeverSleep/Llama-3-Lumimaid-70B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1) \| \| `Qwen2.5-72B-Instruct.json` \| EXL2 measurement file for Qwen2.5-72B-Instruct \| [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) \| \| `Qwen2.5-Coder-32B-Instruct.json` \| EXL2 measurement file for Qwen2.5-Coder-32B-Instruct \| [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) \| \| `QwQ-32B-Preview.json` \| EXL2 measurement file for QwQ-32B-Preview \| [Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview) \| \| `Athene-V2-Chat.json` \| EXL2 measurement file for Athene-V2-Chat \| [Nexusflow/Athene-V2-Chat](https://huggingface.co/Nexusflow/Athene-V2-Chat) \| \| `Llama-3.1-Nemotron-70B-Instruct.json` \| EXL2 measurement file for Llama-3.1-Nemotron-70B-Instruct \| [nvidia/Llama-3.1-Nemotron-70B-Instruct-HF](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF) \| \| `Qwen2.5-32B-Instruct.json` \| EXL2 measurement file for Qwen2.5-32B-Instruct \| [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) \| \| `Chronos-Platinum-72B.json` \| EXL2 measurement file for Chronos-Platinum-72B \| [ZeusLabs/Chronos-Platinum-72B](https://huggingface.co/ZeusLabs/Chronos-Platinum-72B) \| \| `Evathene-v1.3.json` \| EXL2 measurement file for Evathene-v1.3 \| [sophosympatheia/Evathene-v1.3](https://huggingface.co/sophosympatheia/Evathene-v1.3) \| \| `Llama-3.3-70B-Instruct.json` \| EXL2 measurement file for Llama-3.3-70B-Instruct \| [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) \| \| `EVA-LLaMA-3.33-70B-v0.0.json` \| EXL2 measurement file for EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0 \| [EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0](https://huggingface.co/EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0) \| \| `L3.3-70B-Euryale-v2.3.json` \| EXL2 measurement file for Sao10K/L3.3-70B-Euryale-v2.3 \| [Sao10K/L3.3-70B-Euryale-v2.3](https://huggingface.co/Sao10K/L3.3-70B-Euryale-v2.3) \| \| `72B-Qwen2.5-Kunou-v1.json` \| EXL2 measurement file for Sao10K/72B-Qwen2.5-Kunou-v1 \| [Sao10K/72B-Qwen2.5-Kunou-v1](https://huggingface.co/Sao10K/72B-Qwen2.5-Kunou-v1) \| \| `QVQ-72B-Preview.json` \| EXL2 measurement file for Qwen/QVQ-72B-Preview \| [Qwen/QVQ-72B-Preview](https://huggingface.co/Qwen/QVQ-72B-Preview) \| \| `Anubis-70B-v1.json` \| EXL2 measurement file for TheDrummer/Anubis-70B-v1 \| [TheDrummer/Anubis-70B-v1](https://huggingface.co/TheDrummer/Anubis-70B-v1) \| \| `Evayale-v1.0.json` \| EXL2 measurement file for sophosympatheia/Evayale-v1.0 \| [sophosympatheia/Evayale-v1.0](https://huggingface.co/sophosympatheia/Evayale-v1.0) \| \| `L3.3-Damascus-R1.json` \| EXL2 measurement file for Steelskull/L3.3-Damascus-R1 \| [Steelskull/L3.3-Damascus-R1](https://huggingface.co/Steelskull/L3.3-Damascus-R1) \| \| `QwQ-32B.json` \| EXL2 measurement file for Qwen/QwQ-32B \| [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) \|
666tuna/HF3B_callme	666tuna	2025-03-06T03:20:58Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-06T03:20:47Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
tona3738/results	tona3738	2025-03-06T03:20:15Z	4	0	null	[ "safetensors", "bert", "generated_from_trainer", "base_model:robzchhangte/MizBERT", "base_model:finetune:robzchhangte/MizBERT", "license:apache-2.0", "region:us" ]	null	2024-08-15T17:35:41Z	--- license: apache-2.0 base_model: robzchhangte/MizBERT tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: results 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. --> # results This model is a fine-tuned version of [robzchhangte/MizBERT](https://huggingface.co/robzchhangte/MizBERT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7248 - Accuracy: 0.5346 - F1: 0.5346 - Precision: 0.5346 - Recall: 0.5346 ## 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: 15 - eval_batch_size: 15 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 1.6747 \| 0.0585 \| 10 \| 1.2733 \| 0.5 \| 0.5 \| 0.5 \| 0.5 \| \| 1.2979 \| 0.1170 \| 20 \| 1.1629 \| 0.5219 \| 0.5219 \| 0.5219 \| 0.5219 \| \| 1.0906 \| 0.1754 \| 30 \| 1.1048 \| 0.5234 \| 0.5234 \| 0.5234 \| 0.5234 \| \| 0.9134 \| 0.2339 \| 40 \| 0.8426 \| 0.5109 \| 0.5109 \| 0.5109 \| 0.5109 \| \| 0.7985 \| 0.2924 \| 50 \| 0.7739 \| 0.525 \| 0.525 \| 0.525 \| 0.525 \| \| 0.7278 \| 0.3509 \| 60 \| 0.7949 \| 0.4969 \| 0.4969 \| 0.4969 \| 0.4969 \| \| 0.7522 \| 0.4094 \| 70 \| 0.7225 \| 0.525 \| 0.525 \| 0.525 \| 0.525 \| \| 0.7134 \| 0.4678 \| 80 \| 0.7187 \| 0.5109 \| 0.5109 \| 0.5109 \| 0.5109 \| \| 0.6897 \| 0.5263 \| 90 \| 0.7682 \| 0.4781 \| 0.4781 \| 0.4781 \| 0.4781 \| \| 0.7369 \| 0.5848 \| 100 \| 0.7019 \| 0.5078 \| 0.5078 \| 0.5078 \| 0.5078 \| \| 0.6917 \| 0.6433 \| 110 \| 0.6980 \| 0.5109 \| 0.5109 \| 0.5109 \| 0.5109 \| \| 0.698 \| 0.7018 \| 120 \| 0.7038 \| 0.5297 \| 0.5297 \| 0.5297 \| 0.5297 \| \| 0.6974 \| 0.7602 \| 130 \| 0.7039 \| 0.5125 \| 0.5125 \| 0.5125 \| 0.5125 \| \| 0.7141 \| 0.8187 \| 140 \| 0.6941 \| 0.5047 \| 0.5047 \| 0.5047 \| 0.5047 \| \| 0.7127 \| 0.8772 \| 150 \| 0.6937 \| 0.5 \| 0.5 \| 0.5 \| 0.5 \| \| 0.7007 \| 0.9357 \| 160 \| 0.7047 \| 0.5266 \| 0.5266 \| 0.5266 \| 0.5266 \| \| 0.7483 \| 0.9942 \| 170 \| 0.6975 \| 0.4828 \| 0.4828 \| 0.4828 \| 0.4828 \| \| 0.7063 \| 1.0526 \| 180 \| 0.6929 \| 0.5266 \| 0.5266 \| 0.5266 \| 0.5266 \| \| 0.6848 \| 1.1111 \| 190 \| 0.7107 \| 0.4797 \| 0.4797 \| 0.4797 \| 0.4797 \| \| 0.7014 \| 1.1696 \| 200 \| 0.6891 \| 0.5422 \| 0.5422 \| 0.5422 \| 0.5422 \| \| 0.7113 \| 1.2281 \| 210 \| 0.6950 \| 0.5141 \| 0.5141 \| 0.5141 \| 0.5141 \| \| 0.6915 \| 1.2865 \| 220 \| 0.6901 \| 0.5391 \| 0.5391 \| 0.5391 \| 0.5391 \| \| 0.6834 \| 1.3450 \| 230 \| 0.7117 \| 0.5188 \| 0.5188 \| 0.5188 \| 0.5188 \| \| 0.7032 \| 1.4035 \| 240 \| 0.7029 \| 0.5031 \| 0.5031 \| 0.5031 \| 0.5031 \| \| 0.6962 \| 1.4620 \| 250 \| 0.6952 \| 0.5312 \| 0.5312 \| 0.5312 \| 0.5312 \| \| 0.7103 \| 1.5205 \| 260 \| 0.7165 \| 0.5297 \| 0.5297 \| 0.5297 \| 0.5297 \| \| 0.7405 \| 1.5789 \| 270 \| 0.8608 \| 0.475 \| 0.4750 \| 0.475 \| 0.475 \| \| 0.7633 \| 1.6374 \| 280 \| 0.6994 \| 0.5344 \| 0.5344 \| 0.5344 \| 0.5344 \| \| 0.7061 \| 1.6959 \| 290 \| 0.6887 \| 0.5531 \| 0.5531 \| 0.5531 \| 0.5531 \| \| 0.6975 \| 1.7544 \| 300 \| 0.7105 \| 0.475 \| 0.4750 \| 0.475 \| 0.475 \| \| 0.7098 \| 1.8129 \| 310 \| 0.6959 \| 0.5297 \| 0.5297 \| 0.5297 \| 0.5297 \| \| 0.7703 \| 1.8713 \| 320 \| 0.6954 \| 0.5281 \| 0.5281 \| 0.5281 \| 0.5281 \| \| 0.6948 \| 1.9298 \| 330 \| 0.7116 \| 0.475 \| 0.4750 \| 0.475 \| 0.475 \| \| 0.689 \| 1.9883 \| 340 \| 0.7261 \| 0.475 \| 0.4750 \| 0.475 \| 0.475 \| \| 0.7011 \| 2.0468 \| 350 \| 0.7265 \| 0.5234 \| 0.5234 \| 0.5234 \| 0.5234 \| \| 0.7026 \| 2.1053 \| 360 \| 0.7217 \| 0.4734 \| 0.4734 \| 0.4734 \| 0.4734 \| \| 0.6837 \| 2.1637 \| 370 \| 0.7001 \| 0.4984 \| 0.4984 \| 0.4984 \| 0.4984 \| \| 0.6579 \| 2.2222 \| 380 \| 0.7106 \| 0.525 \| 0.525 \| 0.525 \| 0.525 \| \| 0.6755 \| 2.2807 \| 390 \| 0.7218 \| 0.525 \| 0.525 \| 0.525 \| 0.525 \| \| 0.6739 \| 2.3392 \| 400 \| 0.7054 \| 0.5172 \| 0.5172 \| 0.5172 \| 0.5172 \| \| 0.6757 \| 2.3977 \| 410 \| 0.7015 \| 0.5406 \| 0.5406 \| 0.5406 \| 0.5406 \| \| 0.7135 \| 2.4561 \| 420 \| 0.7396 \| 0.4828 \| 0.4828 \| 0.4828 \| 0.4828 \| \| 0.6801 \| 2.5146 \| 430 \| 0.7323 \| 0.4906 \| 0.4906 \| 0.4906 \| 0.4906 \| \| 0.7349 \| 2.5731 \| 440 \| 0.6939 \| 0.5047 \| 0.5047 \| 0.5047 \| 0.5047 \| \| 0.6813 \| 2.6316 \| 450 \| 0.6957 \| 0.5234 \| 0.5234 \| 0.5234 \| 0.5234 \| \| 0.7054 \| 2.6901 \| 460 \| 0.7156 \| 0.5344 \| 0.5344 \| 0.5344 \| 0.5344 \| \| 0.7052 \| 2.7485 \| 470 \| 0.7143 \| 0.5437 \| 0.5437 \| 0.5437 \| 0.5437 \| \| 0.6915 \| 2.8070 \| 480 \| 0.6947 \| 0.5062 \| 0.5062 \| 0.5062 \| 0.5062 \| \| 0.679 \| 2.8655 \| 490 \| 0.7109 \| 0.5312 \| 0.5312 \| 0.5312 \| 0.5312 \| \| 0.6729 \| 2.9240 \| 500 \| 0.7442 \| 0.4938 \| 0.4938 \| 0.4938 \| 0.4938 \| \| 0.7035 \| 2.9825 \| 510 \| 0.7041 \| 0.5281 \| 0.5281 \| 0.5281 \| 0.5281 \| \| 0.7069 \| 3.0409 \| 520 \| 0.7023 \| 0.4766 \| 0.4766 \| 0.4766 \| 0.4766 \| \| 0.7089 \| 3.0994 \| 530 \| 0.6936 \| 0.5359 \| 0.5359 \| 0.5359 \| 0.5359 \| \| 0.6675 \| 3.1579 \| 540 \| 0.6931 \| 0.5188 \| 0.5188 \| 0.5188 \| 0.5188 \| \| 0.6202 \| 3.2164 \| 550 \| 0.8091 \| 0.4703 \| 0.4703 \| 0.4703 \| 0.4703 \| \| 0.6183 \| 3.2749 \| 560 \| 0.7316 \| 0.5406 \| 0.5406 \| 0.5406 \| 0.5406 \| \| 0.5781 \| 3.3333 \| 570 \| 0.7620 \| 0.5437 \| 0.5437 \| 0.5437 \| 0.5437 \| \| 0.6383 \| 3.3918 \| 580 \| 0.7552 \| 0.5219 \| 0.5219 \| 0.5219 \| 0.5219 \| \| 0.628 \| 3.4503 \| 590 \| 0.7266 \| 0.5437 \| 0.5437 \| 0.5437 \| 0.5437 \| \| 0.6198 \| 3.5088 \| 600 \| 0.7217 \| 0.5672 \| 0.5672 \| 0.5672 \| 0.5672 \| \| 0.6572 \| 3.5673 \| 610 \| 0.7962 \| 0.5047 \| 0.5047 \| 0.5047 \| 0.5047 \| \| 0.6119 \| 3.6257 \| 620 \| 0.7258 \| 0.5563 \| 0.5563 \| 0.5563 \| 0.5563 \| \| 0.6651 \| 3.6842 \| 630 \| 0.7445 \| 0.55 \| 0.55 \| 0.55 \| 0.55 \| \| 0.5399 \| 3.7427 \| 640 \| 0.8115 \| 0.5062 \| 0.5062 \| 0.5062 \| 0.5062 \| \| 0.6291 \| 3.8012 \| 650 \| 0.8045 \| 0.5312 \| 0.5312 \| 0.5312 \| 0.5312 \| ### Framework versions - Transformers 4.42.4 - Pytorch 2.3.1+cu121 - Tokenizers 0.19.1
ontocord/wide_3b_sft_stage1.1-ss1-no_redteam_skg_poem.no_issue	ontocord	2025-03-06T03:19:53Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-06T03:16:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
jssky/6cab1b5d-6330-48b7-a862-6acf6823a7c7	jssky	2025-03-06T03:19:32Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Capybara-7B-V1.9", "base_model:adapter:NousResearch/Nous-Capybara-7B-V1.9", "license:mit", "region:us" ]	null	2025-03-06T01:33:46Z	--- library_name: peft license: mit base_model: NousResearch/Nous-Capybara-7B-V1.9 tags: - axolotl - generated_from_trainer model-index: - name: 6cab1b5d-6330-48b7-a862-6acf6823a7c7 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: `0.6.0` ```yaml adapter: lora base_model: NousResearch/Nous-Capybara-7B-V1.9 bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 8705748c33de40f1_train_data.json ds_type: json format: custom path: /workspace/input_data/8705748c33de40f1_train_data.json type: field_instruction: text field_output: title 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: 200 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: jssky/6cab1b5d-6330-48b7-a862-6acf6823a7c7 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: 64 lora_dropout: 0.2 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 1000 micro_batch_size: 8 mlflow_experiment_name: /tmp/8705748c33de40f1_train_data.json model_type: AutoModelForCausalLM modules_to_save: lm_head 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: 200 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: offline wandb_name: 6ea17b8e-18a1-49d3-aa27-fab88b496936 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 6ea17b8e-18a1-49d3-aa27-fab88b496936 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 6cab1b5d-6330-48b7-a862-6acf6823a7c7 This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1.9](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0008 ## 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 adamw_bnb_8bit 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: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.2054 \| 200 \| 0.0026 \| \| 0.0 \| 0.4108 \| 400 \| 0.0025 \| \| 0.0 \| 0.6162 \| 600 \| 0.0001 \| \| 0.0046 \| 0.8216 \| 800 \| 0.0015 \| \| 0.0 \| 1.0270 \| 1000 \| 0.0008 \| ### Framework versions - PEFT 0.14.0 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
melpiece/HF3B_callme	melpiece	2025-03-06T03:18:18Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-06T03:17:57Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
Jonjew/AlysonHannigan	Jonjew	2025-03-06T03:17:46Z	0	0	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:unknown", "region:us" ]	text-to-image	2025-03-06T03:17:09Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: 4LYS0N output: url: images/ComfyUI_00016_.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: 4LYS0N license: unknown --- # Alyson Hannigan - Actress <Gallery /> ## Model description FROM https://civitai.com/models/1180460/alyson-hannigan-actress?modelVersionId=1328416 Trigger 4LYS0N Strength 1.2 Alyson Hannigan Denisof (@alysonhannigan) Alyson Hannigan, born on March 24, 1974, in Washington, D.C., is an American actress and television presenter best known for her iconic roles in Buffy the Vampire Slayer (as Willow Rosenberg) and How I Met Your Mother (as Lily Aldrin). She also gained fame in the American Pie film series as Michelle Flaherty. Alyson began her acting career in commercials as a child and transitioned to television and film roles in her teens, with her first major film appearance in My Stepmother Is an Alien (1988). Standing 5'4" (164 cm) tall and weighing approximately 132 lbs (60 kg), Alyson has hazel eyes, brown hair, and an approachable charm that enhances her performances. She is of Irish and Jewish descent. Alyson graduated from North Hollywood High School and later attended California State University, Northridge. In addition to acting, she has worked as a television host, presenting the show Penn & Teller: Fool Us. She married her Buffy the Vampire Slayer co-star Alexis Denisof in 2003, and the couple has two daughters, Satyana and Keeva. Alyson remains a beloved figure in the entertainment industry for her comedic timing and endearing portrayals. Recommended Strength is 1.0 to 1.2 (1.2 having better results): ## Trigger words You should use `4LYS0N` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/AlysonHannigan/tree/main) them in the Files & versions tab.
lqishuo/ppo-LunarLander-v2	lqishuo	2025-03-06T03:17:40Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-03-06T03:17:23Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 264.90 +/- 19.23 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Nerva1228/niuma1	Nerva1228	2025-03-06T03:16:34Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-03-06T03:16:33Z	--- 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: niuma1 --- # Niuma1 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `niuma1` 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('Nerva1228/niuma1', 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)
Abheben/bert-finetuned-ner	Abheben	2025-03-06T03:14:27Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-03-06T03:06:52Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9330246403175129 - name: Recall type: recall value: 0.9495119488387749 - name: F1 type: f1 value: 0.94119609642172 - name: Accuracy type: accuracy value: 0.9861511744275033 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0628 - Precision: 0.9330 - Recall: 0.9495 - F1: 0.9412 - Accuracy: 0.9862 ## 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 OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0732 \| 1.0 \| 1756 \| 0.0699 \| 0.9086 \| 0.9355 \| 0.9219 \| 0.9818 \| \| 0.0353 \| 2.0 \| 3512 \| 0.0654 \| 0.9363 \| 0.9492 \| 0.9427 \| 0.9861 \| \| 0.0222 \| 3.0 \| 5268 \| 0.0628 \| 0.9330 \| 0.9495 \| 0.9412 \| 0.9862 \| ### Framework versions - Transformers 4.47.1 - Pytorch 2.3.1+cu121 - Datasets 3.3.2 - Tokenizers 0.21.0
marcelovidigal/ModernBERT-base-2-contract-sections-classification-v4-10-max	marcelovidigal	2025-03-06T03:14:16Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "text-classification", "generated_from_trainer", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-03-04T19:42:17Z	--- library_name: transformers license: apache-2.0 base_model: answerdotai/ModernBERT-base tags: - generated_from_trainer model-index: - name: ModernBERT-base-2-contract-sections-classification-v4-10-max 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/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/mvgdr/classificacao-secoes-contratos-v4-modernbert-base/runs/20cn4u4r) # ModernBERT-base-2-contract-sections-classification-v4-10-max This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4801 - Accuracy Evaluate: 0.917 - Precision Evaluate: 0.9221 - Recall Evaluate: 0.9209 - F1 Evaluate: 0.9210 - Accuracy Sklearn: 0.917 - Precision Sklearn: 0.9173 - Recall Sklearn: 0.917 - F1 Sklearn: 0.9166 - Acuracia Rotulo Objeto: 0.9649 - Acuracia Rotulo Obrigacoes: 0.8838 - Acuracia Rotulo Valor: 0.8166 - Acuracia Rotulo Vigencia: 0.9816 - Acuracia Rotulo Rescisao: 0.9474 - Acuracia Rotulo Foro: 0.9385 - Acuracia Rotulo Reajuste: 0.9039 - Acuracia Rotulo Fiscalizacao: 0.8423 - Acuracia Rotulo Publicacao: 0.9951 - Acuracia Rotulo Pagamento: 0.8913 - Acuracia Rotulo Casos Omissos: 0.9015 - Acuracia Rotulo Sancoes: 0.9266 - Acuracia Rotulo Dotacao Orcamentaria: 0.9780 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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 Evaluate \| Precision Evaluate \| Recall Evaluate \| F1 Evaluate \| Accuracy Sklearn \| Precision Sklearn \| Recall Sklearn \| F1 Sklearn \| Acuracia Rotulo Objeto \| Acuracia Rotulo Obrigacoes \| Acuracia Rotulo Valor \| Acuracia Rotulo Vigencia \| Acuracia Rotulo Rescisao \| Acuracia Rotulo Foro \| Acuracia Rotulo Reajuste \| Acuracia Rotulo Fiscalizacao \| Acuracia Rotulo Publicacao \| Acuracia Rotulo Pagamento \| Acuracia Rotulo Casos Omissos \| Acuracia Rotulo Sancoes \| Acuracia Rotulo Dotacao Orcamentaria \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-----------------:\|:------------------:\|:---------------:\|:-----------:\|:----------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------------:\|:--------------------------:\|:---------------------:\|:------------------------:\|:------------------------:\|:--------------------:\|:------------------------:\|:----------------------------:\|:--------------------------:\|:-------------------------:\|:-----------------------------:\|:-----------------------:\|:------------------------------------:\| \| 2.3971 \| 1.0 \| 2000 \| 0.8286 \| 0.7678 \| 0.8292 \| 0.7831 \| 0.7914 \| 0.7678 \| 0.8077 \| 0.7678 \| 0.7695 \| 0.9504 \| 0.6734 \| 0.5244 \| 0.6115 \| 0.8975 \| 0.8808 \| 0.6512 \| 0.7382 \| 0.9113 \| 0.7754 \| 0.8227 \| 0.7982 \| 0.9451 \| \| 1.3917 \| 2.0 \| 4000 \| 0.6639 \| 0.8558 \| 0.8777 \| 0.8708 \| 0.8702 \| 0.8558 \| 0.8673 \| 0.8558 \| 0.8557 \| 0.9587 \| 0.7357 \| 0.6619 \| 0.8976 \| 0.9030 \| 0.9231 \| 0.8719 \| 0.7918 \| 0.9655 \| 0.8587 \| 0.8867 \| 0.8991 \| 0.9670 \| \| 0.887 \| 3.0 \| 6000 \| 0.5830 \| 0.874 \| 0.8789 \| 0.8885 \| 0.8804 \| 0.874 \| 0.8812 \| 0.874 \| 0.8743 \| 0.9442 \| 0.7761 \| 0.7421 \| 0.9081 \| 0.8615 \| 0.9346 \| 0.9004 \| 0.8265 \| 0.9852 \| 0.8841 \| 0.9015 \| 0.9083 \| 0.9780 \| \| 0.9516 \| 4.0 \| 8000 \| 0.5632 \| 0.8885 \| 0.8987 \| 0.8992 \| 0.8979 \| 0.8885 \| 0.8899 \| 0.8885 \| 0.8881 \| 0.9194 \| 0.8081 \| 0.7736 \| 0.9554 \| 0.9474 \| 0.9385 \| 0.8719 \| 0.8297 \| 0.9803 \| 0.8877 \| 0.8818 \| 0.9174 \| 0.9780 \| \| 0.7614 \| 5.0 \| 10000 \| 0.5290 \| 0.8998 \| 0.9083 \| 0.9102 \| 0.9084 \| 0.8998 \| 0.9021 \| 0.8998 \| 0.8998 \| 0.9628 \| 0.8064 \| 0.8052 \| 0.9370 \| 0.9418 \| 0.9846 \| 0.8897 \| 0.8328 \| 0.9951 \| 0.8804 \| 0.9015 \| 0.9174 \| 0.9780 \| \| 0.6291 \| 6.0 \| 12000 \| 0.5590 \| 0.8978 \| 0.9029 \| 0.9089 \| 0.9044 \| 0.8978 \| 0.9009 \| 0.8978 \| 0.8975 \| 0.9421 \| 0.7946 \| 0.7880 \| 0.9764 \| 0.9391 \| 0.9385 \| 0.9075 \| 0.8454 \| 0.9951 \| 0.8913 \| 0.9064 \| 0.9083 \| 0.9835 \| \| 0.4869 \| 7.0 \| 14000 \| 0.4875 \| 0.9103 \| 0.9140 \| 0.9156 \| 0.9143 \| 0.9103 \| 0.9102 \| 0.9103 \| 0.9096 \| 0.9587 \| 0.8737 \| 0.7966 \| 0.9790 \| 0.9446 \| 0.9346 \| 0.9039 \| 0.8139 \| 0.9951 \| 0.8913 \| 0.9064 \| 0.9266 \| 0.9780 \| \| 0.5691 \| 8.0 \| 16000 \| 0.4929 \| 0.9083 \| 0.9119 \| 0.9162 \| 0.9134 \| 0.9083 \| 0.9093 \| 0.9083 \| 0.9079 \| 0.9607 \| 0.8232 \| 0.8195 \| 0.9843 \| 0.9446 \| 0.9385 \| 0.8932 \| 0.8580 \| 0.9951 \| 0.8913 \| 0.9064 \| 0.9174 \| 0.9780 \| \| 0.3831 \| 9.0 \| 18000 \| 0.4892 \| 0.9153 \| 0.9184 \| 0.9198 \| 0.9186 \| 0.9153 \| 0.9157 \| 0.9153 \| 0.9149 \| 0.9649 \| 0.8737 \| 0.8138 \| 0.9843 \| 0.9446 \| 0.9385 \| 0.8968 \| 0.8486 \| 0.9951 \| 0.8913 \| 0.9015 \| 0.9266 \| 0.9780 \| \| 0.3 \| 10.0 \| 20000 \| 0.4801 \| 0.917 \| 0.9221 \| 0.9209 \| 0.9210 \| 0.917 \| 0.9173 \| 0.917 \| 0.9166 \| 0.9649 \| 0.8838 \| 0.8166 \| 0.9816 \| 0.9474 \| 0.9385 \| 0.9039 \| 0.8423 \| 0.9951 \| 0.8913 \| 0.9015 \| 0.9266 \| 0.9780 \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.6.0+cu124 - Datasets 3.3.0 - Tokenizers 0.21.0
texanrangee/d0b020ae-3e28-45ee-9bf5-722674d0619c	texanrangee	2025-03-06T03:12:58Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-06T02:47:45Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ontocord/wide_3b-merge_test	ontocord	2025-03-06T03:12:16Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-06T03:03:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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texanrangee/9c2d671b-1514-4a41-8981-d2e05f2411e1	texanrangee	2025-03-06T03:10:41Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-06T00:42:17Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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yenchik/mlx-gemma-2-2b-it-math	yenchik	2025-03-06T03:09:26Z	6	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "mlx", "base_model:google/gemma-2-2b-it", "base_model:quantized:google/gemma-2-2b-it", "license:gemma", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	2025-03-02T12:54:45Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license tags: - conversational - mlx base_model: google/gemma-2-2b-it --- # yenchik/mlx-gemma-2-2b-it-math The Model [yenchik/mlx-gemma-2-2b-it-math](https://huggingface.co/yenchik/mlx-gemma-2-2b-it-math) was converted to MLX format from [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) using mlx-lm version 0.21.5. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("yenchik/mlx-gemma-2-2b-it-math") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
tamtamx1332/AsenaMixTest	tamtamx1332	2025-03-06T03:09:25Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-03-06T03:08:09Z	--- license: apache-2.0 ---
whatsupmate/mizrahi3000_LoRA	whatsupmate	2025-03-06T03:07:06Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-03-06T03:05:30Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo of ytzhakov person widget: [] tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - whatsupmate/mizrahi3000_LoRA <Gallery /> ## Model description These are whatsupmate/mizrahi3000_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of ytzhakov person to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](whatsupmate/mizrahi3000_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
dskong07/charger-classif-model	dskong07	2025-03-06T03:02:22Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-03-06T02:48:07Z	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: charger-classif-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # charger-classif-model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2678 - Accuracy: 0.9231 ## 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: 2 - total_train_batch_size: 4 - 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: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.4057 \| 0.0769 \| 1 \| 0.5508 \| 0.6923 \| \| 0.5194 \| 0.1538 \| 2 \| 0.5735 \| 0.6923 \| \| 0.4141 \| 0.2308 \| 3 \| 0.5007 \| 0.7692 \| \| 0.5442 \| 0.3077 \| 4 \| 0.5160 \| 0.8462 \| \| 0.43 \| 0.3846 \| 5 \| 0.5931 \| 0.7692 \| \| 0.4126 \| 0.4615 \| 6 \| 0.5228 \| 0.7692 \| \| 0.4151 \| 0.5385 \| 7 \| 0.5552 \| 0.7692 \| \| 0.3753 \| 0.6154 \| 8 \| 0.5825 \| 0.6154 \| \| 0.3468 \| 0.6923 \| 9 \| 0.5637 \| 0.6923 \| \| 0.3467 \| 0.7692 \| 10 \| 0.5148 \| 0.6923 \| \| 0.5188 \| 0.8462 \| 11 \| 0.4735 \| 0.7692 \| \| 0.4342 \| 0.9231 \| 12 \| 0.5058 \| 0.7692 \| \| 0.3888 \| 1.0 \| 13 \| 0.5176 \| 0.6923 \| \| 0.3977 \| 1.0769 \| 14 \| 0.4865 \| 0.7692 \| \| 0.1799 \| 1.1538 \| 15 \| 0.5299 \| 0.6923 \| \| 0.4628 \| 1.2308 \| 16 \| 0.5614 \| 0.6923 \| \| 0.8787 \| 1.3077 \| 17 \| 0.5826 \| 0.6923 \| \| 0.3396 \| 1.3846 \| 18 \| 0.5337 \| 0.7692 \| \| 0.2144 \| 1.4615 \| 19 \| 0.5531 \| 0.6923 \| \| 0.242 \| 1.5385 \| 20 \| 0.5317 \| 0.6923 \| \| 1.1866 \| 1.6154 \| 21 \| 0.5042 \| 0.6923 \| \| 0.2689 \| 1.6923 \| 22 \| 0.4067 \| 0.8462 \| \| 0.3953 \| 1.7692 \| 23 \| 0.4513 \| 0.8462 \| \| 0.1978 \| 1.8462 \| 24 \| 0.5103 \| 0.6923 \| \| 0.3293 \| 1.9231 \| 25 \| 0.4829 \| 0.6923 \| \| 0.3324 \| 2.0 \| 26 \| 0.4915 \| 0.8462 \| \| 0.2096 \| 2.0769 \| 27 \| 0.5136 \| 0.8462 \| \| 0.4142 \| 2.1538 \| 28 \| 0.4490 \| 0.7692 \| \| 0.4267 \| 2.2308 \| 29 \| 0.4697 \| 0.7692 \| \| 0.1871 \| 2.3077 \| 30 \| 0.4744 \| 0.7692 \| \| 0.3145 \| 2.3846 \| 31 \| 0.5596 \| 0.6923 \| \| 0.3417 \| 2.4615 \| 32 \| 0.4589 \| 0.6923 \| \| 0.1548 \| 2.5385 \| 33 \| 0.5245 \| 0.6923 \| \| 0.3131 \| 2.6154 \| 34 \| 0.4507 \| 0.6923 \| \| 0.1974 \| 2.6923 \| 35 \| 0.4068 \| 0.8462 \| \| 0.3148 \| 2.7692 \| 36 \| 0.5019 \| 0.6923 \| \| 0.5036 \| 2.8462 \| 37 \| 0.4761 \| 0.6923 \| \| 0.2178 \| 2.9231 \| 38 \| 0.4132 \| 0.9231 \| \| 0.4536 \| 3.0 \| 39 \| 0.4745 \| 0.7692 \| \| 0.3118 \| 3.0769 \| 40 \| 0.4869 \| 0.7692 \| \| 0.3465 \| 3.1538 \| 41 \| 0.4473 \| 0.7692 \| \| 0.096 \| 3.2308 \| 42 \| 0.4376 \| 0.8462 \| \| 0.1726 \| 3.3077 \| 43 \| 0.5971 \| 0.7692 \| \| 0.1685 \| 3.3846 \| 44 \| 0.4768 \| 0.7692 \| \| 0.2046 \| 3.4615 \| 45 \| 0.3595 \| 0.8462 \| \| 0.1297 \| 3.5385 \| 46 \| 0.4701 \| 0.7692 \| \| 0.4597 \| 3.6154 \| 47 \| 0.4054 \| 0.7692 \| \| 0.3474 \| 3.6923 \| 48 \| 0.3927 \| 0.8462 \| \| 0.4476 \| 3.7692 \| 49 \| 0.5063 \| 0.8462 \| \| 0.1062 \| 3.8462 \| 50 \| 0.4741 \| 0.7692 \| \| 0.5484 \| 3.9231 \| 51 \| 0.4950 \| 0.6923 \| \| 0.0945 \| 4.0 \| 52 \| 0.4647 \| 0.7692 \| \| 0.1053 \| 4.0769 \| 53 \| 0.3743 \| 0.8462 \| \| 0.4122 \| 4.1538 \| 54 \| 0.4350 \| 0.8462 \| \| 0.2825 \| 4.2308 \| 55 \| 0.4246 \| 0.8462 \| \| 0.2912 \| 4.3077 \| 56 \| 0.5250 \| 0.6923 \| \| 0.3193 \| 4.3846 \| 57 \| 0.3639 \| 0.8462 \| \| 0.066 \| 4.4615 \| 58 \| 0.3574 \| 0.9231 \| \| 0.0888 \| 4.5385 \| 59 \| 0.4897 \| 0.6923 \| \| 0.1046 \| 4.6154 \| 60 \| 0.3032 \| 0.9231 \| \| 0.2573 \| 4.6923 \| 61 \| 0.5662 \| 0.6154 \| \| 0.368 \| 4.7692 \| 62 \| 0.3699 \| 0.8462 \| \| 0.1484 \| 4.8462 \| 63 \| 0.3517 \| 0.8462 \| \| 0.1444 \| 4.9231 \| 64 \| 0.2988 \| 0.9231 \| \| 0.1492 \| 5.0 \| 65 \| 0.3523 \| 0.8462 \| \| 0.112 \| 5.0769 \| 66 \| 0.4245 \| 0.8462 \| \| 0.0711 \| 5.1538 \| 67 \| 0.4451 \| 0.6923 \| \| 0.2455 \| 5.2308 \| 68 \| 0.4774 \| 0.7692 \| \| 0.3981 \| 5.3077 \| 69 \| 0.5084 \| 0.7692 \| \| 0.1682 \| 5.3846 \| 70 \| 0.4053 \| 0.8462 \| \| 0.2809 \| 5.4615 \| 71 \| 0.4574 \| 0.6923 \| \| 0.1929 \| 5.5385 \| 72 \| 0.3242 \| 0.7692 \| \| 0.161 \| 5.6154 \| 73 \| 0.3854 \| 0.7692 \| \| 0.1475 \| 5.6923 \| 74 \| 0.3935 \| 0.7692 \| \| 0.1058 \| 5.7692 \| 75 \| 0.5751 \| 0.6923 \| \| 0.1103 \| 5.8462 \| 76 \| 0.3874 \| 0.8462 \| \| 0.1057 \| 5.9231 \| 77 \| 0.3984 \| 0.7692 \| \| 0.1593 \| 6.0 \| 78 \| 0.3299 \| 0.8462 \| \| 0.1154 \| 6.0769 \| 79 \| 0.4778 \| 0.7692 \| \| 0.3131 \| 6.1538 \| 80 \| 0.4863 \| 0.7692 \| \| 0.0791 \| 6.2308 \| 81 \| 0.4897 \| 0.7692 \| \| 0.0635 \| 6.3077 \| 82 \| 0.5831 \| 0.7692 \| \| 0.0704 \| 6.3846 \| 83 \| 0.4384 \| 0.8462 \| \| 0.0597 \| 6.4615 \| 84 \| 0.5519 \| 0.7692 \| \| 0.1117 \| 6.5385 \| 85 \| 0.4525 \| 0.7692 \| \| 0.1542 \| 6.6154 \| 86 \| 0.5354 \| 0.8462 \| \| 0.5737 \| 6.6923 \| 87 \| 0.5034 \| 0.7692 \| \| 0.4216 \| 6.7692 \| 88 \| 0.4514 \| 0.7692 \| \| 0.3276 \| 6.8462 \| 89 \| 0.5688 \| 0.7692 \| \| 0.119 \| 6.9231 \| 90 \| 0.3433 \| 0.9231 \| \| 0.1519 \| 7.0 \| 91 \| 0.4454 \| 0.7692 \| \| 0.1155 \| 7.0769 \| 92 \| 0.3323 \| 0.7692 \| \| 0.1264 \| 7.1538 \| 93 \| 0.4030 \| 0.6923 \| \| 0.0585 \| 7.2308 \| 94 \| 0.3404 \| 0.8462 \| \| 0.1404 \| 7.3077 \| 95 \| 0.3507 \| 0.8462 \| \| 0.0417 \| 7.3846 \| 96 \| 0.4860 \| 0.7692 \| \| 0.0873 \| 7.4615 \| 97 \| 0.4896 \| 0.8462 \| \| 0.0801 \| 7.5385 \| 98 \| 0.4383 \| 0.7692 \| \| 0.2163 \| 7.6154 \| 99 \| 0.3764 \| 0.8462 \| \| 0.1823 \| 7.6923 \| 100 \| 0.4258 \| 0.8462 \| \| 0.1832 \| 7.7692 \| 101 \| 0.2890 \| 0.8462 \| \| 0.0879 \| 7.8462 \| 102 \| 0.2909 \| 0.8462 \| \| 0.2345 \| 7.9231 \| 103 \| 0.3617 \| 0.8462 \| \| 0.1096 \| 8.0 \| 104 \| 0.2678 \| 0.9231 \| ### Framework versions - Transformers 4.48.3 - Pytorch 2.6.0+cpu - Datasets 3.2.0 - Tokenizers 0.21.0
ben832/mflux	ben832	2025-03-06T03:00:27Z	0	0	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:mit", "region:us" ]	text-to-image	2025-03-05T21:40:09Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: flux output: url: images/download.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: mit --- # mflux <Gallery /> ## Download model Weights for this model are available in Safetensors,PyTorch format. [Download](/ben832/mflux/tree/main) them in the Files & versions tab.
ClarenceDan/bd3d3649-4d88-4050-917a-8dfa58d476c0	ClarenceDan	2025-03-06T02:59:09Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM2-1.7B-Instruct", "base_model:adapter:unsloth/SmolLM2-1.7B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-03-06T02:48:36Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM2-1.7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: bd3d3649-4d88-4050-917a-8dfa58d476c0 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/SmolLM2-1.7B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 64ada857f8032e58_train_data.json ds_type: json format: custom path: /workspace/input_data/64ada857f8032e58_train_data.json type: field_input: description field_instruction: input persona field_output: synthesized text 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/bd3d3649-4d88-4050-917a-8dfa58d476c0 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/64ada857f8032e58_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: d48c1d93-6c93-4c81-b9c1-af40841a4fb7 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: d48c1d93-6c93-4c81-b9c1-af40841a4fb7 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # bd3d3649-4d88-4050-917a-8dfa58d476c0 This model is a fine-tuned version of [unsloth/SmolLM2-1.7B-Instruct](https://huggingface.co/unsloth/SmolLM2-1.7B-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 \| 0.0002 \| 1 \| nan \| \| 0.0 \| 0.0005 \| 3 \| nan \| \| 0.0 \| 0.0010 \| 6 \| nan \| \| 0.0 \| 0.0015 \| 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
Alphatao/test_5	Alphatao	2025-03-06T02:57:00Z	0	0	peft	[ "peft", "safetensors", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-0.5B", "base_model:adapter:Qwen/Qwen2-0.5B", "license:apache-2.0", "region:us" ]	null	2025-03-06T02:54:55Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-0.5B tags: - axolotl - generated_from_trainer model-index: - name: test_5 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 base_model: "Qwen/Qwen2-0.5B" model_type: "AutoModelForCausalLM" tokenizer_type: "AutoTokenizer" load_in_8bit: false load_in_4bit: true strict: false chat_template: "llama3" datasets: - path: "/workspace/input_data/train_data.json" format: "custom" type: system_prompt: "" system_format: "{system}" field_instruction: "prompt" field_output: "question" no_input_format: "{instruction}" format: "{instruction}" ds_type: "json" data_files: - "train_data.json" dataset_prepared_path: null val_set_size: 0.04 output_dir: "miner_id_24" sequence_len: 1024 sample_packing: false pad_to_sequence_len: true trust_remote_code: true adapter: "lora" lora_model_dir: null lora_r: 64 lora_alpha: 128 lora_dropout: 0.3 lora_target_linear: true lora_fan_in_fan_out: null gradient_accumulation_steps: 6 micro_batch_size: 4 optimizer: "adamw_bnb_8bit" lr_scheduler: "cosine" learning_rate: 0.0002 num_epochs: 3 max_steps: 2 train_on_inputs: false group_by_length: false bf16: true fp16: null tf32: true max_grad_norm: 1.0 gradient_checkpointing: true early_stopping_patience: 4 save_steps: 100 eval_steps: 100 resume_from_checkpoint: null local_rank: null logging_steps: 1 xformers_attention: null flash_attention: true s2_attention: null load_best_model_at_end: true wandb_project: "Gradients-On-Demand" wandb_entity: null wandb_mode: "online" wandb_run: "your_name" wandb_runid: "c29f8be0-1d6a-40dd-83f1-f1d58697725a" hub_model_id: "Alphatao/test_5" hub_repo: null hub_strategy: "end" hub_token: null warmup_steps: 10 eval_table_size: null eval_max_new_tokens: 128 debug: null deepspeed: null weight_decay: 0.0 fsdp: null fsdp_config: null wandb_name: "c29f8be0-1d6a-40dd-83f1-f1d58697725a" lora_target_modules: ["q_proj", "k_proj", "v_proj"] mlflow_experiment_name: "/tmp/train_data.json" ``` </details><br> # test_5 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: 0.4452 ## 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: 6 - total_train_batch_size: 24 - 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.26 \| 0.0006 \| 1 \| 0.4452 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
AImused/omeg31	AImused	2025-03-06T02:56:17Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-03-06T02:49:51Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
KingEmpire/Leuven_7	KingEmpire	2025-03-06T02:55:19Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-03-06T02:37:23Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
triplee/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit_2025-03-05	triplee	2025-03-06T02:55:05Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "base_model:finetune:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-03-06T02:52:58Z	--- base_model: unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: triplee - License: apache-2.0 - Finetuned from model : unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
nomnoos37/250305-Mistral-Nemo-ggls-v1.3.9-1-epoch	nomnoos37	2025-03-06T02:55:02Z	0	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "base_model:quantized:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-06T02:50:57Z	--- base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: nomnoos37 - License: apache-2.0 - Finetuned from model : unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
KingEmpire/Leuven_3	KingEmpire	2025-03-06T02:54:46Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-03-06T02:37:22Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
KingEmpire/Leuven_6	KingEmpire	2025-03-06T02:54:41Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-03-06T02:37:23Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
KingEmpire/Leuven_5	KingEmpire	2025-03-06T02:54:28Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-03-06T02:37:23Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
KingEmpire/Leuven_1	KingEmpire	2025-03-06T02:54:25Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-03-06T02:37:21Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Alphatao/test3	Alphatao	2025-03-06T02:53:16Z	0	0	peft	[ "peft", "safetensors", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-0.5B", "base_model:adapter:Qwen/Qwen2-0.5B", "license:apache-2.0", "region:us" ]	null	2025-03-06T01:54:20Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-0.5B tags: - axolotl - generated_from_trainer model-index: - name: test3 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 base_model: "Qwen/Qwen2-0.5B" model_type: "AutoModelForCausalLM" tokenizer_type: "AutoTokenizer" load_in_8bit: false load_in_4bit: true strict: false chat_template: "llama3" datasets: - path: "/workspace/input_data/train_data.json" format: "custom" type: system_prompt: "" system_format: "{system}" field_instruction: "prompt" field_output: "question" no_input_format: "{instruction}" format: "{instruction}" ds_type: "json" data_files: - "train_data.json" dataset_prepared_path: null val_set_size: 0.04 output_dir: "miner_id_24" sequence_len: 1024 sample_packing: false pad_to_sequence_len: true trust_remote_code: true adapter: "lora" lora_model_dir: null lora_r: 64 lora_alpha: 128 lora_dropout: 0.3 lora_target_linear: true lora_fan_in_fan_out: null gradient_accumulation_steps: 6 micro_batch_size: 4 optimizer: "adamw_bnb_8bit" lr_scheduler: "cosine" learning_rate: 0.0002 num_epochs: 3 max_steps: 2 train_on_inputs: false group_by_length: false bf16: true fp16: null tf32: true max_grad_norm: 1.0 gradient_checkpointing: true early_stopping_patience: 4 save_steps: 100 eval_steps: 100 resume_from_checkpoint: null local_rank: null logging_steps: 1 xformers_attention: null flash_attention: true s2_attention: null load_best_model_at_end: true wandb_project: "Gradients-On-Demand" wandb_entity: null wandb_mode: "online" wandb_run: "your_name" wandb_runid: "c29f8be0-1d6a-40dd-83f1-f1d58697725a" hub_model_id: "Alphatao/test3" hub_repo: null hub_strategy: "end" hub_token: null warmup_steps: 10 eval_table_size: null eval_max_new_tokens: 128 debug: null deepspeed: null weight_decay: 0.0 fsdp: null fsdp_config: null wandb_name: "c29f8be0-1d6a-40dd-83f1-f1d58697725a" lora_target_modules: ["q_proj", "k_proj", "v_proj"] mlflow_experiment_name: "/tmp/train_data.json" ``` </details><br> # test3 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: 0.4452 ## 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: 6 - total_train_batch_size: 24 - 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.26 \| 0.0006 \| 1 \| 0.4452 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
bonamt11/Llama-3.2-1B-Instruct-bnb-4bit-Classification-model	bonamt11	2025-03-06T02:52:56Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-06T02:50:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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texanrangee/dd7e0664-4477-421e-ae3e-507f51ed6fb8	texanrangee	2025-03-06T02:51:56Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-03-05T23:21:46Z	--- 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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kenken6696/Llama-3.2-1B_3_mix_position_famous_unrecognized	kenken6696	2025-03-06T02:47:29Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-06T02:45:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Luongdzung/bloomVN-0.5B-ppo-sft-order1-mat-phy-che-bio-lit-his-geo-olora	Luongdzung	2025-03-06T02:46:51Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:Luongdzung/bloomVN-0.5B-ppo-sft-order1-mat-phy-che-bio-lit-his-olora-ALL-WEIGHT", "base_model:adapter:Luongdzung/bloomVN-0.5B-ppo-sft-order1-mat-phy-che-bio-lit-his-olora-ALL-WEIGHT", "region:us" ]	null	2025-03-06T02:46:48Z	--- library_name: peft base_model: Luongdzung/bloomVN-0.5B-ppo-sft-order1-mat-phy-che-bio-lit-his-olora-ALL-WEIGHT tags: - generated_from_trainer model-index: - name: bloomVN-0.5B-ppo-sft-order1-mat-phy-che-bio-lit-his-geo-olora 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. --> # bloomVN-0.5B-ppo-sft-order1-mat-phy-che-bio-lit-his-geo-olora This model is a fine-tuned version of [Luongdzung/bloomVN-0.5B-ppo-sft-order1-mat-phy-che-bio-lit-his-olora-ALL-WEIGHT](https://huggingface.co/Luongdzung/bloomVN-0.5B-ppo-sft-order1-mat-phy-che-bio-lit-his-olora-ALL-WEIGHT) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 ### Training results ### Framework versions - PEFT 0.14.0 - Transformers 4.47.0 - Pytorch 2.4.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0
ontocord/wide_3b_sft_stage1.2-ss1-expert_formatted_text	ontocord	2025-03-06T02:43:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-06T01:55:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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SpongeEngine/BlackSheep-Qwen-14B-i1-GGUF	SpongeEngine	2025-03-06T02:39:07Z	0	0	null	[ "gguf", "SpongeQuant", "i1-GGUF", "en", "base_model:TroyDoesAI/BlackSheep-Qwen-14B", "base_model:quantized:TroyDoesAI/BlackSheep-Qwen-14B", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-03-05T21:24:19Z	--- base_model: TroyDoesAI/BlackSheep-Qwen-14B language: - en license: mit quantized_by: SpongeQuant tags: - SpongeQuant - i1-GGUF --- Quantized to `i1-GGUF` using [SpongeQuant](https://github.com/SpongeEngine/SpongeQuant), the Oobabooga of LLM quantization. <div style="display: flex; gap: 20px; align-items: center; margin-top:0;"> <a href="https://github.com/SpongeEngine/SpongeQuant"> <img src="https://huggingface.co/spaces/SpongeEngine/README/resolve/main/github-button.png" width="173"> </a> <a href="https://discord.gg/azNmr2Gdgy"> <img src="https://huggingface.co/spaces/SpongeEngine/README/resolve/main/discord-button.png" width="173"> </a> </div> * <figure> <img src="https://huggingface.co/spaces/SpongeEngine/README/resolve/main/094.png" alt="UN Building Night"> <figcaption>UN Building Night</figcaption> </figure> <figure> <audio controls> <source src="https://huggingface.co/spaces/SpongeEngine/README/resolve/main/011.mp3" type="audio/mp3"> Your browser does not support the audio element. </audio> <figcaption>Chuck Berry – Johnny B. Goode (USA, 1958)</figcaption> </figure> * ### What is a GGUF? GGUF is a file format used for running large language models (LLMs) on different types of computers. It supports both regular processors (CPUs) and graphics cards (GPUs), making it easier to run models across a wide range of hardware. Many LLMs require powerful and expensive GPUs, but GGUF improves compatibility and efficiency by optimizing how models are loaded and executed. If a GPU doesn't have enough memory, GGUF can offload parts of the model to the CPU, allowing it to run even when GPU resources are limited. GGUF is designed to work well with quantized models, which use less memory and run faster, making them ideal for lower-end hardware. However, it can also store full-precision models when needed. Thanks to these optimizations, GGUF allows LLMs to run efficiently on everything from high-end GPUs to laptops and even CPU-only systems. ### What is an i1-GGUF? i1-GGUF is an enhanced type of GGUF model that uses imatrix quantization—a smarter way of reducing model size while preserving key details. Instead of shrinking everything equally, it analyzes the importance of different model components and keeps the most crucial parts more accurate. Like standard GGUF, i1-GGUF allows LLMs to run on various hardware, including CPUs and lower-end GPUs. However, because it prioritizes important weights, i1-GGUF models deliver better responses than traditional GGUF models while maintaining efficiency.
KettaP/fine-tuned-paligemma-model-for-detection	KettaP	2025-03-06T02:37:58Z	0	0	transformers	[ "transformers", "safetensors", "paligemma", "image-text-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-03-06T02:35:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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BeebekBhz/en-ne	BeebekBhz	2025-03-06T02:34:10Z	25	0	transformers	[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "base_model:Helsinki-NLP/opus-mt-en-hi", "base_model:finetune:Helsinki-NLP/opus-mt-en-hi", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-03-04T09:41:32Z	--- library_name: transformers license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-hi tags: - generated_from_keras_callback model-index: - name: en-ne results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # en-ne This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-hi](https://huggingface.co/Helsinki-NLP/opus-mt-en-hi) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.3328 - Validation Loss: 1.5832 - Epoch: 5 ## Model description More information needed ## Intended uses & limitations More information needed ### BLEU Score The model achieves a BLEU score of 14.83 on the `test` split of the `BeebekBhz/en-ne` dataset. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 2.6327 \| 2.0059 \| 0 \| \| 1.9273 \| 1.7883 \| 1 \| \| 1.6957 \| 1.6886 \| 2 \| \| 1.5456 \| 1.6348 \| 3 \| \| 1.4287 \| 1.6065 \| 4 \| \| 1.3328 \| 1.5832 \| 5 \| ### Framework versions - Transformers 4.47.1 - TensorFlow 2.17.1 - Datasets 3.3.2 - Tokenizers 0.21.0
Ayen0/stella1d3_3	Ayen0	2025-03-06T02:31:26Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-03-06T02:12:47Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Stella1D3_3 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` 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('Ayen0/stella1d3_3', 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)
FriendliAI/internlm3-8b-instruct	FriendliAI	2025-03-06T02:30:35Z	0	0	null	[ "safetensors", "internlm3", "text-generation", "conversational", "custom_code", "arxiv:2403.17297", "license:apache-2.0", "region:us" ]	text-generation	2025-03-06T02:30:34Z	--- license: apache-2.0 pipeline_tag: text-generation --- # InternLM <div align="center"> <img src="https://github.com/InternLM/InternLM/assets/22529082/b9788105-8892-4398-8b47-b513a292378e" width="200"/> <div> </div> <div align="center"> <b><font size="5">InternLM</font></b> <sup> <a href="https://internlm.intern-ai.org.cn/"> <i><font size="4">HOT</font></i> </a> </sup> <div> </div> </div> [![evaluation](https://github.com/InternLM/InternLM/assets/22529082/f80a2a58-5ddf-471a-8da4-32ab65c8fd3b)](https://github.com/internLM/OpenCompass/) [💻Github Repo](https://github.com/InternLM/InternLM) • [🤗Demo](https://huggingface.co/spaces/internlm/internlm3-8b-instruct) • [🤔Reporting Issues](https://github.com/InternLM/InternLM/issues/new) • [📜Technical Report](https://arxiv.org/abs/2403.17297) </div> <p align="center"> 👋 join us on <a href="https://discord.gg/xa29JuW87d" target="_blank">Discord</a> and <a href="https://github.com/InternLM/InternLM/assets/25839884/a6aad896-7232-4220-ac84-9e070c2633ce" target="_blank">WeChat</a> </p> ## Introduction InternLM3 has open-sourced an 8-billion parameter instruction model, InternLM3-8B-Instruct, designed for general-purpose usage and advanced reasoning. This model has the following characteristics: - Enhanced performance at reduced cost: State-of-the-art performance on reasoning and knowledge-intensive tasks surpass models like Llama3.1-8B and Qwen2.5-7B. Remarkably, InternLM3 is trained on only 4 trillion high-quality tokens, saving more than 75% of the training cost compared to other LLMs of similar scale. - Deep thinking capability: InternLM3 supports both the deep thinking mode for solving complicated reasoning tasks via the long chain-of-thought and the normal response mode for fluent user interactions. ## InternLM3-8B-Instruct ### Performance Evaluation We conducted a comprehensive evaluation of InternLM using the open-source evaluation tool [OpenCompass](https://github.com/internLM/OpenCompass/). The evaluation covered five dimensions of capabilities: disciplinary competence, language competence, knowledge competence, inference competence, and comprehension competence. Here are some of the evaluation results, and you can visit the [OpenCompass leaderboard](https://rank.opencompass.org.cn) for more evaluation results. \| \| Benchmark \| InternLM3-8B-Instruct \| Qwen2.5-7B-Instruct \| Llama3.1-8B-Instruct \| GPT-4o-mini(closed source) \| \| ------------ \| ------------------------------- \| --------------------- \| ------------------- \| -------------------- \| -------------------------- \| \| General \| CMMLU(0-shot) \| 83.1 \| 75.8 \| 53.9 \| 66.0 \| \| \| MMLU(0-shot) \| 76.6 \| 76.8 \| 71.8 \| 82.7 \| \| \| MMLU-Pro(0-shot) \| 57.6 \| 56.2 \| 48.1 \| 64.1 \| \| Reasoning \| GPQA-Diamond(0-shot) \| 37.4 \| 33.3 \| 24.2 \| 42.9 \| \| \| DROP(0-shot) \| 83.1 \| 80.4 \| 81.6 \| 85.2 \| \| \| HellaSwag(10-shot) \| 91.2 \| 85.3 \| 76.7 \| 89.5 \| \| \| KOR-Bench(0-shot) \| 56.4 \| 44.6 \| 47.7 \| 58.2 \| \| MATH \| MATH-500(0-shot) \| 83.0* \| 72.4 \| 48.4 \| 74.0 \| \| \| AIME2024(0-shot) \| 20.0* \| 16.7 \| 6.7 \| 13.3 \| \| Coding \| LiveCodeBench(2407-2409 Pass@1) \| 17.8 \| 16.8 \| 12.9 \| 21.8 \| \| \| HumanEval(Pass@1) \| 82.3 \| 85.4 \| 72.0 \| 86.6 \| \| Instrunction \| IFEval(Prompt-Strict) \| 79.3 \| 71.7 \| 75.2 \| 79.7 \| \| Long Context \| RULER(4-128K Average) \| 87.9 \| 81.4 \| 88.5 \| 90.7 \| \| Chat \| AlpacaEval 2.0(LC WinRate) \| 51.1 \| 30.3 \| 25.0 \| 50.7 \| \| \| WildBench(Raw Score) \| 33.1 \| 23.3 \| 1.5 \| 40.3 \| \| \| MT-Bench-101(Score 1-10) \| 8.59 \| 8.49 \| 8.37 \| 8.87 \| - Values marked in bold indicate the highest in open source models - The evaluation results were obtained from [OpenCompass](https://github.com/internLM/OpenCompass/) (some data marked with , which means evaluating with Thinking Mode), and evaluation configuration can be found in the configuration files provided by [OpenCompass](https://github.com/internLM/OpenCompass/). - The evaluation data may have numerical differences due to the version iteration of [OpenCompass](https://github.com/internLM/OpenCompass/), so please refer to the latest evaluation results of [OpenCompass](https://github.com/internLM/OpenCompass/). Limitations:* Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information. ### Requirements ```python transformers >= 4.48 ``` ### Conversation Mode #### Transformers inference To load the InternLM3 8B Instruct model using Transformers, use the following code: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_dir = "internlm/internlm3-8b-instruct" tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True) # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error. model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() # (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes. # InternLM3 8B in 4bit will cost nearly 8GB GPU memory. # pip install -U bitsandbytes # 8-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_8bit=True) # 4-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_4bit=True) model = model.eval() system_prompt = """You are an AI assistant whose name is InternLM (书生·浦语). - InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless. - InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.""" messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": "Please tell me five scenic spots in Shanghai"}, ] tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda") generated_ids = model.generate(tokenized_chat, max_new_tokens=1024, temperature=1, repetition_penalty=1.005, top_k=40, top_p=0.8) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(tokenized_chat, generated_ids) ] prompt = tokenizer.batch_decode(tokenized_chat)[0] print(prompt) response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` #### LMDeploy inference LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams. ```bash pip install lmdeploy ``` You can run batch inference locally with the following python code: ```python import lmdeploy model_dir = "internlm/internlm3-8b-instruct" pipe = lmdeploy.pipeline(model_dir) response = pipe("Please tell me five scenic spots in Shanghai") print(response) ``` Or you can launch an OpenAI compatible server with the following command: ```bash lmdeploy serve api_server internlm/internlm3-8b-instruct --model-name internlm3-8b-instruct --server-port 23333 ``` Then you can send a chat request to the server: ```bash curl http://localhost:23333/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "internlm3-8b-instruct", "messages": [ {"role": "user", "content": "Please tell me five scenic spots in Shanghai"} ] }' ``` Find more details in the [LMDeploy documentation](https://lmdeploy.readthedocs.io/en/latest/) #### Ollama inference First install ollama, ```python # install ollama curl -fsSL https://ollama.com/install.sh \| sh # fetch model ollama pull internlm/internlm3-8b-instruct # install pip install ollama ``` inference code, ```python import ollama system_prompt = """You are an AI assistant whose name is InternLM (书生·浦语). - InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless. - InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.""" messages = [ { "role": "system", "content": system_prompt, }, { "role": "user", "content": "Please tell me five scenic spots in Shanghai" }, ] stream = ollama.chat( model='internlm/internlm3-8b-instruct', messages=messages, stream=True, ) for chunk in stream: print(chunk['message']['content'], end='', flush=True) ``` #### vLLM inference Refer to [installation](https://docs.vllm.ai/en/latest/getting_started/installation/index.html) to install the latest code of vllm ```python pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly ``` inference code: ```python from vllm import LLM, SamplingParams llm = LLM(model="internlm/internlm3-8b-instruct") sampling_params = SamplingParams(temperature=1, repetition_penalty=1.005, top_k=40, top_p=0.8) system_prompt = """You are an AI assistant whose name is InternLM (书生·浦语). - InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless. - InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.""" prompts = [ { "role": "system", "content": system_prompt, }, { "role": "user", "content": "Please tell me five scenic spots in Shanghai" }, ] outputs = llm.chat(prompts, sampling_params=sampling_params, use_tqdm=False) print(outputs) ``` ### Thinking Mode #### Thinking Demo <img src="https://github.com/InternLM/InternLM/blob/017ba7446d20ecc3b9ab8e7b66cc034500868ab4/assets/solve_puzzle.png?raw=true" width="400"/> #### Thinking system prompt ```python thinking_system_prompt = """You are an expert mathematician with extensive experience in mathematical competitions. You approach problems through systematic thinking and rigorous reasoning. When solving problems, follow these thought processes: ## Deep Understanding Take time to fully comprehend the problem before attempting a solution. Consider: - What is the real question being asked? - What are the given conditions and what do they tell us? - Are there any special restrictions or assumptions? - Which information is crucial and which is supplementary? ## Multi-angle Analysis Before solving, conduct thorough analysis: - What mathematical concepts and properties are involved? - Can you recall similar classic problems or solution methods? - Would diagrams or tables help visualize the problem? - Are there special cases that need separate consideration? ## Systematic Thinking Plan your solution path: - Propose multiple possible approaches - Analyze the feasibility and merits of each method - Choose the most appropriate method and explain why - Break complex problems into smaller, manageable steps ## Rigorous Proof During the solution process: - Provide solid justification for each step - Include detailed proofs for key conclusions - Pay attention to logical connections - Be vigilant about potential oversights ## Repeated Verification After completing your solution: - Verify your results satisfy all conditions - Check for overlooked special cases - Consider if the solution can be optimized or simplified - Review your reasoning process Remember: 1. Take time to think thoroughly rather than rushing to an answer 2. Rigorously prove each key conclusion 3. Keep an open mind and try different approaches 4. Summarize valuable problem-solving methods 5. Maintain healthy skepticism and verify multiple times Your response should reflect deep mathematical understanding and precise logical thinking, making your solution path and reasoning clear to others. When you're ready, present your complete solution with: - Clear problem understanding - Detailed solution process - Key insights - Thorough verification Focus on clear, logical progression of ideas and thorough explanation of your mathematical reasoning. Provide answers in the same language as the user asking the question, repeat the final answer using a '\\boxed{}' without any units, you have [[8192]] tokens to complete the answer. """ ``` #### Transformers inference ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_dir = "internlm/internlm3-8b-instruct" tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True) # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error. model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() # (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes. # InternLM3 8B in 4bit will cost nearly 8GB GPU memory. # pip install -U bitsandbytes # 8-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_8bit=True) # 4-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_4bit=True) model = model.eval() messages = [ {"role": "system", "content": thinking_system_prompt}, {"role": "user", "content": "Given the function\(f(x)=\mathrm{e}^{x}-ax - a^{3}\),\n(1) When \(a = 1\), find the equation of the tangent line to the curve \(y = f(x)\) at the point \((1,f(1))\).\n(2) If \(f(x)\) has a local minimum and the minimum value is less than \(0\), determine the range of values for \(a\)."}, ] tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda") generated_ids = model.generate(tokenized_chat, max_new_tokens=8192) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(tokenized_chat, generated_ids) ] prompt = tokenizer.batch_decode(tokenized_chat)[0] print(prompt) response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` #### LMDeploy inference LMDeploy is a toolkit for compressing, deploying, and serving LLM. ```bash pip install lmdeploy ``` You can run batch inference locally with the following python code: ```python from lmdeploy import pipeline, GenerationConfig, ChatTemplateConfig model_dir = "internlm/internlm3-8b-instruct" chat_template_config = ChatTemplateConfig(model_name='internlm3') pipe = pipeline(model_dir, chat_template_config=chat_template_config) messages = [ {"role": "system", "content": thinking_system_prompt}, {"role": "user", "content": "Given the function\(f(x)=\mathrm{e}^{x}-ax - a^{3}\),\n(1) When \(a = 1\), find the equation of the tangent line to the curve \(y = f(x)\) at the point \((1,f(1))\).\n(2) If \(f(x)\) has a local minimum and the minimum value is less than \(0\), determine the range of values for \(a\)."}, ] response = pipe(messages, gen_config=GenerationConfig(max_new_tokens=2048)) print(response) ``` #### Ollama inference First install ollama, ```python # install ollama curl -fsSL https://ollama.com/install.sh \| sh # fetch model ollama pull internlm/internlm3-8b-instruct # install pip install ollama ``` inference code, ```python import ollama messages = [ { "role": "system", "content": thinking_system_prompt, }, { "role": "user", "content": "Given the function\(f(x)=\mathrm{e}^{x}-ax - a^{3}\),\n(1) When \(a = 1\), find the equation of the tangent line to the curve \(y = f(x)\) at the point \((1,f(1))\).\n(2) If \(f(x)\) has a local minimum and the minimum value is less than \(0\), determine the range of values for \(a\)." }, ] stream = ollama.chat( model='internlm/internlm3-8b-instruct', messages=messages, stream=True, ) for chunk in stream: print(chunk['message']['content'], end='', flush=True) ``` #### #### vLLM inference Refer to [installation](https://docs.vllm.ai/en/latest/getting_started/installation/index.html) to install the latest code of vllm ```python pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly ``` inference code ```python from vllm import LLM, SamplingParams llm = LLM(model="internlm/internlm3-8b-instruct") sampling_params = SamplingParams(temperature=1, repetition_penalty=1.005, top_k=40, top_p=0.8, max_tokens=8192) prompts = [ { "role": "system", "content": thinking_system_prompt, }, { "role": "user", "content": "Given the function\(f(x)=\mathrm{e}^{x}-ax - a^{3}\),\n(1) When \(a = 1\), find the equation of the tangent line to the curve \(y = f(x)\) at the point \((1,f(1))\).\n(2) If \(f(x)\) has a local minimum and the minimum value is less than \(0\), determine the range of values for \(a\)." }, ] outputs = llm.chat(prompts, sampling_params=sampling_params, use_tqdm=False) print(outputs) ``` ## Open Source License Code and model weights are licensed under Apache-2.0. ## Citation ``` @misc{cai2024internlm2, title={InternLM2 Technical Report}, author={Zheng Cai and Maosong Cao and Haojiong Chen and Kai Chen and Keyu Chen and Xin Chen and Xun Chen and Zehui Chen and Zhi Chen and Pei Chu and Xiaoyi Dong and Haodong Duan and Qi Fan and Zhaoye Fei and Yang Gao and Jiaye Ge and Chenya Gu and Yuzhe Gu and Tao Gui and Aijia Guo and Qipeng Guo and Conghui He and Yingfan Hu and Ting Huang and Tao Jiang and Penglong Jiao and Zhenjiang Jin and Zhikai Lei and Jiaxing Li and Jingwen Li and Linyang Li and Shuaibin Li and Wei Li and Yining Li and Hongwei Liu and Jiangning Liu and Jiawei Hong and Kaiwen Liu and Kuikun Liu and Xiaoran Liu and Chengqi Lv and Haijun Lv and Kai Lv and Li Ma and Runyuan Ma and Zerun Ma and Wenchang Ning and Linke Ouyang and Jiantao Qiu and Yuan Qu and Fukai Shang and Yunfan Shao and Demin Song and Zifan Song and Zhihao Sui and Peng Sun and Yu Sun and Huanze Tang and Bin Wang and Guoteng Wang and Jiaqi Wang and Jiayu Wang and Rui Wang and Yudong Wang and Ziyi Wang and Xingjian Wei and Qizhen Weng and Fan Wu and Yingtong Xiong and Chao Xu and Ruiliang Xu and Hang Yan and Yirong Yan and Xiaogui Yang and Haochen Ye and Huaiyuan Ying and Jia Yu and Jing Yu and Yuhang Zang and Chuyu Zhang and Li Zhang and Pan Zhang and Peng Zhang and Ruijie Zhang and Shuo Zhang and Songyang Zhang and Wenjian Zhang and Wenwei Zhang and Xingcheng Zhang and Xinyue Zhang and Hui Zhao and Qian Zhao and Xiaomeng Zhao and Fengzhe Zhou and Zaida Zhou and Jingming Zhuo and Yicheng Zou and Xipeng Qiu and Yu Qiao and Dahua Lin}, year={2024}, eprint={2403.17297}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## 简介 ### InternLM3-8B-Instruct InternLM3，即书生·浦语大模型第3代，开源了80亿参数，面向通用使用与高阶推理的指令模型（InternLM3-8B-Instruct）。模型具备以下特点： - 更低的代价取得更高的性能: 在推理、知识类任务上取得同量级最优性能，超过Llama3.1-8B和Qwen2.5-7B。值得关注的是InternLM3只用了4万亿词元进行训练，对比同级别模型训练成本节省75%以上。 - 深度思考能力: InternLM3支持通过长思维链求解复杂推理任务的深度思考模式，同时还兼顾了用户体验更流畅的通用回复模式。 #### 性能评测我们使用开源评测工具 [OpenCompass](https://github.com/internLM/OpenCompass/) 从学科综合能力、语言能力、知识能力、推理能力、理解能力五大能力维度对InternLM开展全面评测，部分评测结果如下表所示，欢迎访问[ OpenCompass 榜单 ](https://rank.opencompass.org.cn)获取更多的评测结果。 \| \| 评测集\模型 \| InternLM3-8B-Instruct \| Qwen2.5-7B-Instruct \| Llama3.1-8B-Instruct \| GPT-4o-mini(闭源) \| \| ------------ \| ------------------------------- \| --------------------- \| ------------------- \| -------------------- \| ----------------- \| \| General \| CMMLU(0-shot) \| 83.1 \| 75.8 \| 53.9 \| 66.0 \| \| \| MMLU(0-shot) \| 76.6 \| 76.8 \| 71.8 \| 82.7 \| \| \| MMLU-Pro(0-shot) \| 57.6 \| 56.2 \| 48.1 \| 64.1 \| \| Reasoning \| GPQA-Diamond(0-shot) \| 37.4 \| 33.3 \| 24.2 \| 42.9 \| \| \| DROP(0-shot) \| 83.1 \| 80.4 \| 81.6 \| 85.2 \| \| \| HellaSwag(10-shot) \| 91.2 \| 85.3 \| 76.7 \| 89.5 \| \| \| KOR-Bench(0-shot) \| 56.4 \| 44.6 \| 47.7 \| 58.2 \| \| MATH \| MATH-500(0-shot) \| 83.0* \| 72.4 \| 48.4 \| 74.0 \| \| \| AIME2024(0-shot) \| 20.0* \| 16.7 \| 6.7 \| 13.3 \| \| Coding \| LiveCodeBench(2407-2409 Pass@1) \| 17.8 \| 16.8 \| 12.9 \| 21.8 \| \| \| HumanEval(Pass@1) \| 82.3 \| 85.4 \| 72.0 \| 86.6 \| \| Instrunction \| IFEval(Prompt-Strict) \| 79.3 \| 71.7 \| 75.2 \| 79.7 \| \| LongContext \| RULER(4-128K Average) \| 87.9 \| 81.4 \| 88.5 \| 90.7 \| \| Chat \| AlpacaEval 2.0(LC WinRate) \| 51.1 \| 30.3 \| 25.0 \| 50.7 \| \| \| WildBench(Raw Score) \| 33.1 \| 23.3 \| 1.5 \| 40.3 \| \| \| MT-Bench-101(Score 1-10) \| 8.59 \| 8.49 \| 8.37 \| 8.87 \| - 表中标粗的数值表示在对比的开源模型中的最高值。 - 以上评测结果基于 [OpenCompass](https://github.com/internLM/OpenCompass/) 获得(部分数据标注``代表使用深度思考模式进行评测)，具体测试细节可参见 [OpenCompass](https://github.com/internLM/OpenCompass/) 中提供的配置文件。 - 评测数据会因 [OpenCompass](https://github.com/internLM/OpenCompass/) 的版本迭代而存在数值差异，请以 [OpenCompass](https://github.com/internLM/OpenCompass/) 最新版的评测结果为主。局限性：* 尽管在训练过程中我们非常注重模型的安全性，尽力促使模型输出符合伦理和法律要求的文本，但受限于模型大小以及概率生成范式，模型可能会产生各种不符合预期的输出，例如回复内容包含偏见、歧视等有害内容，请勿传播这些内容。由于传播不良信息导致的任何后果，本项目不承担责任。 #### 依赖 ```python transformers >= 4.48 ``` #### 常规对话模式 ##### Transformers 推理通过以下的代码加载 InternLM3 8B Instruct 模型 ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_dir = "internlm/internlm3-8b-instruct" tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True) # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error. model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() # (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes. # InternLM3 8B in 4bit will cost nearly 8GB GPU memory. # pip install -U bitsandbytes # 8-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_8bit=True) # 4-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_4bit=True) model = model.eval() system_prompt = """You are an AI assistant whose name is InternLM (书生·浦语). - InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless. - InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.""" messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": "Please tell me five scenic spots in Shanghai"}, ] tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda") generated_ids = model.generate(tokenized_chat, max_new_tokens=1024, temperature=1, repetition_penalty=1.005, top_k=40, top_p=0.8) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(tokenized_chat, generated_ids) ] prompt = tokenizer.batch_decode(tokenized_chat)[0] print(prompt) response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` ##### LMDeploy 推理 LMDeploy 是涵盖了 LLM 任务的全套轻量化、部署和服务解决方案。 ```bash pip install lmdeploy ``` 你可以使用以下 python 代码进行本地批量推理: ```python import lmdeploy model_dir = "internlm/internlm3-8b-instruct" pipe = lmdeploy.pipeline(model_dir) response = pipe(["Please tell me five scenic spots in Shanghai"]) print(response) ``` 或者你可以使用以下命令启动兼容 OpenAI API 的服务: ```bash lmdeploy serve api_server internlm/internlm3-8b-instruct --model-name internlm3-8b-instruct --server-port 23333 ``` 然后你可以向服务端发起一个聊天请求: ```bash curl http://localhost:23333/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "internlm3-8b-instruct", "messages": [ {"role": "user", "content": "介绍一下深度学习。"} ] }' ``` 更多信息请查看 [LMDeploy 文档](https://lmdeploy.readthedocs.io/en/latest/) ##### Ollama 推理准备工作 ```python # install ollama curl -fsSL https://ollama.com/install.sh \| sh # fetch 模型 ollama pull internlm/internlm3-8b-instruct # install python库 pip install ollama ``` 推理代码 ```python import ollama system_prompt = """You are an AI assistant whose name is InternLM (书生·浦语). - InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless. - InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.""" messages = [ { "role": "system", "content": system_prompt, }, { "role": "user", "content": "Please tell me five scenic spots in Shanghai" }, ] stream = ollama.chat( model='internlm/internlm3-8b-instruct', messages=messages, stream=True, ) for chunk in stream: print(chunk['message']['content'], end='', flush=True) ``` #### ##### vLLM 推理参考[文档](https://docs.vllm.ai/en/latest/getting_started/installation/index.html) 安装 vllm 最新代码 ```bash pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly ``` 推理代码 ```python from vllm import LLM, SamplingParams llm = LLM(model="internlm/internlm3-8b-instruct") sampling_params = SamplingParams(temperature=1, repetition_penalty=1.005, top_k=40, top_p=0.8) system_prompt = """You are an AI assistant whose name is InternLM (书生·浦语). - InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless. - InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.""" prompts = [ { "role": "system", "content": system_prompt, }, { "role": "user", "content": "Please tell me five scenic spots in Shanghai" }, ] outputs = llm.chat(prompts, sampling_params=sampling_params, use_tqdm=False) print(outputs) ``` #### 深度思考模式 ##### 深度思考 Demo <img src="https://github.com/InternLM/InternLM/blob/017ba7446d20ecc3b9ab8e7b66cc034500868ab4/assets/solve_puzzle.png?raw=true" width="400"/> ##### 深度思考 system prompt ```python thinking_system_prompt = """You are an expert mathematician with extensive experience in mathematical competitions. You approach problems through systematic thinking and rigorous reasoning. When solving problems, follow these thought processes: ## Deep Understanding Take time to fully comprehend the problem before attempting a solution. Consider: - What is the real question being asked? - What are the given conditions and what do they tell us? - Are there any special restrictions or assumptions? - Which information is crucial and which is supplementary? ## Multi-angle Analysis Before solving, conduct thorough analysis: - What mathematical concepts and properties are involved? - Can you recall similar classic problems or solution methods? - Would diagrams or tables help visualize the problem? - Are there special cases that need separate consideration? ## Systematic Thinking Plan your solution path: - Propose multiple possible approaches - Analyze the feasibility and merits of each method - Choose the most appropriate method and explain why - Break complex problems into smaller, manageable steps ## Rigorous Proof During the solution process: - Provide solid justification for each step - Include detailed proofs for key conclusions - Pay attention to logical connections - Be vigilant about potential oversights ## Repeated Verification After completing your solution: - Verify your results satisfy all conditions - Check for overlooked special cases - Consider if the solution can be optimized or simplified - Review your reasoning process Remember: 1. Take time to think thoroughly rather than rushing to an answer 2. Rigorously prove each key conclusion 3. Keep an open mind and try different approaches 4. Summarize valuable problem-solving methods 5. Maintain healthy skepticism and verify multiple times Your response should reflect deep mathematical understanding and precise logical thinking, making your solution path and reasoning clear to others. When you're ready, present your complete solution with: - Clear problem understanding - Detailed solution process - Key insights - Thorough verification Focus on clear, logical progression of ideas and thorough explanation of your mathematical reasoning. Provide answers in the same language as the user asking the question, repeat the final answer using a '\\boxed{}' without any units, you have [[8192]] tokens to complete the answer. """ ``` ##### Transformers 推理 ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_dir = "internlm/internlm3-8b-instruct" tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True) # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error. model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() # (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes. # InternLM3 8B in 4bit will cost nearly 8GB GPU memory. # pip install -U bitsandbytes # 8-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_8bit=True) # 4-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_4bit=True) model = model.eval() messages = [ {"role": "system", "content": thinking_system_prompt}, {"role": "user", "content": "已知函数\(f(x)=\mathrm{e}^{x}-ax - a^{3}\)。\n（1）当\(a = 1\)时，求曲线\(y = f(x)\)在点\((1,f(1))\)处的切线方程；\n（2）若\(f(x)\)有极小值，且极小值小于\(0\)，求\(a\)的取值范围。"}, ] tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda") generated_ids = model.generate(tokenized_chat, max_new_tokens=8192) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(tokenized_chat, generated_ids) ] prompt = tokenizer.batch_decode(tokenized_chat)[0] print(prompt) response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` ##### LMDeploy 推理 LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams. ```bash pip install lmdeploy ``` You can run batch inference locally with the following python code: ```python from lmdeploy import pipeline, GenerationConfig, ChatTemplateConfig model_dir = "internlm/internlm3-8b-instruct" chat_template_config = ChatTemplateConfig(model_name='internlm3') pipe = pipeline(model_dir, chat_template_config=chat_template_config) messages = [ {"role": "system", "content": thinking_system_prompt}, {"role": "user", "content": "已知函数\(f(x)=\mathrm{e}^{x}-ax - a^{3}\)。\n（1）当\(a = 1\)时，求曲线\(y = f(x)\)在点\((1,f(1))\)处的切线方程；\n（2）若\(f(x)\)有极小值，且极小值小于\(0\)，求\(a\)的取值范围。"}, ] response = pipe(messages, gen_config=GenerationConfig(max_new_tokens=2048)) print(response) ``` ##### Ollama 推理准备工作 ```python # install ollama curl -fsSL https://ollama.com/install.sh \| sh # fetch 模型 ollama pull internlm/internlm3-8b-instruct # install python库 pip install ollama ``` inference code, ```python import ollama messages = [ { "role": "system", "content": thinking_system_prompt, }, { "role": "user", "content": "Given the function\(f(x)=\mathrm{e}^{x}-ax - a^{3}\),\n(1) When \(a = 1\), find the equation of the tangent line to the curve \(y = f(x)\) at the point \((1,f(1))\).\n(2) If \(f(x)\) has a local minimum and the minimum value is less than \(0\), determine the range of values for \(a\)." }, ] stream = ollama.chat( model='internlm/internlm3-8b-instruct', messages=messages, stream=True, ) for chunk in stream: print(chunk['message']['content'], end='', flush=True) ``` #### ##### vLLM 推理参考[文档](https://docs.vllm.ai/en/latest/getting_started/installation/index.html) 安装 vllm 最新代码 ```bash pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly ``` 推理代码 ```python from vllm import LLM, SamplingParams llm = LLM(model="internlm/internlm3-8b-instruct") sampling_params = SamplingParams(temperature=1, repetition_penalty=1.005, top_k=40, top_p=0.8, max_tokens=8192) prompts = [ { "role": "system", "content": thinking_system_prompt, }, { "role": "user", "content": "已知函数\(f(x)=\mathrm{e}^{x}-ax - a^{3}\)。\n（1）当\(a = 1\)时，求曲线\(y = f(x)\)在点\((1,f(1))\)处的切线方程；\n（2）若\(f(x)\)有极小值，且极小值小于\(0\)，求\(a\)的取值范围。" }, ] outputs = llm.chat(prompts, sampling_params=sampling_params, use_tqdm=False) print(outputs) ``` ## 开源许可证本仓库的代码和权重依照 Apache-2.0 协议开源。 ## 引用 ``` @misc{cai2024internlm2, title={InternLM2 Technical Report}, author={Zheng Cai and Maosong Cao and Haojiong Chen and Kai Chen and Keyu Chen and Xin Chen and Xun Chen and Zehui Chen and Zhi Chen and Pei Chu and Xiaoyi Dong and Haodong Duan and Qi Fan and Zhaoye Fei and Yang Gao and Jiaye Ge and Chenya Gu and Yuzhe Gu and Tao Gui and Aijia Guo and Qipeng Guo and Conghui He and Yingfan Hu and Ting Huang and Tao Jiang and Penglong Jiao and Zhenjiang Jin and Zhikai Lei and Jiaxing Li and Jingwen Li and Linyang Li and Shuaibin Li and Wei Li and Yining Li and Hongwei Liu and Jiangning Liu and Jiawei Hong and Kaiwen Liu and Kuikun Liu and Xiaoran Liu and Chengqi Lv and Haijun Lv and Kai Lv and Li Ma and Runyuan Ma and Zerun Ma and Wenchang Ning and Linke Ouyang and Jiantao Qiu and Yuan Qu and Fukai Shang and Yunfan Shao and Demin Song and Zifan Song and Zhihao Sui and Peng Sun and Yu Sun and Huanze Tang and Bin Wang and Guoteng Wang and Jiaqi Wang and Jiayu Wang and Rui Wang and Yudong Wang and Ziyi Wang and Xingjian Wei and Qizhen Weng and Fan Wu and Yingtong Xiong and Chao Xu and Ruiliang Xu and Hang Yan and Yirong Yan and Xiaogui Yang and Haochen Ye and Huaiyuan Ying and Jia Yu and Jing Yu and Yuhang Zang and Chuyu Zhang and Li Zhang and Pan Zhang and Peng Zhang and Ruijie Zhang and Shuo Zhang and Songyang Zhang and Wenjian Zhang and Wenwei Zhang and Xingcheng Zhang and Xinyue Zhang and Hui Zhao and Qian Zhao and Xiaomeng Zhao and Fengzhe Zhou and Zaida Zhou and Jingming Zhuo and Yicheng Zou and Xipeng Qiu and Yu Qiao and Dahua Lin}, year={2024}, eprint={2403.17297}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
TOMFORD79/Special_Titanium4	TOMFORD79	2025-03-06T02:28:59Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-03-06T02:15:52Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
taobao-mnn/QwQ-32B-MNN	taobao-mnn	2025-03-06T02:28:56Z	0	0	null	[ "chat", "text-generation", "en", "license:apache-2.0", "region:us" ]	text-generation	2025-03-06T02:08:02Z	--- license: apache-2.0 language: - en pipeline_tag: text-generation tags: - chat --- # QwQ-32B-MNN ## Introduction This model is a 4-bit quantized version of the MNN model exported from QwQ-32B using [llmexport](https://github.com/alibaba/MNN/tree/master/transformers/llm/export). ## Download ```bash # install huggingface pip install huggingface ``` ```bash # shell download huggingface download --model 'taobao-mnn/QwQ-32B-MNN' --local_dir 'path/to/dir' ``` ```python # SDK download from huggingface_hub import snapshot_download model_dir = snapshot_download('taobao-mnn/QwQ-32B-MNN') ``` ```bash # git clone git clone https://www.modelscope.cn/taobao-mnn/QwQ-32B-MNN ``` ## Usage ```bash # clone MNN source git clone https://github.com/alibaba/MNN.git # compile cd MNN mkdir build && cd build cmake .. -DMNN_LOW_MEMORY=true -DMNN_CPU_WEIGHT_DEQUANT_GEMM=true -DMNN_BUILD_LLM=true -DMNN_SUPPORT_TRANSFORMER_FUSE=true make -j # run ./llm_demo /path/to/QwQ-32B-MNN/config.json prompt.txt ``` ## Document [MNN-LLM](https://mnn-docs.readthedocs.io/en/latest/transformers/llm.html#)
ClarenceDan/6cd884c7-ba6d-43ca-82c6-37de4ffcb04b	ClarenceDan	2025-03-06T02:28:25Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:lmsys/vicuna-7b-v1.5", "base_model:adapter:lmsys/vicuna-7b-v1.5", "license:llama2", "region:us" ]	null	2025-03-06T01:53:01Z	--- library_name: peft license: llama2 base_model: lmsys/vicuna-7b-v1.5 tags: - axolotl - generated_from_trainer model-index: - name: 6cd884c7-ba6d-43ca-82c6-37de4ffcb04b 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: lmsys/vicuna-7b-v1.5 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - c85e29fecb2ff3d0_train_data.json ds_type: json format: custom path: /workspace/input_data/c85e29fecb2ff3d0_train_data.json type: field_instruction: instruction field_output: 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: 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/6cd884c7-ba6d-43ca-82c6-37de4ffcb04b 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/c85e29fecb2ff3d0_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: 8cbffdb5-6a01-4537-a1fa-6ea0aeab36ad wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 8cbffdb5-6a01-4537-a1fa-6ea0aeab36ad warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 6cd884c7-ba6d-43ca-82c6-37de4ffcb04b This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8164 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0253 \| 0.0002 \| 1 \| 0.9100 \| \| 1.0145 \| 0.0005 \| 3 \| 0.9087 \| \| 0.9082 \| 0.0010 \| 6 \| 0.8890 \| \| 0.8546 \| 0.0015 \| 9 \| 0.8164 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
casque/JasminePonyXL_character-10	casque	2025-03-06T02:27:07Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-03-06T02:26:27Z	--- license: creativeml-openrail-m ---
peulsilva/qwen-0.5b-instruct-summary-pt-rank64	peulsilva	2025-03-06T02:26:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-03-06T02:25:38Z	--- base_model: unsloth/qwen2-0.5b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: peulsilva - License: apache-2.0 - Finetuned from model : unsloth/qwen2-0.5b-instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mlfoundations-dev/llama3-1_8b_gsmyrnis_test_dpo_data	mlfoundations-dev	2025-03-06T02:25:36Z	40	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-04T01:33:47Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - trl - dpo - llama-factory - generated_from_trainer model-index: - name: llama3-1_8b_gsmyrnis_test_dpo_data 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. --> # llama3-1_8b_gsmyrnis_test_dpo_data This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/gsmyrnis_test_dpo_data dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-07 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - total_train_batch_size: 32 - total_eval_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1 - Datasets 3.0.2 - Tokenizers 0.20.3

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