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Kuongan/CS221-deberta-v3-base	Kuongan	2024-12-18T09:27:57Z	70	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T08:03:37Z	--- library_name: transformers license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: CS221-deberta-v3-base 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. --> # CS221-deberta-v3-base This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3858 - F1: 0.7702 - Roc Auc: 0.8268 - Accuracy: 0.4765 ## 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: 32 - eval_batch_size: 32 - 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: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:--------:\| \| 0.5686 \| 1.0 \| 70 \| 0.5804 \| 0.4593 \| 0.6262 \| 0.1516 \| \| 0.4291 \| 2.0 \| 140 \| 0.4434 \| 0.6553 \| 0.7436 \| 0.3700 \| \| 0.3496 \| 3.0 \| 210 \| 0.3954 \| 0.7153 \| 0.7843 \| 0.4025 \| \| 0.2663 \| 4.0 \| 280 \| 0.3775 \| 0.7515 \| 0.8110 \| 0.4567 \| \| 0.2147 \| 5.0 \| 350 \| 0.3772 \| 0.7513 \| 0.8122 \| 0.4567 \| \| 0.1815 \| 6.0 \| 420 \| 0.3787 \| 0.7589 \| 0.8183 \| 0.4585 \| \| 0.1409 \| 7.0 \| 490 \| 0.3915 \| 0.7617 \| 0.8187 \| 0.4729 \| \| 0.1165 \| 8.0 \| 560 \| 0.3858 \| 0.7702 \| 0.8268 \| 0.4765 \| \| 0.1082 \| 9.0 \| 630 \| 0.3874 \| 0.7693 \| 0.8262 \| 0.4675 \| \| 0.1069 \| 10.0 \| 700 \| 0.3864 \| 0.7693 \| 0.8262 \| 0.4675 \| ### Framework versions - Transformers 4.47.1 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0
HanningZhang/deepseek-prm	HanningZhang	2024-12-18T09:16:29Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T09:13:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isspek/xlnet-base-cased_ebola_chatgpt_1_2e-5_16_undersampling_0.2	isspek	2024-12-18T09:09:53Z	128	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-17T18:11:12Z	--- 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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isspek/xlnet-base-cased_ebola_chatgpt_5_2e-5_16_undersampling_0.1	isspek	2024-12-18T09:09:28Z	137	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-17T18:08:49Z	--- 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. 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isspek/xlnet-base-cased_ebola_chatgpt_3_2e-5_16_undersampling_0.1	isspek	2024-12-18T09:08:35Z	125	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-17T18:11:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isspek/xlnet-base-cased_ebola_chatgpt_2_2e-5_16_undersampling_0.1	isspek	2024-12-18T09:08:10Z	123	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-17T18:13:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Nubletz/bert-simplestyle-split-embedding-recon-691	Nubletz	2024-12-18T09:08:09Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-12-18T05:18:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isspek/xlnet-base-cased_ebola_llama_5_2e-5_16_undersampling_0.5	isspek	2024-12-18T09:07:24Z	120	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T13:32:26Z	--- 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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isspek/xlnet-base-cased_ebola_llama_2_2e-5_16_undersampling_0.5	isspek	2024-12-18T09:06:01Z	119	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T13:36:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isspek/xlnet-base-cased_ebola_llama_1_2e-5_16_undersampling_0.5	isspek	2024-12-18T09:05:36Z	119	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T13:34:26Z	--- 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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isspek/xlnet-base-cased_ebola_llama_5_2e-5_16_undersampling_0.4	isspek	2024-12-18T09:05:08Z	119	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T13:33:02Z	--- 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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isspek/xlnet-base-cased_ebola_llama_4_2e-5_16_undersampling_0.4	isspek	2024-12-18T09:04:43Z	119	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T13:37:46Z	--- 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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isspek/xlnet-base-cased_ebola_llama_4_2e-5_16_undersampling_0.3	isspek	2024-12-18T09:02:25Z	120	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T13:35:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isspek/xlnet-base-cased_ebola_llama_2_2e-5_16_undersampling_0.3	isspek	2024-12-18T09:01:30Z	120	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T13:35:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isspek/xlnet-base-cased_ebola_llama_3_2e-5_16_undersampling_0.2	isspek	2024-12-18T08:59:46Z	116	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T13:34:09Z	--- 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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Primeness/teabagger0012	Primeness	2024-12-18T08:58:30Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T07:00:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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DeepDream2045/8818eebe-3505-4b30-8d5c-72c319b17bab	DeepDream2045	2024-12-18T08:58:08Z	5	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4", "base_model:adapter:MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4", "region:us" ]	null	2024-12-18T08:44:59Z	--- library_name: peft base_model: MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4 tags: - axolotl - generated_from_trainer model-index: - name: 8818eebe-3505-4b30-8d5c-72c319b17bab 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: `0.5.2` ```yaml adapter: lora base_model: MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f3058a58b1f571da_train_data.json ds_type: json format: custom path: /workspace/input_data/f3058a58b1f571da_train_data.json type: field_instruction: question field_output: answers format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 1 eval_max_new_tokens: 128 eval_steps: 25 eval_table_size: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 16 gradient_checkpointing: true group_by_length: true hub_model_id: DeepDream2045/8818eebe-3505-4b30-8d5c-72c319b17bab 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: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 50 micro_batch_size: 2 mlflow_experiment_name: /tmp/f3058a58b1f571da_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 25 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: 8818eebe-3505-4b30-8d5c-72c319b17bab wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 8818eebe-3505-4b30-8d5c-72c319b17bab warmup_ratio: 0.05 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 8818eebe-3505-4b30-8d5c-72c319b17bab This model is a fine-tuned version of [MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4](https://huggingface.co/MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6490 ## 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 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 2 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5723 \| 0.0258 \| 1 \| 4.0140 \| \| 0.4878 \| 0.6462 \| 25 \| 0.6940 \| \| 0.7053 \| 1.3021 \| 50 \| 0.6490 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.3.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
2GM/3_test_chatbot2_blossom	2GM	2024-12-18T08:55:23Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T08:28:29Z	--- 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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(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]
mingxilei/roberta-imdb-eps-5	mingxilei	2024-12-18T08:52:50Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T08:29:05Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: roberta-imdb-eps-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. --> # roberta-imdb-eps-5 This model was trained from scratch 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: 32 - eval_batch_size: 32 - 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: 1 ### Framework versions - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
shahin-canary/sdxl-sks-charctr-omn-v7	shahin-canary	2024-12-18T08:50:01Z	14	1	diffusers	[ "diffusers", "autotrain", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "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	2024-12-18T08:49:58Z	--- tags: - autotrain - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of charctr_omn with a plain green background license: openrail++ --- # AutoTrain SDXL LoRA DreamBooth - shahin-canary/sdxl-sks-charctr-omn-v7 <Gallery /> ## Model description These are shahin-canary/sdxl-sks-charctr-omn-v7 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: True. Special VAE used for training: None. ## Trigger words You should use photo of charctr_omn with a plain green background to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](shahin-canary/sdxl-sks-charctr-omn-v7/tree/main) them in the Files & versions tab.
Marialab/finetuned-seamless-m4T-medium-1000-step	Marialab	2024-12-18T08:49:43Z	77	0	transformers	[ "transformers", "safetensors", "seamless_m4t_v2", "feature-extraction", "generated_from_trainer", "ar", "dataset:darija-c", "base_model:facebook/seamless-m4t-medium", "base_model:finetune:facebook/seamless-m4t-medium", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	feature-extraction	2024-12-18T08:47:08Z	--- library_name: transformers language: - ar license: cc-by-nc-4.0 base_model: facebook/seamless-m4t-medium tags: - generated_from_trainer datasets: - darija-c metrics: - bleu model-index: - name: Finetuned seamless-m4t-medium for darija speech translation 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. --> # Finetuned seamless-m4t-medium for darija speech translation This model is a fine-tuned version of [facebook/seamless-m4t-medium](https://huggingface.co/facebook/seamless-m4t-medium) on the Darija-C dataset. It achieves the following results on the evaluation set: - Loss: 0.9487 - Bleu: 0.6520 ## 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: 16 - eval_batch_size: 4 - 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 - lr_scheduler_warmup_steps: 100 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 11.4385 \| 12.5 \| 50 \| 7.7186 \| 0.0 \| \| 7.0056 \| 25.0 \| 100 \| 4.6583 \| 0.0 \| \| 4.7721 \| 37.5 \| 150 \| 3.4895 \| 0.0402 \| \| 3.9618 \| 50.0 \| 200 \| 2.7834 \| 0.0324 \| \| 3.2307 \| 62.5 \| 250 \| 2.3169 \| 0.0601 \| \| 2.9341 \| 75.0 \| 300 \| 2.1102 \| 0.0969 \| \| 2.5517 \| 87.5 \| 350 \| 1.9636 \| 0.1041 \| \| 2.3681 \| 100.0 \| 400 \| 1.8770 \| 0.0888 \| \| 2.1031 \| 112.5 \| 450 \| 1.7589 \| 0.1302 \| \| 2.1191 \| 125.0 \| 500 \| 1.6578 \| 0.1765 \| \| 1.9185 \| 137.5 \| 550 \| 1.5659 \| 0.1802 \| \| 1.9021 \| 150.0 \| 600 \| 1.4514 \| 0.4482 \| \| 2.0155 \| 162.5 \| 650 \| 1.3543 \| 0.3924 \| \| 1.8151 \| 175.0 \| 700 \| 1.3195 \| 0.3651 \| \| 1.7461 \| 187.5 \| 750 \| 1.1878 \| 0.4723 \| \| 1.6512 \| 200.0 \| 800 \| 1.1107 \| 0.5124 \| \| 1.6378 \| 212.5 \| 850 \| 1.0550 \| 0.5922 \| \| 1.4851 \| 225.0 \| 900 \| 0.9948 \| 0.6548 \| \| 1.5016 \| 237.5 \| 950 \| 0.9596 \| 0.6390 \| \| 1.4868 \| 250.0 \| 1000 \| 0.9487 \| 0.6520 \| ### Framework versions - Transformers 4.47.1 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0
mradermacher/BeagleLake-7B-i1-GGUF	mradermacher	2024-12-18T08:46:04Z	37	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "mistral", "fhai50032/RolePlayLake-7B", "mlabonne/NeuralBeagle14-7B", "en", "base_model:fhai50032/BeagleLake-7B", "base_model:quantized:fhai50032/BeagleLake-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-12-18T07:50:12Z	--- base_model: fhai50032/BeagleLake-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - mistral - fhai50032/RolePlayLake-7B - mlabonne/NeuralBeagle14-7B --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/fhai50032/BeagleLake-7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/BeagleLake-7B-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/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-Q2_K.gguf) \| i1-Q2_K \| 2.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF/resolve/main/BeagleLake-7B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/BeagleLake-7B-GGUF	mradermacher	2024-12-18T08:44:12Z	78	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "mistral", "fhai50032/RolePlayLake-7B", "mlabonne/NeuralBeagle14-7B", "en", "base_model:fhai50032/BeagleLake-7B", "base_model:quantized:fhai50032/BeagleLake-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-17T18:59:45Z	--- base_model: fhai50032/BeagleLake-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - mistral - fhai50032/RolePlayLake-7B - mlabonne/NeuralBeagle14-7B --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/fhai50032/BeagleLake-7B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/BeagleLake-7B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-GGUF/resolve/main/BeagleLake-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-GGUF/resolve/main/BeagleLake-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-GGUF/resolve/main/BeagleLake-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-GGUF/resolve/main/BeagleLake-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-GGUF/resolve/main/BeagleLake-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-GGUF/resolve/main/BeagleLake-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-GGUF/resolve/main/BeagleLake-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-GGUF/resolve/main/BeagleLake-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-GGUF/resolve/main/BeagleLake-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-GGUF/resolve/main/BeagleLake-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-GGUF/resolve/main/BeagleLake-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/BeagleLake-7B-GGUF/resolve/main/BeagleLake-7B.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/yam-jom-7B-slerp-GGUF	mradermacher	2024-12-18T08:44:12Z	23	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2", "yam-peleg/Experiment26-7B", "en", "base_model:mayacinka/yam-jom-7B-slerp", "base_model:quantized:mayacinka/yam-jom-7B-slerp", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-12-18T08:35:41Z	--- base_model: mayacinka/yam-jom-7B-slerp language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2 - yam-peleg/Experiment26-7B --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/mayacinka/yam-jom-7B-slerp <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/yam-jom-7B-slerp-GGUF/resolve/main/yam-jom-7B-slerp.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/yam-jom-7B-slerp-GGUF/resolve/main/yam-jom-7B-slerp.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/yam-jom-7B-slerp-GGUF/resolve/main/yam-jom-7B-slerp.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/yam-jom-7B-slerp-GGUF/resolve/main/yam-jom-7B-slerp.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/yam-jom-7B-slerp-GGUF/resolve/main/yam-jom-7B-slerp.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/yam-jom-7B-slerp-GGUF/resolve/main/yam-jom-7B-slerp.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/yam-jom-7B-slerp-GGUF/resolve/main/yam-jom-7B-slerp.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/yam-jom-7B-slerp-GGUF/resolve/main/yam-jom-7B-slerp.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/yam-jom-7B-slerp-GGUF/resolve/main/yam-jom-7B-slerp.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/yam-jom-7B-slerp-GGUF/resolve/main/yam-jom-7B-slerp.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/yam-jom-7B-slerp-GGUF/resolve/main/yam-jom-7B-slerp.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/yam-jom-7B-slerp-GGUF/resolve/main/yam-jom-7B-slerp.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/GML-Mistral-merged-v1-GGUF	mradermacher	2024-12-18T08:27:28Z	13	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "en", "base_model:mlabonne/GML-Mistral-merged-v1", "base_model:quantized:mlabonne/GML-Mistral-merged-v1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-12-18T07:39:06Z	--- base_model: mlabonne/GML-Mistral-merged-v1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/mlabonne/GML-Mistral-merged-v1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/GML-Mistral-merged-v1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/GML-Mistral-merged-v1-GGUF/resolve/main/GML-Mistral-merged-v1.Q2_K.gguf) \| Q2_K \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/GML-Mistral-merged-v1-GGUF/resolve/main/GML-Mistral-merged-v1.Q3_K_S.gguf) \| Q3_K_S \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/GML-Mistral-merged-v1-GGUF/resolve/main/GML-Mistral-merged-v1.Q3_K_M.gguf) \| Q3_K_M \| 4.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/GML-Mistral-merged-v1-GGUF/resolve/main/GML-Mistral-merged-v1.Q3_K_L.gguf) \| Q3_K_L \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/GML-Mistral-merged-v1-GGUF/resolve/main/GML-Mistral-merged-v1.IQ4_XS.gguf) \| IQ4_XS \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/GML-Mistral-merged-v1-GGUF/resolve/main/GML-Mistral-merged-v1.Q4_K_S.gguf) \| Q4_K_S \| 5.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/GML-Mistral-merged-v1-GGUF/resolve/main/GML-Mistral-merged-v1.Q4_K_M.gguf) \| Q4_K_M \| 5.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/GML-Mistral-merged-v1-GGUF/resolve/main/GML-Mistral-merged-v1.Q5_K_S.gguf) \| Q5_K_S \| 6.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/GML-Mistral-merged-v1-GGUF/resolve/main/GML-Mistral-merged-v1.Q5_K_M.gguf) \| Q5_K_M \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/GML-Mistral-merged-v1-GGUF/resolve/main/GML-Mistral-merged-v1.Q6_K.gguf) \| Q6_K \| 7.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/GML-Mistral-merged-v1-GGUF/resolve/main/GML-Mistral-merged-v1.Q8_0.gguf) \| Q8_0 \| 9.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/GML-Mistral-merged-v1-GGUF/resolve/main/GML-Mistral-merged-v1.f16.gguf) \| f16 \| 18.1 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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 -->
Hachipo/qwen2.5-0.5B_educational_instruct_top1000	Hachipo	2024-12-18T08:27:09Z	186	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T08:26:04Z	--- 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. 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cdactvm/w2v-bert-kannada_new	cdactvm	2024-12-18T08:25:21Z	54	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-12-16T06:26:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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FlukeTJ/bge-m3-m2v-distilled-512	FlukeTJ	2024-12-18T08:25:13Z	7	0	model2vec	[ "model2vec", "safetensors", "embeddings", "static-embeddings", "base_model:BAAI/bge-m3", "base_model:finetune:BAAI/bge-m3", "license:mit", "region:us" ]	null	2024-12-18T08:24:53Z	--- base_model: BAAI/bge-m3 library_name: model2vec license: mit model_name: FlukeTJ/bge-m3-m2v-distilled-512 tags: - embeddings - static-embeddings --- # FlukeTJ/bge-m3-m2v-distilled-512 Model Card This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. ## Installation Install model2vec using pip: ``` pip install model2vec ``` ## Usage Load this model using the `from_pretrained` method: ```python from model2vec import StaticModel # Load a pretrained Model2Vec model model = StaticModel.from_pretrained("FlukeTJ/bge-m3-m2v-distilled-512") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` Alternatively, you can distill your own model using the `distill` method: ```python from model2vec.distill import distill # Choose a Sentence Transformer model model_name = "BAAI/bge-base-en-v1.5" # Distill the model m2v_model = distill(model_name=model_name, pca_dims=256) # Save the model m2v_model.save_pretrained("m2v_model") ``` ## How it works Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec. It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using zipf weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence. ## Additional Resources - [All Model2Vec models on the hub](https://huggingface.co/models?library=model2vec) - [Model2Vec Repo](https://github.com/MinishLab/model2vec) - [Model2Vec Results](https://github.com/MinishLab/model2vec?tab=readme-ov-file#results) - [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials) ## Library Authors Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled). ## Citation Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work. ``` @software{minishlab2024model2vec, authors = {Stephan Tulkens, Thomas van Dongen}, title = {Model2Vec: Turn any Sentence Transformer into a Small Fast Model}, year = {2024}, url = {https://github.com/MinishLab/model2vec}, } ```
Net007/AudioModel	Net007	2024-12-18T08:20:35Z	7	0	null	[ "safetensors", "wav2vec2", "audio-classification", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "region:us" ]	audio-classification	2024-12-18T08:04:31Z	--- base_model: - facebook/wav2vec2-base pipeline_tag: audio-classification ---
deepseek-ai/deepseek-vl2	deepseek-ai	2024-12-18T08:18:21Z	9,417	228	transformers	[ "transformers", "safetensors", "deepseek_vl_v2", "image-text-to-text", "arxiv:2412.10302", "license:other", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-12-13T09:06:44Z	--- license: other license_name: deepseek license_link: https://github.com/deepseek-ai/DeepSeek-LLM/blob/HEAD/LICENSE-MODEL pipeline_tag: image-text-to-text library_name: transformers --- ## 1. Introduction Introducing DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL. DeepSeek-VL2 demonstrates superior capabilities across various tasks, including but not limited to visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. Our model series is composed of three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small and DeepSeek-VL2, with 1.0B, 2.8B and 4.5B activated parameters respectively. DeepSeek-VL2 achieves competitive or state-of-the-art performance with similar or fewer activated parameters compared to existing open-source dense and MoE-based models. [DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding](https://arxiv.org/abs/2412.10302) [Github Repository](https://github.com/deepseek-ai/DeepSeek-VL2) Zhiyu Wu, Xiaokang Chen, Zizheng Pan, Xingchao Liu, Wen Liu, Damai Dai, Huazuo Gao, Yiyang Ma, Chengyue Wu, Bingxuan Wang, Zhenda Xie, Yu Wu, Kai Hu, Jiawei Wang, Yaofeng Sun, Yukun Li, Yishi Piao, Kang Guan, Aixin Liu, Xin Xie, Yuxiang You, Kai Dong, Xingkai Yu, Haowei Zhang, Liang Zhao, Yisong Wang, Chong Ruan* (* Equal Contribution, Project Lead, * Corresponding author) ![](https://github.com/deepseek-ai/DeepSeek-VL2/blob/main/images/vl2_teaser.jpeg) ### 2. Model Summary DeepSeek-VL2 is built on DeepSeekMoE-27B. ## 3. Quick Start ### Installation On the basis of `Python >= 3.8` environment, install the necessary dependencies by running the following command: ```shell pip install -e . ``` ### Notifications 1. We suggest to use a temperature T <= 0.7 when sampling. We observe a larger temperature decreases the generation quality. 2. To keep the number of tokens managable in the context window, we apply dynamic tiling strategy to <=2 images. When there are >=3 images, we directly pad the images to 384384 as inputs without tiling. 3. The main difference between DeepSeek-VL2-Tiny, DeepSeek-VL2-Small and DeepSeek-VL2 is the base LLM. ### Simple Inference Example ```python import torch from transformers import AutoModelForCausalLM from deepseek_vl.models import DeepseekVLV2Processor, DeepseekVLV2ForCausalLM from deepseek_vl.utils.io import load_pil_images # specify the path to the model model_path = "deepseek-ai/deepseek-vl2-small" vl_chat_processor: DeepseekVLV2Processor = DeepseekVLV2Processor.from_pretrained(model_path) tokenizer = vl_chat_processor.tokenizer vl_gpt: DeepseekVLV2ForCausalLM = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True) vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval() ## single image conversation example conversation = [ { "role": "<\|User\|>", "content": "<image>\n<\|ref\|>The giraffe at the back.<\|/ref\|>.", "images": ["./images/visual_grounding.jpeg"], }, {"role": "<\|Assistant\|>", "content": ""}, ] ## multiple images (or in-context learning) conversation example # conversation = [ # { # "role": "User", # "content": "<image_placeholder>A dog wearing nothing in the foreground, " # "<image_placeholder>a dog wearing a santa hat, " # "<image_placeholder>a dog wearing a wizard outfit, and " # "<image_placeholder>what's the dog wearing?", # "images": [ # "images/dog_a.png", # "images/dog_b.png", # "images/dog_c.png", # "images/dog_d.png", # ], # }, # {"role": "Assistant", "content": ""} # ] # load images and prepare for inputs pil_images = load_pil_images(conversation) prepare_inputs = vl_chat_processor( conversations=conversation, images=pil_images, force_batchify=True, system_prompt="" ).to(vl_gpt.device) # run image encoder to get the image embeddings inputs_embeds = vl_gpt.prepare_inputs_embeds(*prepare_inputs) # run the model to get the response outputs = vl_gpt.language_model.generate( inputs_embeds=inputs_embeds, attention_mask=prepare_inputs.attention_mask, pad_token_id=tokenizer.eos_token_id, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, max_new_tokens=512, do_sample=False, use_cache=True ) answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True) print(f"{prepare_inputs['sft_format'][0]}", answer) ``` ### Gradio Demo (TODO) ## 4. License This code repository is licensed under [MIT License](./LICENSE-CODE). The use of DeepSeek-VL2 models is subject to [DeepSeek Model License](./LICENSE-MODEL). DeepSeek-VL2 series supports commercial use. ## 5. Citation ``` @misc{wu2024deepseekvl2mixtureofexpertsvisionlanguagemodels, title={DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding}, author={Zhiyu Wu and Xiaokang Chen and Zizheng Pan and Xingchao Liu and Wen Liu and Damai Dai and Huazuo Gao and Yiyang Ma and Chengyue Wu and Bingxuan Wang and Zhenda Xie and Yu Wu and Kai Hu and Jiawei Wang and Yaofeng Sun and Yukun Li and Yishi Piao and Kang Guan and Aixin Liu and Xin Xie and Yuxiang You and Kai Dong and Xingkai Yu and Haowei Zhang and Liang Zhao and Yisong Wang and Chong Ruan}, year={2024}, eprint={2412.10302}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2412.10302}, } ``` ## 6. Contact If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).
deepseek-ai/deepseek-vl2-small	deepseek-ai	2024-12-18T08:17:59Z	16,921	112	transformers	[ "transformers", "safetensors", "deepseek_vl_v2", "image-text-to-text", "conversational", "arxiv:2412.10302", "license:other", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-12-13T09:01:03Z	--- license: other license_name: deepseek license_link: https://github.com/deepseek-ai/DeepSeek-LLM/blob/HEAD/LICENSE-MODEL pipeline_tag: image-text-to-text library_name: transformers --- ## 1. Introduction Introducing DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL. DeepSeek-VL2 demonstrates superior capabilities across various tasks, including but not limited to visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. Our model series is composed of three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small and DeepSeek-VL2, with 1.0B, 2.8B and 4.5B activated parameters respectively. DeepSeek-VL2 achieves competitive or state-of-the-art performance with similar or fewer activated parameters compared to existing open-source dense and MoE-based models. [DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding](https://arxiv.org/abs/2412.10302) [Github Repository](https://github.com/deepseek-ai/DeepSeek-VL2) Zhiyu Wu, Xiaokang Chen, Zizheng Pan, Xingchao Liu, Wen Liu, Damai Dai, Huazuo Gao, Yiyang Ma, Chengyue Wu, Bingxuan Wang, Zhenda Xie, Yu Wu, Kai Hu, Jiawei Wang, Yaofeng Sun, Yukun Li, Yishi Piao, Kang Guan, Aixin Liu, Xin Xie, Yuxiang You, Kai Dong, Xingkai Yu, Haowei Zhang, Liang Zhao, Yisong Wang, Chong Ruan* (* Equal Contribution, Project Lead, * Corresponding author) ![](https://github.com/deepseek-ai/DeepSeek-VL2/tree/main/images/vl2_teaser.jpeg) ### 2. Model Summary DeepSeek-VL2-small is built on DeepSeekMoE-16B. ## 3. Quick Start ### Installation On the basis of `Python >= 3.8` environment, install the necessary dependencies by running the following command: ```shell pip install -e . ``` ### Notifications 1. We suggest to use a temperature T <= 0.7 when sampling. We observe a larger temperature decreases the generation quality. 2. To keep the number of tokens managable in the context window, we apply dynamic tiling strategy to <=2 images. When there are >=3 images, we directly pad the images to 384384 as inputs without tiling. 3. The main difference between DeepSeek-VL2-Tiny, DeepSeek-VL2-Small and DeepSeek-VL2 is the base LLM. ### Simple Inference Example ```python import torch from transformers import AutoModelForCausalLM from deepseek_vl.models import DeepseekVLV2Processor, DeepseekVLV2ForCausalLM from deepseek_vl.utils.io import load_pil_images # specify the path to the model model_path = "deepseek-ai/deepseek-vl2-small" vl_chat_processor: DeepseekVLV2Processor = DeepseekVLV2Processor.from_pretrained(model_path) tokenizer = vl_chat_processor.tokenizer vl_gpt: DeepseekVLV2ForCausalLM = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True) vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval() ## single image conversation example conversation = [ { "role": "<\|User\|>", "content": "<image>\n<\|ref\|>The giraffe at the back.<\|/ref\|>.", "images": ["./images/visual_grounding.jpeg"], }, {"role": "<\|Assistant\|>", "content": ""}, ] ## multiple images (or in-context learning) conversation example # conversation = [ # { # "role": "User", # "content": "<image_placeholder>A dog wearing nothing in the foreground, " # "<image_placeholder>a dog wearing a santa hat, " # "<image_placeholder>a dog wearing a wizard outfit, and " # "<image_placeholder>what's the dog wearing?", # "images": [ # "images/dog_a.png", # "images/dog_b.png", # "images/dog_c.png", # "images/dog_d.png", # ], # }, # {"role": "Assistant", "content": ""} # ] # load images and prepare for inputs pil_images = load_pil_images(conversation) prepare_inputs = vl_chat_processor( conversations=conversation, images=pil_images, force_batchify=True, system_prompt="" ).to(vl_gpt.device) # run image encoder to get the image embeddings inputs_embeds = vl_gpt.prepare_inputs_embeds(*prepare_inputs) # run the model to get the response outputs = vl_gpt.language_model.generate( inputs_embeds=inputs_embeds, attention_mask=prepare_inputs.attention_mask, pad_token_id=tokenizer.eos_token_id, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, max_new_tokens=512, do_sample=False, use_cache=True ) answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True) print(f"{prepare_inputs['sft_format'][0]}", answer) ``` ### Gradio Demo (TODO) ## 4. License This code repository is licensed under [MIT License](./LICENSE-CODE). The use of DeepSeek-VL2 models is subject to [DeepSeek Model License](./LICENSE-MODEL). DeepSeek-VL2 series supports commercial use. ## 5. Citation ``` @misc{wu2024deepseekvl2mixtureofexpertsvisionlanguagemodels, title={DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding}, author={Zhiyu Wu and Xiaokang Chen and Zizheng Pan and Xingchao Liu and Wen Liu and Damai Dai and Huazuo Gao and Yiyang Ma and Chengyue Wu and Bingxuan Wang and Zhenda Xie and Yu Wu and Kai Hu and Jiawei Wang and Yaofeng Sun and Yukun Li and Yishi Piao and Kang Guan and Aixin Liu and Xin Xie and Yuxiang You and Kai Dong and Xingkai Yu and Haowei Zhang and Liang Zhao and Yisong Wang and Chong Ruan}, year={2024}, eprint={2412.10302}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2412.10302}, } ``` ## 6. Contact If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).
deepseek-ai/deepseek-vl2-tiny	deepseek-ai	2024-12-18T08:17:15Z	50,575	118	transformers	[ "transformers", "safetensors", "deepseek_vl_v2", "image-text-to-text", "arxiv:2412.10302", "license:other", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-12-13T08:49:22Z	--- license: other license_name: deepseek license_link: https://github.com/deepseek-ai/DeepSeek-LLM/blob/HEAD/LICENSE-MODEL pipeline_tag: image-text-to-text library_name: transformers --- ## 1. Introduction Introducing DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL. DeepSeek-VL2 demonstrates superior capabilities across various tasks, including but not limited to visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. Our model series is composed of three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small and DeepSeek-VL2, with 1.0B, 2.8B and 4.5B activated parameters respectively. DeepSeek-VL2 achieves competitive or state-of-the-art performance with similar or fewer activated parameters compared to existing open-source dense and MoE-based models. [DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding](https://arxiv.org/abs/2412.10302) [Github Repository](https://github.com/deepseek-ai/DeepSeek-VL2) Zhiyu Wu, Xiaokang Chen, Zizheng Pan, Xingchao Liu, Wen Liu, Damai Dai, Huazuo Gao, Yiyang Ma, Chengyue Wu, Bingxuan Wang, Zhenda Xie, Yu Wu, Kai Hu, Jiawei Wang, Yaofeng Sun, Yukun Li, Yishi Piao, Kang Guan, Aixin Liu, Xin Xie, Yuxiang You, Kai Dong, Xingkai Yu, Haowei Zhang, Liang Zhao, Yisong Wang, Chong Ruan* (* Equal Contribution, Project Lead, * Corresponding author) ![](https://github.com/deepseek-ai/DeepSeek-VL2/tree/main/images/vl2_teaser.jpeg) ### 2. Model Summary DeepSeek-VL2-tiny is built on DeepSeekMoE-3B (total activated parameters are 1.0B). ## 3. Quick Start ### Installation On the basis of `Python >= 3.8` environment, install the necessary dependencies by running the following command: ```shell pip install -e . ``` ### Notifications 1. We suggest to use a temperature T <= 0.7 when sampling. We observe a larger temperature decreases the generation quality. 2. To keep the number of tokens managable in the context window, we apply dynamic tiling strategy to <=2 images. When there are >=3 images, we directly pad the images to 384384 as inputs without tiling. 3. The main difference between DeepSeek-VL2-Tiny, DeepSeek-VL2-Small and DeepSeek-VL2 is the base LLM. ### Simple Inference Example ```python import torch from transformers import AutoModelForCausalLM from deepseek_vl.models import DeepseekVLV2Processor, DeepseekVLV2ForCausalLM from deepseek_vl.utils.io import load_pil_images # specify the path to the model model_path = "deepseek-ai/deepseek-vl2-small" vl_chat_processor: DeepseekVLV2Processor = DeepseekVLV2Processor.from_pretrained(model_path) tokenizer = vl_chat_processor.tokenizer vl_gpt: DeepseekVLV2ForCausalLM = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True) vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval() ## single image conversation example conversation = [ { "role": "<\|User\|>", "content": "<image>\n<\|ref\|>The giraffe at the back.<\|/ref\|>.", "images": ["./images/visual_grounding.jpeg"], }, {"role": "<\|Assistant\|>", "content": ""}, ] ## multiple images (or in-context learning) conversation example # conversation = [ # { # "role": "User", # "content": "<image_placeholder>A dog wearing nothing in the foreground, " # "<image_placeholder>a dog wearing a santa hat, " # "<image_placeholder>a dog wearing a wizard outfit, and " # "<image_placeholder>what's the dog wearing?", # "images": [ # "images/dog_a.png", # "images/dog_b.png", # "images/dog_c.png", # "images/dog_d.png", # ], # }, # {"role": "Assistant", "content": ""} # ] # load images and prepare for inputs pil_images = load_pil_images(conversation) prepare_inputs = vl_chat_processor( conversations=conversation, images=pil_images, force_batchify=True, system_prompt="" ).to(vl_gpt.device) # run image encoder to get the image embeddings inputs_embeds = vl_gpt.prepare_inputs_embeds(*prepare_inputs) # run the model to get the response outputs = vl_gpt.language_model.generate( inputs_embeds=inputs_embeds, attention_mask=prepare_inputs.attention_mask, pad_token_id=tokenizer.eos_token_id, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, max_new_tokens=512, do_sample=False, use_cache=True ) answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True) print(f"{prepare_inputs['sft_format'][0]}", answer) ``` ### Gradio Demo (TODO) ## 4. License This code repository is licensed under [MIT License](./LICENSE-CODE). The use of DeepSeek-VL2 models is subject to [DeepSeek Model License](./LICENSE-MODEL). DeepSeek-VL2 series supports commercial use. ## 5. Citation ``` @misc{wu2024deepseekvl2mixtureofexpertsvisionlanguagemodels, title={DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding}, author={Zhiyu Wu and Xiaokang Chen and Zizheng Pan and Xingchao Liu and Wen Liu and Damai Dai and Huazuo Gao and Yiyang Ma and Chengyue Wu and Bingxuan Wang and Zhenda Xie and Yu Wu and Kai Hu and Jiawei Wang and Yaofeng Sun and Yukun Li and Yishi Piao and Kang Guan and Aixin Liu and Xin Xie and Yuxiang You and Kai Dong and Xingkai Yu and Haowei Zhang and Liang Zhao and Yisong Wang and Chong Ruan}, year={2024}, eprint={2412.10302}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2412.10302}, } ``` ## 6. Contact If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).
mradermacher/dolphin-2.0-mistral-7b-GGUF	mradermacher	2024-12-18T08:08:22Z	19	0	transformers	[ "transformers", "gguf", "en", "dataset:ehartford/dolphin", "dataset:jondurbin/airoboros-2.2.1", "base_model:cognitivecomputations/dolphin-2.0-mistral-7b", "base_model:quantized:cognitivecomputations/dolphin-2.0-mistral-7b", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-17T18:48:29Z	--- base_model: cognitivecomputations/dolphin-2.0-mistral-7b datasets: - ehartford/dolphin - jondurbin/airoboros-2.2.1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/cognitivecomputations/dolphin-2.0-mistral-7b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/dolphin-2.0-mistral-7b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.0-mistral-7b-GGUF/resolve/main/dolphin-2.0-mistral-7b.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.0-mistral-7b-GGUF/resolve/main/dolphin-2.0-mistral-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.0-mistral-7b-GGUF/resolve/main/dolphin-2.0-mistral-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.0-mistral-7b-GGUF/resolve/main/dolphin-2.0-mistral-7b.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.0-mistral-7b-GGUF/resolve/main/dolphin-2.0-mistral-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.0-mistral-7b-GGUF/resolve/main/dolphin-2.0-mistral-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.0-mistral-7b-GGUF/resolve/main/dolphin-2.0-mistral-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.0-mistral-7b-GGUF/resolve/main/dolphin-2.0-mistral-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.0-mistral-7b-GGUF/resolve/main/dolphin-2.0-mistral-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.0-mistral-7b-GGUF/resolve/main/dolphin-2.0-mistral-7b.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.0-mistral-7b-GGUF/resolve/main/dolphin-2.0-mistral-7b.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.0-mistral-7b-GGUF/resolve/main/dolphin-2.0-mistral-7b.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
yuh0512/SmolLM2-360M-Instruct-tuningv9	yuh0512	2024-12-18T08:02:59Z	151	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T08:01:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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RyanYr/reflect_llm8B_om2-mstlrg300k460k-llm3370b130k-t12_SFTDPOt1_psdp-t1_b.5	RyanYr	2024-12-18T08:01:47Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:RyanYr/reflect_llm8B_om2-mstlrg-300k460k-t12_llm33-130k-t12_SFTt1-lr1e-6_DPOt1", "base_model:finetune:RyanYr/reflect_llm8B_om2-mstlrg-300k460k-t12_llm33-130k-t12_SFTt1-lr1e-6_DPOt1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T07:23:04Z	--- base_model: RyanYr/reflect_llm8B_om2-mstlrg-300k460k-t12_llm33-130k-t12_SFTt1-lr1e-6_DPOt1 library_name: transformers model_name: reflect_llm8B_om2-mstlrg300k460k-llm3370b130k-t12_SFTDPOt1_psdp-t1_b.5 tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for reflect_llm8B_om2-mstlrg300k460k-llm3370b130k-t12_SFTDPOt1_psdp-t1_b.5 This model is a fine-tuned version of [RyanYr/reflect_llm8B_om2-mstlrg-300k460k-t12_llm33-130k-t12_SFTt1-lr1e-6_DPOt1](https://huggingface.co/RyanYr/reflect_llm8B_om2-mstlrg-300k460k-t12_llm33-130k-t12_SFTt1-lr1e-6_DPOt1). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="RyanYr/reflect_llm8B_om2-mstlrg300k460k-llm3370b130k-t12_SFTDPOt1_psdp-t1_b.5", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/yyr/huggingface/runs/lqzsovrc) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.45.2 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Norod78/huggingface-emoji-flux	Norod78	2024-12-18T07:57:19Z	34	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "migrated", "character", "emoji", "yellow", "huggingface", "huggy", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-12-18T07:56:39Z	--- license: other license_name: bespoke-lora-trained-license license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=Image&allowDerivatives=True&allowDifferentLicense=False tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora - migrated - character - emoji - yellow - huggingface - huggy base_model: black-forest-labs/FLUX.1-dev instance_prompt: huggingface emoji widget: - text: 'The girl with a pearl earring huggingface emoji' output: url: >- 46171136.jpeg - text: 'A chubby huggingface emoji eating Spaghetti' output: url: >- 46171161.jpeg - text: 'A batman huggingface emoji' output: url: >- 46171174.jpeg - text: 'A pikachu huggingface emoji' output: url: >- 46171312.jpeg - text: 'A wonderwoman huggingface emoji' output: url: >- 46171315.jpeg --- # Huggingface Emoji [FLUX] <Gallery /> ([CivitAI](https://civitai.com/models/1048655)) ## Model description <p>Huggy, the Huggingface Mascot Emoji 🤗</p><p>Trained on <a target="_blank" rel="ugc" href="http://astria.ai">astria.ai</a></p><p>Use "huggingface emoji" in your prompts</p> ## Trigger words You should use `huggingface emoji` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Norod78/huggingface-emoji-flux/tree/main) them in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch device = "cuda" if torch.cuda.is_available() else "cpu" pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to(device) pipeline.load_lora_weights('Norod78/huggingface-emoji-flux', weight_name='Huggingface-Emoji_Flux-LoRA-1962967.safetensors') image = pipeline('A wonderwoman huggingface emoji').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)
poooj/test_model	poooj	2024-12-18T07:55:39Z	8	0	null	[ "pytorch", "tensorboard", "safetensors", "albert", "generated_from_trainer", "base_model:ai4bharat/indic-bert", "base_model:finetune:ai4bharat/indic-bert", "license:mit", "region:us" ]	null	2024-12-17T12:37:59Z	--- license: mit base_model: ai4bharat/indic-bert tags: - generated_from_trainer metrics: - accuracy model-index: - name: test_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. --> # test_model This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5251 - Accuracy: 0.7788 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 65 \| 0.5317 \| 0.7788 \| \| No log \| 2.0 \| 130 \| 0.5251 \| 0.7788 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cpu - Datasets 2.17.0 - Tokenizers 0.19.1
dgambettavuw/M_gen0_run0_llama2-7b_xlsum_doc1000_real64_synt64_vuw	dgambettavuw	2024-12-18T07:54:02Z	161	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-12-18T07:45:24Z	--- 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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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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ARAIcorp/sn29-0016m1-v002-beta	ARAIcorp	2024-12-18T07:54:00Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T07:47:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/NeuralKrishna-7B-V2-DPO-GGUF	mradermacher	2024-12-18T07:50:14Z	6	1	transformers	[ "transformers", "gguf", "en", "base_model:Kukedlc/NeuralKrishna-7B-V2-DPO", "base_model:quantized:Kukedlc/NeuralKrishna-7B-V2-DPO", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-12-17T18:38:33Z	--- base_model: Kukedlc/NeuralKrishna-7B-V2-DPO language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Kukedlc/NeuralKrishna-7B-V2-DPO <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/NeuralKrishna-7B-V2-DPO-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/NeuralKrishna-7B-V2-DPO-GGUF/resolve/main/NeuralKrishna-7B-V2-DPO.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKrishna-7B-V2-DPO-GGUF/resolve/main/NeuralKrishna-7B-V2-DPO.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKrishna-7B-V2-DPO-GGUF/resolve/main/NeuralKrishna-7B-V2-DPO.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKrishna-7B-V2-DPO-GGUF/resolve/main/NeuralKrishna-7B-V2-DPO.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKrishna-7B-V2-DPO-GGUF/resolve/main/NeuralKrishna-7B-V2-DPO.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKrishna-7B-V2-DPO-GGUF/resolve/main/NeuralKrishna-7B-V2-DPO.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKrishna-7B-V2-DPO-GGUF/resolve/main/NeuralKrishna-7B-V2-DPO.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKrishna-7B-V2-DPO-GGUF/resolve/main/NeuralKrishna-7B-V2-DPO.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKrishna-7B-V2-DPO-GGUF/resolve/main/NeuralKrishna-7B-V2-DPO.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKrishna-7B-V2-DPO-GGUF/resolve/main/NeuralKrishna-7B-V2-DPO.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKrishna-7B-V2-DPO-GGUF/resolve/main/NeuralKrishna-7B-V2-DPO.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralKrishna-7B-V2-DPO-GGUF/resolve/main/NeuralKrishna-7B-V2-DPO.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Nubletz/bert-simplestyle-split-embedding-recon-537	Nubletz	2024-12-18T07:47:09Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-12-18T05:16:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Hachipo/qwen2.5-0.5B_educational_instruct_top2000	Hachipo	2024-12-18T07:46:36Z	151	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T07:45:32Z	--- 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. 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isspek/bert-base-cased_covid_5_2e-5_16_undersampling_0.5	isspek	2024-12-18T07:39:14Z	165	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T07:38:56Z	--- 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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isspek/bert-base-cased_covid_2_2e-5_16_undersampling_0.5	isspek	2024-12-18T07:38:01Z	159	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T07:37:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isspek/bert-base-cased_covid_1_2e-5_16_undersampling_0.5	isspek	2024-12-18T07:37:36Z	116	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T07:37:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isspek/bert-base-cased_covid_5_2e-5_16_undersampling_0.4	isspek	2024-12-18T07:37:11Z	116	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T07:36:52Z	--- 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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isspek/bert-base-cased_covid_4_2e-5_16_undersampling_0.4	isspek	2024-12-18T07:36:46Z	108	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T07:36:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isspek/bert-base-cased_covid_3_2e-5_16_undersampling_0.4	isspek	2024-12-18T07:36:18Z	71	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T07:35:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isspek/bert-base-cased_covid_2_2e-5_16_undersampling_0.4	isspek	2024-12-18T07:35:53Z	59	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T07:35:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isspek/bert-base-cased_covid_1_2e-5_16_undersampling_0.4	isspek	2024-12-18T07:35:27Z	192	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T07:35:09Z	--- 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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nzm97/math_question_grade_detection_Bert_databalanced	nzm97	2024-12-18T07:34:54Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-large-uncased", "base_model:finetune:google-bert/bert-large-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T07:34:12Z	--- library_name: transformers license: apache-2.0 base_model: google-bert/bert-large-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: math_question_grade_detection_Bert_databalanced 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. --> # math_question_grade_detection_Bert_databalanced This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6880 - Accuracy: 0.7603 - Precision: 0.7651 - Recall: 0.7603 - F1: 0.7588 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| No log \| 0.2817 \| 50 \| 2.1003 \| 0.2349 \| 0.3799 \| 0.2349 \| 0.2106 \| \| No log \| 0.5634 \| 100 \| 1.9607 \| 0.2762 \| 0.3337 \| 0.2762 \| 0.2498 \| \| No log \| 0.8451 \| 150 \| 1.5031 \| 0.4778 \| 0.4633 \| 0.4778 \| 0.4591 \| \| No log \| 1.1268 \| 200 \| 1.2546 \| 0.5460 \| 0.5596 \| 0.5460 \| 0.5176 \| \| No log \| 1.4085 \| 250 \| 1.0941 \| 0.5746 \| 0.5804 \| 0.5746 \| 0.5675 \| \| No log \| 1.6901 \| 300 \| 0.9381 \| 0.6730 \| 0.6943 \| 0.6730 \| 0.6721 \| \| No log \| 1.9718 \| 350 \| 0.8974 \| 0.6619 \| 0.6822 \| 0.6619 \| 0.6570 \| \| No log \| 2.2535 \| 400 \| 0.8243 \| 0.6889 \| 0.6913 \| 0.6889 \| 0.6856 \| \| No log \| 2.5352 \| 450 \| 0.8219 \| 0.6937 \| 0.7131 \| 0.6937 \| 0.6881 \| \| 1.2537 \| 2.8169 \| 500 \| 0.7642 \| 0.7159 \| 0.7239 \| 0.7159 \| 0.7121 \| \| 1.2537 \| 3.0986 \| 550 \| 0.7580 \| 0.7175 \| 0.7197 \| 0.7175 \| 0.7068 \| \| 1.2537 \| 3.3803 \| 600 \| 0.7310 \| 0.7397 \| 0.7523 \| 0.7397 \| 0.7387 \| \| 1.2537 \| 3.6620 \| 650 \| 0.7562 \| 0.7413 \| 0.7466 \| 0.7413 \| 0.7349 \| \| 1.2537 \| 3.9437 \| 700 \| 0.6512 \| 0.7730 \| 0.7792 \| 0.7730 \| 0.7726 \| \| 1.2537 \| 4.2254 \| 750 \| 0.6941 \| 0.7476 \| 0.7484 \| 0.7476 \| 0.7447 \| \| 1.2537 \| 4.5070 \| 800 \| 0.6866 \| 0.7571 \| 0.7607 \| 0.7571 \| 0.7550 \| \| 1.2537 \| 4.7887 \| 850 \| 0.6942 \| 0.7603 \| 0.7644 \| 0.7603 \| 0.7588 \| \| 1.2537 \| 5.0704 \| 900 \| 0.7230 \| 0.7683 \| 0.7821 \| 0.7683 \| 0.7656 \| \| 1.2537 \| 5.3521 \| 950 \| 0.7123 \| 0.7603 \| 0.7669 \| 0.7603 \| 0.7588 \| \| 0.321 \| 5.6338 \| 1000 \| 0.6939 \| 0.7667 \| 0.7725 \| 0.7667 \| 0.7652 \| \| 0.321 \| 5.9155 \| 1050 \| 0.6884 \| 0.7667 \| 0.7723 \| 0.7667 \| 0.7657 \| \| 0.321 \| 6.1972 \| 1100 \| 0.6880 \| 0.7603 \| 0.7651 \| 0.7603 \| 0.7588 \| ### Framework versions - Transformers 4.46.3 - Pytorch 2.4.0 - Datasets 3.1.0 - Tokenizers 0.20.3
isspek/bert-base-cased_covid_4_2e-5_16_undersampling_0.3	isspek	2024-12-18T07:34:37Z	165	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T07:34:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dgambettavuw/M_gen0_run0_llama2-7b_wiki_doc1000_real64_synt64_vuw	dgambettavuw	2024-12-18T07:34:05Z	374	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-12-18T07:31:06Z	--- 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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tokyo-electron-device-ai/llama3-tedllm-8b-v0	tokyo-electron-device-ai	2024-12-18T07:26:34Z	24	0	null	[ "safetensors", "llama", "ja", "en", "arxiv:2407.12869", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:finetune:meta-llama/Meta-Llama-3-8B", "license:llama3", "region:us" ]	null	2024-10-17T01:39:08Z	--- license: llama3 language: - ja - en base_model: - meta-llama/Meta-Llama-3-8B --- # Llama3-tedllm-8B-v0 Llama3-tedllm-8b-v0 is a bilingual Japanese-English generative model built through continuous pre-training from [Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on approximately 173 billion tokens. This model is designed to enhance the Japanese language understanding and generation while preserving English proficiency of Llama-3. # How to use Below is a sample code to use this model for text generation. ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tokyo-electron-device-ai/llama3-tedllm-8b-v0") model = AutoModelForCausalLM.from_pretrained("tokyo-electron-device-ai/llama3-tedllm-8b-v0", device_map="auto", torch_dtype=torch.bfloat16) text = "人工知能とは何か説明してください" tokenized_input = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt").to(model.device) with torch.no_grad(): output = model.generate( tokenized_input, max_new_tokens=50, do_sample=True, top_p=0.9, temperature=0.6, )[0] print(tokenizer.decode(output)) ``` # Model Details * Developed by: TED, Cerebras Systems * Language(s): Japanese and English * Model architecture: Matches LLaMA-3 8B * License: Meta Llama 3 License * Trained from model: LLaMa-3 8B * Vocabulary size: 141,056 tokens * Context length: 8192 tokens * Input: text data * Output: model generates text # Intended Use & Limitations * Intended Use: This model is continuously pretrained using Llama-3-8B as the foundation. The model has not been exposed to instruction tuning data. It is designed for text generation tasks and can also be fine-tuned for specific downstream applications, making it suitable for a variety of users, including researchers, developers, and businesses. * Limitations: Despite its versatility, the model is trained on web-crawled datasets like mc4 and OSCAR, which may contain inaccuracies, biases, or harmful content. As a result, it can generate incorrect, misleading, or offensive outputs. Users should critically evaluate the results, especially in high-stakes or sensitive applications, and are responsible for ensuring compliance with legal and ethical standards. This model is a tool, not a source of truth, and its outputs should be verified in context. # Training Details ### Training process We follow the approach described in [Bilingual Adaptation of Monolingual Foundation Models](https://arxiv.org/abs/2407.12869) for training. - Starting with the Llama-3-8B base checkpoint, we extend the LLaMA-3 vocabulary by 10%, from 128,000 to 141,056 tokens, to increase a variety of Japanese Kanjis tokens. This improves Japanese tokenization efficiency by 21%. - We initialize newly added embeddings using similarity-based token embedding initialization. Added embedding vectors are initialized with a weighted average of embeddings of top K most similar tokens in the original LLaMA-3 vocabulary, using an external embedding. - We start with embedding-only training on 8.6B tokens, freezing the weights of all layers expect for the embedding and unembedding layers. - This is followed by full continuous pre-training on 164B tokens, where all model weights are updated. ### Training data This model was continuously trained on 173B tokens, with the training data consisting of 20% English and 80% Japanese. The raw Japanese data was filtered using scripts from [llm-jp-corpus repository](https://github.com/llm-jp/llm-jp-corpus). The following Japanese datasets were included into the training data mixture: * [legacy-datasets/mc4](https://huggingface.co/datasets/legacy-datasets/mc4) * [range3/cc100-ja](https://huggingface.co/datasets/range3/cc100-ja) * [if001/oscar_2023_filtered](https://huggingface.co/datasets/if001/oscar_2023_filtered) * [dumps.wikimedia.org](https://dumps.wikimedia.org/) Note: This released model was trained exclusively on open-source datasets. We also trained models using proprietary domain-specific data, but there are no plans to release those models. ### Hyper-parameters * batch_size: 720 * peak_learning_rate: 7.5e-5 * optimizer: AdamW * weight_decay: 0.1 * annealing_steps: 500 Note: We created another model name, llama3-tedllm-8b-v0-annealing as the model with the annealing_step applied. If you are interested, please check [here](https://huggingface.co/tokyo-electron-device-ai/llama3-tedllm-8b-v0-annealing). ### Training Infrastructure The model was trained on a Cerebras Wafer-Scale Cluster, using from 4 to 16 CS-3 systems during different phases of training. Training on the Cerebras Wafer-Scale Clusters leverages Cerebras' Weight Streaming execution paradigm, which simplifies the training of LLMs by disaggregating compute from memory used for model weights. This enables efficient scaling of training across multiple nodes using simple data parallelism. You can learn more about Cerebras Wafer-Scale clusters and Weight Streaming execution [here](https://8968533.fs1.hubspotusercontent-na1.net/hubfs/8968533/Virtual%20Booth%20Docs/CS%20Weight%20Streaming%20White%20Paper.pdf). ### Evaluation We conducted a comprehensive evaluation of [Llama3-tedllm-8b-v0-annealing](https://huggingface.co/tokyo-electron-device-ai/llama3-tedllm-8b-v0-annealing) and benchmarked it against other leading Japanese-English bilingual models. Considering evaluation results in both Japanese and English, our model performs on par with the best Japanese-English bilingual models of a similar size, while offering significantly higher tokenization efficiency, which leads to a substantial reduction in inference cost. - Japanese benchmark: [llm-jp-eval==1.4.1](https://github.com/llm-jp/llm-jp-eval/tree/v1.4.1%5D) - English benchmark: MMLU, BoolQ, Winogrande, Hellaswag #### Japanese Task Result \|Model\|EL\|FA\|HE\|MC\|MR\|NLI\|QA\|RC\|AVG\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \| Llama-3-8B \| 0.372 \| 0.254 \| 0.505 \| 0.647 \| 0.650 \| 0.634 \| 0.413 \| 0.868 \| 0.543 \| \| Llama3-tedllm-8b-v0 \| 0.384 \| 0.254 \| 0.540 \| 0.747 \| 0.680 \| 0.622 \| 0.507 \| 0.867 \| 0.575 \| \| Llama-3-Swallow-8B-v0.1 \| 0.407 \| 0.277 \| 0.525 \| 0.750 \| 0.720 \| 0.612 \| 0.522 \| 0.890 \| 0.588 \| \| Llama-3-ELYZA-JP-8B \| 0.461 \| 0.276 \| 0.485 \| 0.763 \| 0.700 \| 0.610 \| 0.491 \| 0.900 \| 0.586 \| #### English Task Result \|Model\| MMLU \| BoolQ \| Winogrande \| Hellaswag \| Average \| \|---\|---\|---\|---\|---\|---\| \| Llama-3-8B \| 0.622 \| 0.812 \| 0.728 \| 0.792 \| 0.738 \| \| Llama3-tedllm-8b-v0 \| 0.591 \| 0.826 \| 0.736 \| 0.770 \| 0.731 \| \| Llama-3-Swallow-8B-v0.1 \| 0.591 \| 0.824 \| 0.726 \| 0.772 \| 0.728 \| \| Llama-3-ELYZA-JP-8B \| 0.564 \| 0.824 \| 0.726 \| 0.772 \| 0.721 \| # Model Card Contact If you have any question, please feel free to contact [email protected].
dgambettavuw/M_llama2-7b	dgambettavuw	2024-12-18T07:25:35Z	91	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-12-18T07:23:21Z	--- 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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(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]
morichun/SmolLM2-FT-MyDataset	morichun	2024-12-18T07:21:57Z	146	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "smol-course", "module_1", "trl", "sft", "conversational", "base_model:HuggingFaceTB/SmolLM2-135M", "base_model:finetune:HuggingFaceTB/SmolLM2-135M", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T07:21:28Z	--- base_model: HuggingFaceTB/SmolLM2-135M library_name: transformers model_name: SmolLM2-FT-MyDataset tags: - generated_from_trainer - smol-course - module_1 - trl - sft licence: license --- # Model Card for SmolLM2-FT-MyDataset This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="morichun/SmolLM2-FT-MyDataset", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/morichu/your_project_name/runs/gowkea15) This model was trained with SFT. ### Framework versions - TRL: 0.13.0 - Transformers: 4.46.3 - Pytorch: 2.5.1+cu121 - Datasets: 3.2.0 - Tokenizers: 0.20.3 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
PrunaAI/tiiuae-Falcon3-3B-Instruct-1.58bit-bnb-8bit-smashed	PrunaAI	2024-12-18T07:18:03Z	5	0	null	[ "safetensors", "llama", "pruna-ai", "base_model:tiiuae/Falcon3-3B-Instruct-1.58bit", "base_model:quantized:tiiuae/Falcon3-3B-Instruct-1.58bit", "bitnet", "region:us" ]	null	2024-12-18T07:14:01Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: tiiuae/Falcon3-3B-Instruct-1.58bit metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer"> <img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with llm-int8. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo tiiuae/Falcon3-3B-Instruct-1.58bit installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/tiiuae-Falcon3-3B-Instruct-1.58bit-bnb-8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-3B-Instruct-1.58bit") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model tiiuae/Falcon3-3B-Instruct-1.58bit before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Do it by yourself [here](https://docs.pruna.ai/en/latest/setup/pip.html).
khilan-crest/twitter-roberta-base-sentiment-latest_18122024T124126	khilan-crest	2024-12-18T07:13:29Z	107	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:cardiffnlp/twitter-roberta-base-sentiment-latest", "base_model:finetune:cardiffnlp/twitter-roberta-base-sentiment-latest", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T07:12:32Z	--- library_name: transformers base_model: cardiffnlp/twitter-roberta-base-sentiment-latest tags: - generated_from_trainer metrics: - f1 model-index: - name: twitter-roberta-base-sentiment-latest_18122024T124126 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. --> # twitter-roberta-base-sentiment-latest_18122024T124126 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0695 - F1: 0.5782 - Learning Rate: 1e-07 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use adamw_hf 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: 200 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Rate \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-----:\| \| No log \| 1.0 \| 315 \| 1.0695 \| 0.5782 \| 1e-07 \| ### Framework versions - Transformers 4.46.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.20.3
T145/KRONOS-8B-V2	T145	2024-12-18T07:06:48Z	12	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:T145/KRONOS-8B-V1-P1", "base_model:merge:T145/KRONOS-8B-V1-P1", "base_model:T145/KRONOS-8B-V1-P2", "base_model:merge:T145/KRONOS-8B-V1-P2", "base_model:T145/KRONOS-8B-V1-P3", "base_model:merge:T145/KRONOS-8B-V1-P3", "base_model:unsloth/Meta-Llama-3.1-8B", "base_model:merge:unsloth/Meta-Llama-3.1-8B", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:merge:unsloth/Meta-Llama-3.1-8B-Instruct", "license:llama3.1", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-08T02:47:56Z	--- base_model: - unsloth/Meta-Llama-3.1-8B - T145/KRONOS-8B-V1-P2 - unsloth/Meta-Llama-3.1-8B-Instruct - T145/KRONOS-8B-V1-P1 - T145/KRONOS-8B-V1-P3 library_name: transformers tags: - mergekit - merge license: llama3.1 model-index: - name: KRONOS-8B-V2 results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 51.8 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=T145/KRONOS-8B-V2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 30.67 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=T145/KRONOS-8B-V2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 22.66 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=T145/KRONOS-8B-V2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 6.49 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=T145/KRONOS-8B-V2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 8.26 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=T145/KRONOS-8B-V2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 30.42 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=T145/KRONOS-8B-V2 name: Open LLM Leaderboard --- # KRONOS-8B-V2 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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [unsloth/Meta-Llama-3.1-8B](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) as a base. ### Models Merged The following models were included in the merge: * [T145/KRONOS-8B-V1-P2](https://huggingface.co/T145/KRONOS-8B-V1-P2) * [unsloth/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct) * [T145/KRONOS-8B-V1-P1](https://huggingface.co/T145/KRONOS-8B-V1-P1) * [T145/KRONOS-8B-V1-P3](https://huggingface.co/T145/KRONOS-8B-V1-P3) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: unsloth/Meta-Llama-3.1-8B dtype: bfloat16 merge_method: ties parameters: density: 1.0 weight: 1.0 slices: - sources: - layer_range: [0, 32] model: T145/KRONOS-8B-V1-P1 parameters: density: 1.0 weight: 1.0 - layer_range: [0, 32] model: T145/KRONOS-8B-V1-P2 parameters: density: 1.0 weight: 1.0 - layer_range: [0, 32] model: T145/KRONOS-8B-V1-P3 parameters: density: 1.0 weight: 1.0 - layer_range: [0, 32] model: unsloth/Meta-Llama-3.1-8B-Instruct parameters: density: 1.0 weight: 1.0 - layer_range: [0, 32] model: unsloth/Meta-Llama-3.1-8B tokenizer_source: unsloth/Meta-Llama-3.1-8B-Instruct ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/T145__KRONOS-8B-V2-details)! Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=T145/KRONOS-8B-V2)! \| Metric \|% Value\| \|-------------------\|------:\| \|Avg. \| 25.05\| \|IFEval (0-Shot) \| 51.80\| \|BBH (3-Shot) \| 30.67\| \|MATH Lvl 5 (4-Shot)\| 22.66\| \|GPQA (0-shot) \| 6.49\| \|MuSR (0-shot) \| 8.26\| \|MMLU-PRO (5-shot) \| 30.42\|
mradermacher/Mistral-Hermes-Support-Ties-GGUF	mradermacher	2024-12-18T07:02:43Z	38	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "mistralai/Mistral-7B-v0.1+predibase/customer_support", "NousResearch/Nous-Hermes-2-Mistral-7B-DPO", "en", "base_model:arcee-ai/Mistral-Hermes-Support-Ties", "base_model:quantized:arcee-ai/Mistral-Hermes-Support-Ties", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-18T04:31:17Z	--- base_model: arcee-ai/Mistral-Hermes-Support-Ties language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - mistralai/Mistral-7B-v0.1+predibase/customer_support - NousResearch/Nous-Hermes-2-Mistral-7B-DPO --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/arcee-ai/Mistral-Hermes-Support-Ties <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Hermes-Support-Ties-GGUF/resolve/main/Mistral-Hermes-Support-Ties.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Hermes-Support-Ties-GGUF/resolve/main/Mistral-Hermes-Support-Ties.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Hermes-Support-Ties-GGUF/resolve/main/Mistral-Hermes-Support-Ties.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Hermes-Support-Ties-GGUF/resolve/main/Mistral-Hermes-Support-Ties.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Hermes-Support-Ties-GGUF/resolve/main/Mistral-Hermes-Support-Ties.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Hermes-Support-Ties-GGUF/resolve/main/Mistral-Hermes-Support-Ties.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Hermes-Support-Ties-GGUF/resolve/main/Mistral-Hermes-Support-Ties.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Hermes-Support-Ties-GGUF/resolve/main/Mistral-Hermes-Support-Ties.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Hermes-Support-Ties-GGUF/resolve/main/Mistral-Hermes-Support-Ties.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Hermes-Support-Ties-GGUF/resolve/main/Mistral-Hermes-Support-Ties.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Hermes-Support-Ties-GGUF/resolve/main/Mistral-Hermes-Support-Ties.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-Hermes-Support-Ties-GGUF/resolve/main/Mistral-Hermes-Support-Ties.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Beagle14-7B-i1-GGUF	mradermacher	2024-12-18T06:59:18Z	157	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "fblgit/UNA-TheBeagle-7b-v1", "argilla/distilabeled-Marcoro14-7B-slerp", "en", "base_model:mlabonne/Beagle14-7B", "base_model:quantized:mlabonne/Beagle14-7B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-12-18T06:21:37Z	--- base_model: mlabonne/Beagle14-7B language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - fblgit/UNA-TheBeagle-7b-v1 - argilla/distilabeled-Marcoro14-7B-slerp --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/mlabonne/Beagle14-7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Beagle14-7B-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/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-Q2_K.gguf) \| i1-Q2_K \| 2.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Beagle14-7B-i1-GGUF/resolve/main/Beagle14-7B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/WestOrcaNeural-V2-DARETIES-7B-GGUF	mradermacher	2024-12-18T06:54:58Z	14	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP", "senseable/WestLake-7B-v2", "mlabonne/NeuralBeagle14-7B", "en", "base_model:jsfs11/WestOrcaNeural-V2-DARETIES-7B", "base_model:quantized:jsfs11/WestOrcaNeural-V2-DARETIES-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-12-18T06:07:55Z	--- base_model: jsfs11/WestOrcaNeural-V2-DARETIES-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP - senseable/WestLake-7B-v2 - mlabonne/NeuralBeagle14-7B --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/jsfs11/WestOrcaNeural-V2-DARETIES-7B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/WestOrcaNeural-V2-DARETIES-7B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/WestOrcaNeural-V2-DARETIES-7B-GGUF/resolve/main/WestOrcaNeural-V2-DARETIES-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/WestOrcaNeural-V2-DARETIES-7B-GGUF/resolve/main/WestOrcaNeural-V2-DARETIES-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/WestOrcaNeural-V2-DARETIES-7B-GGUF/resolve/main/WestOrcaNeural-V2-DARETIES-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/WestOrcaNeural-V2-DARETIES-7B-GGUF/resolve/main/WestOrcaNeural-V2-DARETIES-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/WestOrcaNeural-V2-DARETIES-7B-GGUF/resolve/main/WestOrcaNeural-V2-DARETIES-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/WestOrcaNeural-V2-DARETIES-7B-GGUF/resolve/main/WestOrcaNeural-V2-DARETIES-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/WestOrcaNeural-V2-DARETIES-7B-GGUF/resolve/main/WestOrcaNeural-V2-DARETIES-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/WestOrcaNeural-V2-DARETIES-7B-GGUF/resolve/main/WestOrcaNeural-V2-DARETIES-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/WestOrcaNeural-V2-DARETIES-7B-GGUF/resolve/main/WestOrcaNeural-V2-DARETIES-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/WestOrcaNeural-V2-DARETIES-7B-GGUF/resolve/main/WestOrcaNeural-V2-DARETIES-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/WestOrcaNeural-V2-DARETIES-7B-GGUF/resolve/main/WestOrcaNeural-V2-DARETIES-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/WestOrcaNeural-V2-DARETIES-7B-GGUF/resolve/main/WestOrcaNeural-V2-DARETIES-7B.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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 -->
sparkle1111/shirankedo-kto-7b-instruct	sparkle1111	2024-12-18T06:43:51Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T06:36:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mingxilei/distilbert-imdb-eps-0.1	mingxilei	2024-12-18T06:30:22Z	106	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-17T23:36:21Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-imdb-eps-0.1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-imdb-eps-0.1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0349 - Accuracy: 0.5 ## 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: 32 - eval_batch_size: 32 - 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0376 \| 1.0 \| 782 \| 0.0349 \| 0.5 \| ### Framework versions - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
davidrd123/JamesTissot-Flux-LoKr	davidrd123	2024-12-18T06:27:17Z	5	0	diffusers	[ "diffusers", "flux", "flux-diffusers", "text-to-image", "simpletuner", "safe-for-work", "lora", "template:sd-lora", "lycoris", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-12-17T09:56:24Z	--- license: other base_model: "black-forest-labs/FLUX.1-dev" tags: - flux - flux-diffusers - text-to-image - diffusers - simpletuner - safe-for-work - lora - template:sd-lora - lycoris inference: true widget: - text: 'unconditional (blank prompt)' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_0_0.png - text: 'In the style of a James Tissot painting, a woman in a black dress with white ruffled underlayers sits in a red chair, her posture relaxed. A black cat rests beside her, and a vase of white flowers sits on a nearby table. The room features a mirror and framed artwork.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png - text: 'In the style of a James Tissot painting, two women in light blue ruffled dresses stand in a luxurious room with large windows overlooking tropical plants. One pours tea at a small table while another sits nearby. The room contains ornate furniture, an intricate carpet, and a samovar.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_2_0.png - text: 'In the style of a James Tissot painting, a woman wearing a checkered dress sits at a breakfast table with a carafe and fruit, reading a letter. A man holds up a newspaper while ships are visible through large windows behind them.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_3_0.png - text: 'In the style of a James Tissot painting, a young woman practices piano in a conservatory, sunlight streaming through art nouveau windows onto her emerald green dress. Potted orchids line the walls, and sheet music scattered across the floor catches the late afternoon light.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_4_0.png - text: 'In the style of a James Tissot painting, two sisters prepare for a masquerade ball, one adjusting the other''s venetian mask while standing before a gilt mirror. Their elaborate dresses in complementary shades of burgundy and navy reflect in the candlelit room.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_5_0.png - text: 'In the style of a James Tissot painting, a lady artist works at her easel in a sunny studio, her paint-stained apron contrasting with her formal Victorian dress. Through the window, hot air balloons float above a cityscape of chimneys and spires.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_6_0.png - text: 'In the style of a James Tissot painting, a woman astronomer in a midnight blue Victorian dress with silver buttons studies the night sky through a brass telescope on an observatory balcony. Her detailed skirt catches moonlight as she leans forward, while star charts and astronomical instruments rest on a marble-topped table nearby. Through the domed ceiling''s opening, the Pleiades cluster shimmers above.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_7_0.png - text: 'In the style of a James Tissot painting, an elegant Japanese geisha in a coral and gold kimono serves tea to a Victorian lady wearing a lavender bustle dress in a fusion parlor. Wisteria cascades through the open shoji screens, while European oil paintings hang above Japanese tatami mats. A peacock fan rests on a lacquered table beside an English silver tea service.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_8_0.png --- # JamesTissot-Flux-LoKr This is a LyCORIS adapter derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). No validation prompt was used during training. None ## Validation settings - CFG: `3.0` - CFG Rescale: `0.0` - Steps: `20` - Sampler: `FlowMatchEulerDiscreteScheduler` - Seed: `42` - Resolution: `968x1280` - Skip-layer guidance: Note: The validation settings are not necessarily the same as the [training settings](#training-settings). You can find some example images in the following gallery: <Gallery /> The text encoder was not trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 9 - Training steps: 5000 - Learning rate: 0.0004 - Learning rate schedule: polynomial - Warmup steps: 200 - Max grad norm: 0.1 - Effective batch size: 3 - Micro-batch size: 3 - Gradient accumulation steps: 1 - Number of GPUs: 1 - Gradient checkpointing: True - Prediction type: flow-matching (extra parameters=['flux_schedule_auto_shift', 'shift=0.0', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flux_beta_schedule_alpha=10.0', 'flux_beta_schedule_beta=1.0', 'flow_matching_loss=compatible']) - Optimizer: adamw_bf16 - Trainable parameter precision: Pure BF16 - Caption dropout probability: 10.0% ### LyCORIS Config: ```json { "algo": "lokr", "multiplier": 1.0, "linear_dim": 10000, "linear_alpha": 1, "factor": 16, "apply_preset": { "target_module": [ "Attention", "FeedForward" ], "module_algo_map": { "Attention": { "factor": 16 }, "FeedForward": { "factor": 8 } } } } ``` ## Datasets ### ab-512 - Repeats: 11 - Total number of images: 29 - Total number of aspect buckets: 7 - Resolution: 0.262144 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### ab-768 - Repeats: 11 - Total number of images: 29 - Total number of aspect buckets: 9 - Resolution: 0.589824 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### ab-1024 - Repeats: 5 - Total number of images: 29 - Total number of aspect buckets: 11 - Resolution: 1.048576 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### ab-crops-512 - Repeats: 7 - Total number of images: 29 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: True - Crop style: random - Crop aspect: square - Used for regularisation data: No ### ab-1024-crop - Repeats: 7 - Total number of images: 29 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: random - Crop aspect: square - Used for regularisation data: No ## Inference ```python import torch from diffusers import DiffusionPipeline from lycoris import create_lycoris_from_weights def download_adapter(repo_id: str): import os from huggingface_hub import hf_hub_download adapter_filename = "pytorch_lora_weights.safetensors" cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models')) cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_") path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path) path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename) os.makedirs(path_to_adapter, exist_ok=True) hf_hub_download( repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter ) return path_to_adapter_file model_id = 'black-forest-labs/FLUX.1-dev' adapter_repo_id = 'davidrd123/JamesTissot-Flux-LoKr' adapter_filename = 'pytorch_lora_weights.safetensors' adapter_file_path = download_adapter(repo_id=adapter_repo_id) pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16 lora_scale = 1.0 wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer) wrapper.merge_to() prompt = "An astronaut is riding a horse through the jungles of Thailand." ## Optional: quantise the model to save on vram. ## Note: The model was quantised during training, and so it is recommended to do the same during inference time. from optimum.quanto import quantize, freeze, qint8 quantize(pipeline.transformer, weights=qint8) freeze(pipeline.transformer) pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level image = pipeline( prompt=prompt, num_inference_steps=20, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42), width=968, height=1280, guidance_scale=3.0, ).images[0] image.save("output.png", format="PNG") ```
mingxilei/distilbert-imdb-eps-1	mingxilei	2024-12-18T06:25:23Z	106	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-17T23:16:38Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-imdb-eps-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-imdb-eps-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0979 - Accuracy: 0.8997 ## 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: 32 - eval_batch_size: 32 - 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1677 \| 1.0 \| 782 \| 0.0979 \| 0.8997 \| ### Framework versions - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
omlab/omdet-turbo-swin-tiny-hf	omlab	2024-12-18T06:19:10Z	45,453	33	null	[ "safetensors", "omdet-turbo", "zero-shot-object-detection", "arxiv:2403.06892", "license:apache-2.0", "region:us" ]	zero-shot-object-detection	2024-09-24T16:52:34Z	--- license: apache-2.0 pipeline_tag: zero-shot-object-detection --- # OmDet model The OmDet model was proposed in [Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head](https://arxiv.org/abs/2403.06892) by Tiancheng Zhao, Peng Liu, Xuan He, Lu Zhang, Kyusong Lee from Om AI Lab. # Github Repository If you like our model, please consider following our project on GitHub [OmDet](https://github.com/om-ai-lab/OmDet) to receive updates and information about new model releases. We also invite you to explore our latest work on the Agents Framework [OmAgent](https://github.com/om-ai-lab/OmAgent). # Intended use cases This model is intended for zero-shot (also called open-vocabulary) object detection. # Usage ## Single image inference Here's how to load the model and prepare the inputs to perform zero-shot object detection on a single image: ```python import requests from PIL import Image from transformers import AutoProcessor, OmDetTurboForObjectDetection processor = AutoProcessor.from_pretrained("omlab/omdet-turbo-swin-tiny-hf") model = OmDetTurboForObjectDetection.from_pretrained("omlab/omdet-turbo-swin-tiny-hf") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) classes = ["cat", "remote"] inputs = processor(image, text=classes, return_tensors="pt") outputs = model(inputs) # convert outputs (bounding boxes and class logits) results = processor.post_process_grounded_object_detection( outputs, classes=classes, target_sizes=[image.size[::-1]], score_threshold=0.3, nms_threshold=0.3, )[0] for score, class_name, box in zip( results["scores"], results["classes"], results["boxes"] ): box = [round(i, 1) for i in box.tolist()] print( f"Detected {class_name} with confidence " f"{round(score.item(), 2)} at location {box}" ) ``` ## Batched images inference OmDet-Turbo can perform batched multi-image inference, with support for different text prompts and classes in the same batch: ```python >>> import torch >>> import requests >>> from io import BytesIO >>> from PIL import Image >>> from transformers import AutoProcessor, OmDetTurboForObjectDetection >>> processor = AutoProcessor.from_pretrained("omlab/omdet-turbo-swin-tiny-hf") >>> model = OmDetTurboForObjectDetection.from_pretrained("omlab/omdet-turbo-swin-tiny-hf") >>> url1 = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image1 = Image.open(BytesIO(requests.get(url1).content)).convert("RGB") >>> classes1 = ["cat", "remote"] >>> task1 = "Detect {}.".format(", ".join(classes1)) >>> url2 = "http://images.cocodataset.org/train2017/000000257813.jpg" >>> image2 = Image.open(BytesIO(requests.get(url2).content)).convert("RGB") >>> classes2 = ["boat"] >>> task2 = "Detect everything that looks like a boat." >>> url3 = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" >>> image3 = Image.open(BytesIO(requests.get(url3).content)).convert("RGB") >>> classes3 = ["statue", "trees"] >>> task3 = "Focus on the foreground, detect statue and trees." >>> inputs = processor( ... images=[image1, image2, image3], ... text=[classes1, classes2, classes3], ... task=[task1, task2, task3], ... return_tensors="pt", ... ) >>> with torch.no_grad(): ... outputs = model(inputs) >>> # convert outputs (bounding boxes and class logits) >>> results = processor.post_process_grounded_object_detection( ... outputs, ... classes=[classes1, classes2, classes3], ... target_sizes=[image1.size[::-1], image2.size[::-1], image3.size[::-1]], ... score_threshold=0.2, ... nms_threshold=0.3, ... ) >>> for i, result in enumerate(results): ... for score, class_name, box in zip( ... result["scores"], result["classes"], result["boxes"] ... ): ... box = [round(i, 1) for i in box.tolist()] ... print( ... f"Detected {class_name} with confidence " ... f"{round(score.item(), 2)} at location {box} in image {i}" ... ) Detected remote with confidence 0.77 at location [39.9, 70.4, 176.7, 118.0] in image 0 Detected cat with confidence 0.72 at location [11.6, 54.2, 314.8, 474.0] in image 0 Detected remote with confidence 0.56 at location [333.4, 75.8, 370.7, 187.0] in image 0 Detected cat with confidence 0.55 at location [345.2, 24.0, 639.8, 371.7] in image 0 Detected boat with confidence 0.32 at location [146.9, 219.8, 209.6, 250.7] in image 1 Detected boat with confidence 0.3 at location [319.1, 223.2, 403.2, 238.4] in image 1 Detected boat with confidence 0.27 at location [37.7, 220.3, 84.0, 235.9] in image 1 Detected boat with confidence 0.22 at location [407.9, 207.0, 441.7, 220.2] in image 1 Detected statue with confidence 0.73 at location [544.7, 210.2, 651.9, 502.8] in image 2 Detected trees with confidence 0.25 at location [3.9, 584.3, 391.4, 785.6] in image 2 Detected trees with confidence 0.25 at location [1.4, 621.2, 118.2, 787.8] in image 2 Detected statue with confidence 0.2 at location [428.1, 205.5, 767.3, 759.5] in image 2 ```
mradermacher/SmolTulu-1.7b-Reinforced-GGUF	mradermacher	2024-12-18T06:12:20Z	61	0	transformers	[ "transformers", "gguf", "Tulu3", "Smollm", "SLMs", "Small", "Huggingface", "Allenai", "SFT", "DPO", "GGUF", "RLVR", "RL", "en", "dataset:allenai/RLVR-GSM-MATH-IF-Mixed-Constraints", "base_model:SultanR/SmolTulu-1.7b-Reinforced", "base_model:quantized:SultanR/SmolTulu-1.7b-Reinforced", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-17T06:20:54Z	--- base_model: SultanR/SmolTulu-1.7b-Reinforced datasets: - allenai/RLVR-GSM-MATH-IF-Mixed-Constraints language: - en library_name: transformers license: apache-2.0 no_imatrix: nan detected in blk.8.attn_q.weight quantized_by: mradermacher tags: - Tulu3 - Smollm - SLMs - Small - Huggingface - Allenai - SFT - DPO - GGUF - RLVR - RL --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/SultanR/SmolTulu-1.7b-Reinforced <!-- provided-files --> ## 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/SmolTulu-1.7b-Reinforced-GGUF/resolve/main/SmolTulu-1.7b-Reinforced.Q2_K.gguf) \| Q2_K \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/SmolTulu-1.7b-Reinforced-GGUF/resolve/main/SmolTulu-1.7b-Reinforced.Q3_K_S.gguf) \| Q3_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/SmolTulu-1.7b-Reinforced-GGUF/resolve/main/SmolTulu-1.7b-Reinforced.Q3_K_M.gguf) \| Q3_K_M \| 1.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/SmolTulu-1.7b-Reinforced-GGUF/resolve/main/SmolTulu-1.7b-Reinforced.Q3_K_L.gguf) \| Q3_K_L \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/SmolTulu-1.7b-Reinforced-GGUF/resolve/main/SmolTulu-1.7b-Reinforced.IQ4_XS.gguf) \| IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/SmolTulu-1.7b-Reinforced-GGUF/resolve/main/SmolTulu-1.7b-Reinforced.Q4_K_S.gguf) \| Q4_K_S \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SmolTulu-1.7b-Reinforced-GGUF/resolve/main/SmolTulu-1.7b-Reinforced.Q4_K_M.gguf) \| Q4_K_M \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SmolTulu-1.7b-Reinforced-GGUF/resolve/main/SmolTulu-1.7b-Reinforced.Q5_K_S.gguf) \| Q5_K_S \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/SmolTulu-1.7b-Reinforced-GGUF/resolve/main/SmolTulu-1.7b-Reinforced.Q5_K_M.gguf) \| Q5_K_M \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/SmolTulu-1.7b-Reinforced-GGUF/resolve/main/SmolTulu-1.7b-Reinforced.Q6_K.gguf) \| Q6_K \| 1.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/SmolTulu-1.7b-Reinforced-GGUF/resolve/main/SmolTulu-1.7b-Reinforced.Q8_0.gguf) \| Q8_0 \| 1.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/SmolTulu-1.7b-Reinforced-GGUF/resolve/main/SmolTulu-1.7b-Reinforced.f16.gguf) \| f16 \| 3.5 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
CheeLi03/whisper-base-th-puct-5k	CheeLi03	2024-12-18T06:10:07Z	87	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "nl", "dataset:fleurs", "base_model:openai/whisper-base", "base_model:finetune:openai/whisper-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-12-18T03:17:04Z	--- base_model: openai/whisper-base datasets: - fleurs language: - nl library_name: transformers license: apache-2.0 metrics: - wer tags: - hf-asr-leaderboard - generated_from_trainer model-index: - name: Whisper Base Dutch Punctuation 5k - Chee Li results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Google Fleurs type: fleurs config: th_th split: None args: 'config: nl split: test' metrics: - type: wer value: 196.69912134884825 name: Wer --- <!-- 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 Base Dutch Punctuation 5k - Chee Li This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Google Fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.6304 - Wer: 196.6991 ## 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: 16 - 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 - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 0.1341 \| 5.2356 \| 1000 \| 0.4705 \| 244.7875 \| \| 0.0167 \| 10.4712 \| 2000 \| 0.5314 \| 231.2040 \| \| 0.0025 \| 15.7068 \| 3000 \| 0.5875 \| 214.1297 \| \| 0.0013 \| 20.9424 \| 4000 \| 0.6187 \| 199.7863 \| \| 0.001 \| 26.1780 \| 5000 \| 0.6304 \| 196.6991 \| ### Framework versions - Transformers 4.46.2 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.20.3
ChenglongShi/small_zh2en_translate_model	ChenglongShi	2024-12-18T06:02:42Z	31	0	transformers	[ "transformers", "pytorch", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/Llama-3.2-3B-Instruct-bnb-4bit", "base_model:quantized:unsloth/Llama-3.2-3B-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "8-bit", "region:us", "conversational" ]	null	2024-12-12T08:27:08Z	--- base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: ChenglongShi - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-3B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
sumeyya/segformer-b0-finetuned-Eduardo-food103-GOOGLE100	sumeyya	2024-12-18T06:01:34Z	194	0	transformers	[ "transformers", "tensorboard", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	image-segmentation	2024-12-16T12:21:27Z	--- library_name: transformers license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-Eduardo-food103-GOOGLE100 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. --> # segformer-b0-finetuned-Eduardo-food103-GOOGLE100 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the EduardoPacheco/FoodSeg103 dataset. It achieves the following results on the evaluation set: - Loss: 2.0865 - Mean Iou: 0.0515 - Mean Accuracy: 0.1257 - Overall Accuracy: 0.2045 - Accuracy Background: nan - Accuracy Candy: nan - Accuracy Egg tart: nan - Accuracy French fries: 0.0 - Accuracy Chocolate: nan - Accuracy Biscuit: nan - Accuracy Popcorn: nan - Accuracy Pudding: nan - Accuracy Ice cream: 0.0 - Accuracy Cheese butter: 0.0 - Accuracy Cake: 0.0 - Accuracy Wine: 0.0 - Accuracy Milkshake: nan - Accuracy Coffee: nan - Accuracy Juice: 0.0 - Accuracy Milk: nan - Accuracy Tea: nan - Accuracy Almond: nan - Accuracy Red beans: nan - Accuracy Cashew: nan - Accuracy Dried cranberries: nan - Accuracy Soy: nan - Accuracy Walnut: nan - Accuracy Peanut: nan - Accuracy Egg: nan - Accuracy Apple: nan - Accuracy Date: nan - Accuracy Apricot: nan - Accuracy Avocado: nan - Accuracy Banana: nan - Accuracy Strawberry: nan - Accuracy Cherry: nan - Accuracy Blueberry: nan - Accuracy Raspberry: nan - Accuracy Mango: nan - Accuracy Olives: nan - Accuracy Peach: 0.0 - Accuracy Lemon: nan - Accuracy Pear: nan - Accuracy Fig: nan - Accuracy Pineapple: nan - Accuracy Grape: nan - Accuracy Kiwi: nan - Accuracy Melon: nan - Accuracy Orange: 0.0 - Accuracy Watermelon: nan - Accuracy Steak: 0.8548 - Accuracy Pork: 0.5362 - Accuracy Chicken duck: 0.3458 - Accuracy Sausage: 0.0 - Accuracy Fried meat: nan - Accuracy Lamb: 0.0 - Accuracy Sauce: 0.0 - Accuracy Crab: nan - Accuracy Fish: nan - Accuracy Shellfish: 0.0 - Accuracy Shrimp: 0.0 - Accuracy Soup: 0.0 - Accuracy Bread: 0.0157 - Accuracy Corn: 0.0 - Accuracy Hamburg: nan - Accuracy Pizza: nan - Accuracy hanamaki baozi: 0.0 - Accuracy Wonton dumplings: nan - Accuracy Pasta: nan - Accuracy Noodles: 0.2191 - Accuracy Rice: 0.3396 - Accuracy Pie: 0.0 - Accuracy Tofu: 0.0 - Accuracy Eggplant: nan - Accuracy Potato: 0.6707 - Accuracy Garlic: nan - Accuracy Cauliflower: 0.0 - Accuracy Tomato: 0.0295 - Accuracy Kelp: nan - Accuracy Seaweed: nan - Accuracy Spring onion: 0.0 - Accuracy Rape: 0.0 - Accuracy Ginger: nan - Accuracy Okra: 0.0 - Accuracy Lettuce: 0.0014 - Accuracy Pumpkin: nan - Accuracy Cucumber: 0.2728 - Accuracy White radish: 0.0 - Accuracy Carrot: 0.9345 - Accuracy Asparagus: nan - Accuracy Bamboo shoots: nan - Accuracy Broccoli: 0.7618 - Accuracy Celery stick: 0.0400 - Accuracy Cilantro mint: 0.0 - Accuracy Snow peas: nan - Accuracy cabbage: nan - Accuracy Bean sprouts: nan - Accuracy Onion: 0.0075 - Accuracy Pepper: nan - Accuracy Green beans: nan - Accuracy French beans: nan - Accuracy King oyster mushroom: nan - Accuracy Shiitake: nan - Accuracy Enoki mushroom: nan - Accuracy Oyster mushroom: nan - Accuracy White button mushroom: 0.0 - Accuracy Salad: nan - Accuracy Other ingredients: 0.0 - Iou Background: 0.0 - Iou Candy: nan - Iou Egg tart: nan - Iou French fries: 0.0 - Iou Chocolate: nan - Iou Biscuit: 0.0 - Iou Popcorn: nan - Iou Pudding: nan - Iou Ice cream: 0.0 - Iou Cheese butter: 0.0 - Iou Cake: 0.0 - Iou Wine: 0.0 - Iou Milkshake: nan - Iou Coffee: nan - Iou Juice: 0.0 - Iou Milk: nan - Iou Tea: nan - Iou Almond: nan - Iou Red beans: nan - Iou Cashew: nan - Iou Dried cranberries: nan - Iou Soy: nan - Iou Walnut: nan - Iou Peanut: nan - Iou Egg: nan - Iou Apple: nan - Iou Date: nan - Iou Apricot: nan - Iou Avocado: nan - Iou Banana: nan - Iou Strawberry: nan - Iou Cherry: nan - Iou Blueberry: nan - Iou Raspberry: nan - Iou Mango: nan - Iou Olives: nan - Iou Peach: 0.0 - Iou Lemon: nan - Iou Pear: nan - Iou Fig: nan - Iou Pineapple: nan - Iou Grape: nan - Iou Kiwi: nan - Iou Melon: nan - Iou Orange: 0.0 - Iou Watermelon: nan - Iou Steak: 0.1109 - Iou Pork: 0.2326 - Iou Chicken duck: 0.1176 - Iou Sausage: 0.0 - Iou Fried meat: nan - Iou Lamb: 0.0 - Iou Sauce: 0.0 - Iou Crab: nan - Iou Fish: nan - Iou Shellfish: 0.0 - Iou Shrimp: 0.0 - Iou Soup: 0.0 - Iou Bread: 0.0065 - Iou Corn: 0.0 - Iou Hamburg: nan - Iou Pizza: nan - Iou hanamaki baozi: 0.0 - Iou Wonton dumplings: nan - Iou Pasta: nan - Iou Noodles: 0.1944 - Iou Rice: 0.2630 - Iou Pie: 0.0 - Iou Tofu: 0.0 - Iou Eggplant: nan - Iou Potato: 0.2078 - Iou Garlic: nan - Iou Cauliflower: 0.0 - Iou Tomato: 0.0283 - Iou Kelp: nan - Iou Seaweed: nan - Iou Spring onion: 0.0 - Iou Rape: 0.0 - Iou Ginger: nan - Iou Okra: 0.0 - Iou Lettuce: 0.0010 - Iou Pumpkin: nan - Iou Cucumber: 0.1036 - Iou White radish: 0.0 - Iou Carrot: 0.4668 - Iou Asparagus: nan - Iou Bamboo shoots: nan - Iou Broccoli: 0.3830 - Iou Celery stick: 0.0400 - Iou Cilantro mint: 0.0 - Iou Snow peas: nan - Iou cabbage: nan - Iou Bean sprouts: nan - Iou Onion: 0.0073 - Iou Pepper: nan - Iou Green beans: nan - Iou French beans: nan - Iou King oyster mushroom: nan - Iou Shiitake: nan - Iou Enoki mushroom: nan - Iou Oyster mushroom: nan - Iou White button mushroom: 0.0 - Iou Salad: nan - Iou Other ingredients: 0.0 ## 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: 12 - eval_batch_size: 12 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Background \| Accuracy Candy \| Accuracy Egg tart \| Accuracy French fries \| Accuracy Chocolate \| Accuracy Biscuit \| Accuracy Popcorn \| Accuracy Pudding \| Accuracy Ice cream \| Accuracy Cheese butter \| Accuracy Cake \| Accuracy Wine \| Accuracy Milkshake \| Accuracy Coffee \| Accuracy Juice \| Accuracy Milk \| Accuracy Tea \| Accuracy Almond \| Accuracy Red beans \| Accuracy Cashew \| Accuracy Dried cranberries \| Accuracy Soy \| Accuracy Walnut \| Accuracy Peanut \| Accuracy Egg \| Accuracy Apple \| Accuracy Date \| Accuracy Apricot \| Accuracy Avocado \| Accuracy Banana \| Accuracy Strawberry \| Accuracy Cherry \| Accuracy Blueberry \| Accuracy Raspberry \| Accuracy Mango \| Accuracy Olives \| Accuracy Peach \| Accuracy Lemon \| Accuracy Pear \| Accuracy Fig \| Accuracy Pineapple \| Accuracy Grape \| Accuracy Kiwi \| Accuracy Melon \| Accuracy Orange \| Accuracy Watermelon \| Accuracy Steak \| Accuracy Pork \| Accuracy Chicken duck \| Accuracy Sausage \| Accuracy Fried meat \| Accuracy Lamb \| Accuracy Sauce \| Accuracy Crab \| Accuracy Fish \| Accuracy Shellfish \| Accuracy Shrimp \| Accuracy Soup \| Accuracy Bread \| Accuracy Corn \| Accuracy Hamburg \| Accuracy Pizza \| Accuracy hanamaki baozi \| Accuracy Wonton dumplings \| Accuracy Pasta \| Accuracy Noodles \| Accuracy Rice \| Accuracy Pie \| Accuracy Tofu \| Accuracy Eggplant \| Accuracy Potato \| Accuracy Garlic \| Accuracy Cauliflower \| Accuracy Tomato \| Accuracy Kelp \| Accuracy Seaweed \| Accuracy Spring onion \| Accuracy Rape \| Accuracy Ginger \| Accuracy Okra \| Accuracy Lettuce \| Accuracy Pumpkin \| Accuracy Cucumber \| Accuracy White radish \| Accuracy Carrot \| Accuracy Asparagus \| Accuracy Bamboo shoots \| Accuracy Broccoli \| Accuracy Celery stick \| Accuracy Cilantro mint \| Accuracy Snow peas \| Accuracy cabbage \| Accuracy Bean sprouts \| Accuracy Onion \| Accuracy Pepper \| Accuracy Green beans \| Accuracy French beans \| Accuracy King oyster mushroom \| Accuracy Shiitake \| Accuracy Enoki mushroom \| Accuracy Oyster mushroom \| Accuracy White button mushroom \| Accuracy Salad \| Accuracy Other ingredients \| Iou Background \| Iou Candy \| Iou Egg tart \| Iou French fries \| Iou Chocolate \| Iou Biscuit \| Iou Popcorn \| Iou Pudding \| Iou Ice cream \| Iou Cheese butter \| Iou Cake \| Iou Wine \| Iou Milkshake \| Iou Coffee \| Iou Juice \| Iou Milk \| Iou Tea \| Iou Almond \| Iou Red beans \| Iou Cashew \| Iou Dried cranberries \| Iou Soy \| Iou Walnut \| Iou Peanut \| Iou Egg \| Iou Apple \| Iou Date \| Iou Apricot \| Iou Avocado \| Iou Banana \| Iou Strawberry \| Iou Cherry \| Iou Blueberry \| Iou Raspberry \| Iou Mango \| Iou Olives \| Iou Peach \| Iou Lemon \| Iou Pear \| Iou Fig \| Iou Pineapple \| Iou Grape \| Iou Kiwi \| Iou Melon \| Iou Orange \| Iou Watermelon \| Iou Steak \| Iou Pork \| Iou Chicken duck \| Iou Sausage \| Iou Fried meat \| Iou Lamb \| Iou Sauce \| Iou Crab \| Iou Fish \| Iou Shellfish \| Iou Shrimp \| Iou Soup \| Iou Bread \| Iou Corn \| Iou Hamburg \| Iou Pizza \| Iou hanamaki baozi \| Iou Wonton dumplings \| Iou Pasta \| Iou Noodles \| Iou Rice \| Iou Pie \| Iou Tofu \| Iou Eggplant \| Iou Potato \| Iou Garlic \| Iou Cauliflower \| Iou Tomato \| Iou Kelp \| Iou Seaweed \| Iou Spring onion \| Iou Rape \| Iou Ginger \| Iou Okra \| Iou Lettuce \| Iou Pumpkin \| Iou Cucumber \| Iou White radish \| Iou Carrot \| Iou Asparagus \| Iou Bamboo shoots \| Iou Broccoli \| Iou Celery stick \| Iou Cilantro mint \| Iou Snow peas \| Iou cabbage \| Iou Bean sprouts \| Iou Onion \| Iou Pepper \| Iou Green beans \| Iou French beans \| Iou King oyster mushroom \| Iou Shiitake \| Iou Enoki mushroom \| Iou Oyster mushroom \| Iou White button mushroom \| Iou Salad \| Iou Other ingredients \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:--------------:\|:-----------------:\|:---------------------:\|:------------------:\|:----------------:\|:----------------:\|:----------------:\|:------------------:\|:----------------------:\|:-------------:\|:-------------:\|:------------------:\|:---------------:\|:--------------:\|:-------------:\|:------------:\|:---------------:\|:------------------:\|:---------------:\|:--------------------------:\|:------------:\|:---------------:\|:---------------:\|:------------:\|:--------------:\|:-------------:\|:----------------:\|:----------------:\|:---------------:\|:-------------------:\|:---------------:\|:------------------:\|:------------------:\|:--------------:\|:---------------:\|:--------------:\|:--------------:\|:-------------:\|:------------:\|:------------------:\|:--------------:\|:-------------:\|:--------------:\|:---------------:\|:-------------------:\|:--------------:\|:-------------:\|:---------------------:\|:----------------:\|:-------------------:\|:-------------:\|:--------------:\|:-------------:\|:-------------:\|:------------------:\|:---------------:\|:-------------:\|:--------------:\|:-------------:\|:----------------:\|:--------------:\|:------------------------:\|:-------------------------:\|:--------------:\|:----------------:\|:-------------:\|:------------:\|:-------------:\|:-----------------:\|:---------------:\|:---------------:\|:--------------------:\|:---------------:\|:-------------:\|:----------------:\|:---------------------:\|:-------------:\|:---------------:\|:-------------:\|:----------------:\|:----------------:\|:-----------------:\|:---------------------:\|:---------------:\|:------------------:\|:----------------------:\|:-----------------:\|:---------------------:\|:----------------------:\|:------------------:\|:-----------------:\|:---------------------:\|:--------------:\|:---------------:\|:--------------------:\|:---------------------:\|:-----------------------------:\|:-----------------:\|:-----------------------:\|:------------------------:\|:------------------------------:\|:--------------:\|:--------------------------:\|:--------------:\|:---------:\|:------------:\|:----------------:\|:-------------:\|:-----------:\|:-----------:\|:-----------:\|:-------------:\|:-----------------:\|:--------:\|:--------:\|:-------------:\|:----------:\|:---------:\|:--------:\|:-------:\|:----------:\|:-------------:\|:----------:\|:---------------------:\|:-------:\|:----------:\|:----------:\|:-------:\|:---------:\|:--------:\|:-----------:\|:-----------:\|:----------:\|:--------------:\|:----------:\|:-------------:\|:-------------:\|:---------:\|:----------:\|:---------:\|:---------:\|:--------:\|:-------:\|:-------------:\|:---------:\|:--------:\|:---------:\|:----------:\|:--------------:\|:---------:\|:--------:\|:----------------:\|:-----------:\|:--------------:\|:--------:\|:---------:\|:--------:\|:--------:\|:-------------:\|:----------:\|:--------:\|:---------:\|:--------:\|:-----------:\|:---------:\|:-------------------:\|:--------------------:\|:---------:\|:-----------:\|:--------:\|:-------:\|:--------:\|:------------:\|:----------:\|:----------:\|:---------------:\|:----------:\|:--------:\|:-----------:\|:----------------:\|:--------:\|:----------:\|:--------:\|:-----------:\|:-----------:\|:------------:\|:----------------:\|:----------:\|:-------------:\|:-----------------:\|:------------:\|:----------------:\|:-----------------:\|:-------------:\|:------------:\|:----------------:\|:---------:\|:----------:\|:---------------:\|:----------------:\|:------------------------:\|:------------:\|:------------------:\|:-------------------:\|:-------------------------:\|:---------:\|:---------------------:\| \| 2.7669 \| 14.2857 \| 100 \| 2.5982 \| 0.0437 \| 0.1131 \| 0.1847 \| nan \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| 0.9470 \| 0.0774 \| 0.0606 \| 0.0 \| nan \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0843 \| 0.0 \| nan \| nan \| 0.0 \| nan \| nan \| 0.0 \| 0.6846 \| 0.0 \| 0.0 \| nan \| 0.5680 \| nan \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| nan \| 0.3424 \| 0.0 \| 0.9617 \| nan \| nan \| 0.7870 \| 0.0112 \| 0.0003 \| nan \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| nan \| 0.0 \| nan \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| 0.0730 \| 0.0747 \| 0.0481 \| 0.0 \| nan \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0302 \| 0.0 \| nan \| nan \| 0.0 \| nan \| nan \| 0.0 \| 0.6074 \| 0.0 \| 0.0 \| nan \| 0.2870 \| nan \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| nan \| 0.0955 \| 0.0 \| 0.3976 \| nan \| nan \| 0.2527 \| 0.0110 \| 0.0003 \| nan \| nan \| nan \| 0.0 \| nan \| 0.0 \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| 0.0 \| \| 1.8931 \| 28.5714 \| 200 \| 2.2174 \| 0.0416 \| 0.1085 \| 0.1951 \| nan \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| 0.4517 \| 0.5716 \| 0.0521 \| 0.0 \| nan \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0020 \| 0.0 \| nan \| nan \| 0.0 \| nan \| nan \| 0.1063 \| 0.2845 \| 0.0 \| 0.0 \| nan \| 0.8817 \| nan \| 0.0 \| 0.0162 \| nan \| nan \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0074 \| nan \| 0.2246 \| 0.0 \| 0.9538 \| nan \| nan \| 0.7794 \| 0.0013 \| 0.0037 \| nan \| nan \| nan \| 0.0026 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| nan \| 0.0 \| nan \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| 0.0589 \| 0.2125 \| 0.0357 \| 0.0 \| nan \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0009 \| 0.0 \| nan \| nan \| 0.0 \| nan \| nan \| 0.1060 \| 0.2194 \| 0.0 \| 0.0 \| nan \| 0.2077 \| nan \| 0.0 \| 0.0159 \| nan \| nan \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0059 \| nan \| 0.0737 \| 0.0 \| 0.4452 \| nan \| nan \| 0.3571 \| 0.0013 \| 0.0036 \| nan \| nan \| nan \| 0.0025 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| 0.0 \| \| 1.5479 \| 42.8571 \| 300 \| 2.0865 \| 0.0515 \| 0.1257 \| 0.2045 \| nan \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| 0.8548 \| 0.5362 \| 0.3458 \| 0.0 \| nan \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0157 \| 0.0 \| nan \| nan \| 0.0 \| nan \| nan \| 0.2191 \| 0.3396 \| 0.0 \| 0.0 \| nan \| 0.6707 \| nan \| 0.0 \| 0.0295 \| nan \| nan \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0014 \| nan \| 0.2728 \| 0.0 \| 0.9345 \| nan \| nan \| 0.7618 \| 0.0400 \| 0.0 \| nan \| nan \| nan \| 0.0075 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| nan \| 0.0 \| nan \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| 0.1109 \| 0.2326 \| 0.1176 \| 0.0 \| nan \| 0.0 \| 0.0 \| nan \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0065 \| 0.0 \| nan \| nan \| 0.0 \| nan \| nan \| 0.1944 \| 0.2630 \| 0.0 \| 0.0 \| nan \| 0.2078 \| nan \| 0.0 \| 0.0283 \| nan \| nan \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0010 \| nan \| 0.1036 \| 0.0 \| 0.4668 \| nan \| nan \| 0.3830 \| 0.0400 \| 0.0 \| nan \| nan \| nan \| 0.0073 \| nan \| nan \| nan \| nan \| nan \| nan \| nan \| 0.0 \| nan \| 0.0 \| ### Framework versions - Transformers 4.46.3 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.20.3
duyntnet/Sailor2-20B-Chat-imatrix-GGUF	duyntnet	2024-12-18T05:57:54Z	66	0	transformers	[ "transformers", "gguf", "imatrix", "Sailor2-20B-Chat", "text-generation", "en", "license:other", "region:us", "conversational" ]	text-generation	2024-12-17T23:30:58Z	--- license: other language: - en pipeline_tag: text-generation inference: false tags: - transformers - gguf - imatrix - Sailor2-20B-Chat --- Quantizations of https://huggingface.co/sail/Sailor2-20B-Chat ### Inference Clients/UIs * [llama.cpp](https://github.com/ggerganov/llama.cpp) * [KoboldCPP](https://github.com/LostRuins/koboldcpp) * [ollama](https://github.com/ollama/ollama) * [jan](https://github.com/janhq/jan) * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [GPT4All](https://github.com/nomic-ai/gpt4all) --- # From original readme Sailor2 is a community-driven initiative that brings cutting-edge multilingual language models to South-East Asia (SEA). Our research highlights a strong demand for models in the 8B and 20B parameter range for production use, alongside 1B models for specialized applications, such as speculative decoding and research purposes. These models, released under the Apache 2.0 license, provide enhanced accessibility to advanced language technologies across the region. Sailor2 builds upon the foundation of the awesome multilingual model [Qwen 2.5](https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e) and is continuously pre-trained on 500B tokens to support 15 languages better with a unified model. These languages include English, Chinese, Burmese, Cebuano, Ilocano, Indonesian, Javanese, Khmer, Lao, Malay, Sundanese, Tagalog, Thai, Vietnamese, and Waray. By addressing the growing demand for diverse, robust, and accessible language models, Sailor2 seeks to serve the underserved in SEA areas with open, inclusive, and accessible multilingual LLMs. The Sailor2 model comes in three sizes, 1B, 8B, and 20B, which are expanded from the Qwen2.5 base models of 0.5B, 7B, and 14B, respectively. ## Requirements The code of Sailor2 has been in the latest Hugging face transformers and we advise you to install `transformers==4.46.3`. ## Quickstart Here provides a code snippet to show you how to load the tokenizer and model and how to generate contents. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" model = AutoModelForCausalLM.from_pretrained( 'sail/Sailor2-20B-Chat', torch_dtype=torch.bfloat16, device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained('sail/Sailor2-20B-Chat') system_prompt= \ 'You are an AI assistant named Sailor2, created by Sea AI Lab. \ As an AI assistant, you can answer questions in English, Chinese, and Southeast Asian languages \ such as Burmese, Cebuano, Ilocano, Indonesian, Javanese, Khmer, Lao, Malay, Sundanese, Tagalog, Thai, Vietnamese, and Waray. \ Your responses should be friendly, unbiased, informative, detailed, and faithful.' prompt = "Beri saya pengenalan singkat tentang model bahasa besar." # prompt = "Hãy cho tôi một giới thiệu ngắn gọn về mô hình ngôn ngữ lớn." # prompt = "ให้ฉันแนะนำสั้น ๆ เกี่ยวกับโมเดลภาษาขนาดใหญ่" messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) input_ids = model_inputs.input_ids.to(device) generated_ids = model.generate( input_ids, max_new_tokens=512, ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ```
CheeLi03/whisper-base-fa-puct-5k	CheeLi03	2024-12-18T05:55:14Z	6	0	null	[ "tensorboard", "safetensors", "whisper", "hf-asr-leaderboard", "generated_from_trainer", "it", "dataset:fleurs", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "region:us" ]	null	2024-12-18T03:14:04Z	--- base_model: openai/whisper-tiny datasets: - fleurs language: - it license: apache-2.0 metrics: - wer tags: - hf-asr-leaderboard - generated_from_trainer model-index: - name: Whisper Tiny Italian 5k - Chee Li results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Google Fleurs type: fleurs config: fa_ir split: None args: 'config: it split: test' metrics: - type: wer value: 36.47645153251931 name: Wer --- <!-- 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 Tiny Italian 5k - Chee Li This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Google Fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.5897 - Wer: 36.4765 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\| \| 0.175 \| 4.6083 \| 1000 \| 0.4024 \| 37.5480 \| \| 0.0198 \| 9.2166 \| 2000 \| 0.4795 \| 36.7555 \| \| 0.0039 \| 13.8249 \| 3000 \| 0.5412 \| 37.0297 \| \| 0.0018 \| 18.4332 \| 4000 \| 0.5772 \| 36.4017 \| \| 0.0013 \| 23.0415 \| 5000 \| 0.5897 \| 36.4765 \| ### Framework versions - Transformers 4.43.4 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
mradermacher/Llama-3-portuguese-Tom-cat-8b-instruct-GGUF	mradermacher	2024-12-18T05:47:16Z	15	0	transformers	[ "transformers", "gguf", "portugues", "portuguese", "QA", "instruct", "pt", "dataset:rhaymison/superset", "base_model:rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct", "base_model:quantized:rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-18T04:10:06Z	--- base_model: rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct datasets: - rhaymison/superset language: - pt library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - portugues - portuguese - QA - instruct --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-portuguese-Tom-cat-8b-instruct-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-portuguese-Tom-cat-8b-instruct-GGUF/resolve/main/Llama-3-portuguese-Tom-cat-8b-instruct.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-portuguese-Tom-cat-8b-instruct-GGUF/resolve/main/Llama-3-portuguese-Tom-cat-8b-instruct.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-portuguese-Tom-cat-8b-instruct-GGUF/resolve/main/Llama-3-portuguese-Tom-cat-8b-instruct.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-portuguese-Tom-cat-8b-instruct-GGUF/resolve/main/Llama-3-portuguese-Tom-cat-8b-instruct.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-portuguese-Tom-cat-8b-instruct-GGUF/resolve/main/Llama-3-portuguese-Tom-cat-8b-instruct.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-portuguese-Tom-cat-8b-instruct-GGUF/resolve/main/Llama-3-portuguese-Tom-cat-8b-instruct.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-portuguese-Tom-cat-8b-instruct-GGUF/resolve/main/Llama-3-portuguese-Tom-cat-8b-instruct.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-portuguese-Tom-cat-8b-instruct-GGUF/resolve/main/Llama-3-portuguese-Tom-cat-8b-instruct.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-portuguese-Tom-cat-8b-instruct-GGUF/resolve/main/Llama-3-portuguese-Tom-cat-8b-instruct.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-portuguese-Tom-cat-8b-instruct-GGUF/resolve/main/Llama-3-portuguese-Tom-cat-8b-instruct.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-portuguese-Tom-cat-8b-instruct-GGUF/resolve/main/Llama-3-portuguese-Tom-cat-8b-instruct.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-portuguese-Tom-cat-8b-instruct-GGUF/resolve/main/Llama-3-portuguese-Tom-cat-8b-instruct.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mergekit-community/mergekit-model_stock-ysywggg	mergekit-community	2024-12-18T05:46:08Z	54	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:Azazelle/ANJIR-ADAPTER-128", "base_model:merge:Azazelle/ANJIR-ADAPTER-128", "base_model:Azazelle/Llama-3-8B-Abomination-LORA", "base_model:merge:Azazelle/Llama-3-8B-Abomination-LORA", "base_model:Azazelle/Nimue-8B", "base_model:merge:Azazelle/Nimue-8B", "base_model:BeastGokul/Bio-Medical-MultiModal-Llama-3-8B-Finetuned", "base_model:merge:BeastGokul/Bio-Medical-MultiModal-Llama-3-8B-Finetuned", "base_model:ResplendentAI/Smarts_Llama3", "base_model:merge:ResplendentAI/Smarts_Llama3", "base_model:grimjim/Llama-3-Instruct-abliteration-LoRA-8B", "base_model:merge:grimjim/Llama-3-Instruct-abliteration-LoRA-8B", "base_model:kik41/lora-length-long-llama-3-8b-v2", "base_model:merge:kik41/lora-length-long-llama-3-8b-v2", "base_model:kik41/lora-type-descriptive-llama-3-8b-v2", "base_model:merge:kik41/lora-type-descriptive-llama-3-8b-v2", "base_model:mergekit-community/mergekit-model_stock-anvdilz", "base_model:merge:mergekit-community/mergekit-model_stock-anvdilz", "base_model:surya-narayanan/anatomy", "base_model:merge:surya-narayanan/anatomy", "base_model:surya-narayanan/biology", "base_model:merge:surya-narayanan/biology", "base_model:surya-narayanan/formal_logic", "base_model:merge:surya-narayanan/formal_logic", "base_model:surya-narayanan/health", "base_model:merge:surya-narayanan/health", "base_model:surya-narayanan/human_sexuality", "base_model:merge:surya-narayanan/human_sexuality", "base_model:surya-narayanan/professional_medicine", "base_model:merge:surya-narayanan/professional_medicine", "base_model:surya-narayanan/professional_psychology", "base_model:merge:surya-narayanan/professional_psychology", "base_model:surya-narayanan/psychology", "base_model:merge:surya-narayanan/psychology", "base_model:surya-narayanan/sociology", "base_model:merge:surya-narayanan/sociology", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T05:23:56Z	--- base_model: - mergekit-community/mergekit-model_stock-anvdilz - BeastGokul/Bio-Medical-MultiModal-Llama-3-8B-Finetuned - mergekit-community/mergekit-model_stock-anvdilz - surya-narayanan/health - mergekit-community/mergekit-model_stock-anvdilz - surya-narayanan/formal_logic - mergekit-community/mergekit-model_stock-anvdilz - kik41/lora-type-descriptive-llama-3-8b-v2 - mergekit-community/mergekit-model_stock-anvdilz - Azazelle/Nimue-8B - mergekit-community/mergekit-model_stock-anvdilz - ResplendentAI/Smarts_Llama3 - mergekit-community/mergekit-model_stock-anvdilz - kik41/lora-length-long-llama-3-8b-v2 - mergekit-community/mergekit-model_stock-anvdilz - surya-narayanan/sociology - mergekit-community/mergekit-model_stock-anvdilz - surya-narayanan/biology - mergekit-community/mergekit-model_stock-anvdilz - surya-narayanan/anatomy - mergekit-community/mergekit-model_stock-anvdilz - surya-narayanan/professional_psychology - mergekit-community/mergekit-model_stock-anvdilz - grimjim/Llama-3-Instruct-abliteration-LoRA-8B - mergekit-community/mergekit-model_stock-anvdilz - surya-narayanan/psychology - mergekit-community/mergekit-model_stock-anvdilz - surya-narayanan/professional_medicine - mergekit-community/mergekit-model_stock-anvdilz - surya-narayanan/human_sexuality - mergekit-community/mergekit-model_stock-anvdilz - Azazelle/ANJIR-ADAPTER-128 - mergekit-community/mergekit-model_stock-anvdilz - Azazelle/Llama-3-8B-Abomination-LORA library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [grimjim/Llama-3-Instruct-abliteration-LoRA-8B](https://huggingface.co/grimjim/Llama-3-Instruct-abliteration-LoRA-8B) as a base. ### Models Merged The following models were included in the merge: * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [BeastGokul/Bio-Medical-MultiModal-Llama-3-8B-Finetuned](https://huggingface.co/BeastGokul/Bio-Medical-MultiModal-Llama-3-8B-Finetuned) * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [surya-narayanan/health](https://huggingface.co/surya-narayanan/health) * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [surya-narayanan/formal_logic](https://huggingface.co/surya-narayanan/formal_logic) * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [kik41/lora-type-descriptive-llama-3-8b-v2](https://huggingface.co/kik41/lora-type-descriptive-llama-3-8b-v2) * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [Azazelle/Nimue-8B](https://huggingface.co/Azazelle/Nimue-8B) * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [ResplendentAI/Smarts_Llama3](https://huggingface.co/ResplendentAI/Smarts_Llama3) * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [kik41/lora-length-long-llama-3-8b-v2](https://huggingface.co/kik41/lora-length-long-llama-3-8b-v2) * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [surya-narayanan/sociology](https://huggingface.co/surya-narayanan/sociology) * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [surya-narayanan/biology](https://huggingface.co/surya-narayanan/biology) * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [surya-narayanan/anatomy](https://huggingface.co/surya-narayanan/anatomy) * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [surya-narayanan/professional_psychology](https://huggingface.co/surya-narayanan/professional_psychology) * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [surya-narayanan/psychology](https://huggingface.co/surya-narayanan/psychology) * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [surya-narayanan/professional_medicine](https://huggingface.co/surya-narayanan/professional_medicine) * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [surya-narayanan/human_sexuality](https://huggingface.co/surya-narayanan/human_sexuality) * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [Azazelle/ANJIR-ADAPTER-128](https://huggingface.co/Azazelle/ANJIR-ADAPTER-128) * [mergekit-community/mergekit-model_stock-anvdilz](https://huggingface.co/mergekit-community/mergekit-model_stock-anvdilz) + [Azazelle/Llama-3-8B-Abomination-LORA](https://huggingface.co/Azazelle/Llama-3-8B-Abomination-LORA) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: mergekit-community/mergekit-model_stock-anvdilz+Azazelle/ANJIR-ADAPTER-128 - model: mergekit-community/mergekit-model_stock-anvdilz+Azazelle/Nimue-8B - model: mergekit-community/mergekit-model_stock-anvdilz+surya-narayanan/formal_logic - model: mergekit-community/mergekit-model_stock-anvdilz+surya-narayanan/sociology - model: mergekit-community/mergekit-model_stock-anvdilz+surya-narayanan/health - model: mergekit-community/mergekit-model_stock-anvdilz+surya-narayanan/professional_medicine - model: mergekit-community/mergekit-model_stock-anvdilz+BeastGokul/Bio-Medical-MultiModal-Llama-3-8B-Finetuned - model: mergekit-community/mergekit-model_stock-anvdilz+surya-narayanan/biology - model: mergekit-community/mergekit-model_stock-anvdilz+surya-narayanan/psychology - model: mergekit-community/mergekit-model_stock-anvdilz+surya-narayanan/professional_psychology - model: mergekit-community/mergekit-model_stock-anvdilz+ResplendentAI/Smarts_Llama3 - model: mergekit-community/mergekit-model_stock-anvdilz+Azazelle/Llama-3-8B-Abomination-LORA - model: mergekit-community/mergekit-model_stock-anvdilz+kik41/lora-type-descriptive-llama-3-8b-v2 - model: mergekit-community/mergekit-model_stock-anvdilz+kik41/lora-length-long-llama-3-8b-v2 - model: mergekit-community/mergekit-model_stock-anvdilz+surya-narayanan/anatomy - model: mergekit-community/mergekit-model_stock-anvdilz+surya-narayanan/human_sexuality merge_method: model_stock base_model: mergekit-community/mergekit-model_stock-anvdilz+grimjim/Llama-3-Instruct-abliteration-LoRA-8B dtype: bfloat16 ```
GoodiesHere/Apollo-LMMs-Apollo-7B-t32	GoodiesHere	2024-12-18T05:44:58Z	540	50	transformers	[ "transformers", "safetensors", "apollo", "text-generation", "video", "video-understanding", "vision", "multimodal", "conversational", "custom_code", "instruction-tuning", "video-text-to-text", "en", "arxiv:2412.10360", "license:apache-2.0", "autotrain_compatible", "region:us" ]	video-text-to-text	2024-12-18T05:39:36Z	--- license: apache-2.0 language: - en pipeline_tag: video-text-to-text tags: - video - video-understanding - vision - multimodal - conversational - custom_code - instruction-tuning library_name: transformers --- # Apollo: An Exploration of Video Understanding in Large Multimodal Models Apollo is a family of Large Multimodal Models (LMMs) that push the state-of-the-art in video understanding. It supports tasks including: - Long-form video comprehension - Temporal reasoning - Complex video question-answering - Multi-turn conversations grounded in video content Apollo models excel at handling hour-long videos, balancing speed and accuracy through strategic design decisions. Our models outperform most 7B competitors at just 3B parameters and even rival 30B-scale models. Key Highlights: - 7B model varient - 32 tokens/frame ## Quick Start Installation: ```bash pip install -e . pip install flash-attn --no-build-isolation ``` Inference Example: ```python import torch from transformers import AutoModelForCausalLM from apollo.mm_utils import ( KeywordsStoppingCriteria, tokenizer_mm_token, ApolloMMLoader ) from apollo.conversations import conv_templates, SeparatorStyle from huggingface_hub import snapshot_download model_url = "Apollo-LMMs/Apollo-3B-t32" model_path = snapshot_download(model_url, repo_type="model") device = "cuda" if torch.cuda.is_available() else "cpu" model = AutoModelForCausalLM.from_pretrained( model_path, trust_remote_code=True, low_cpu_mem_usage=True ).to(device=device, dtype=torch.bfloat16) tokenizer = model.tokenizer vision_processors = model.vision_tower.vision_processor config = model.config num_repeat_token = config.mm_connector_cfg['num_output_tokens'] mm_processor = ApolloMMLoader( vision_processors, config.clip_duration, frames_per_clip=4, clip_sampling_ratio=0.65, model_max_length=config.model_max_length, device=device, num_repeat_token=num_repeat_token ) video_path = "path/to/video.mp4" question = "Describe this video in detail" mm_data, replace_string = mm_processor.load_video(video_path) conv = conv_templates["qwen_2"].copy() conv.append_message(conv.roles[0], replace_string + "\n\n" + question) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() input_ids = tokenizer_mm_token(prompt, tokenizer, return_tensors="pt").unsqueeze(0).to(device) stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 stopping_criteria = KeywordsStoppingCriteria([stop_str], tokenizer, input_ids) with torch.inference_mode(): output_ids = model.generate( input_ids, vision_input=[mm_data], data_types=['video'], do_sample=True, temperature=0.4, max_new_tokens=256, top_p=0.7, use_cache=True, num_beams=1, stopping_criteria=[stopping_criteria] ) pred = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() print(pred) ``` ## Citation If you find this project useful, please consider citing: ```BibTeX @article{zohar2024apollo, title={Apollo: An Exploration of Video Understanding in Large Multimodal Models}, author={Zohar, Orr and Wang, Xiaohan and Dubois, Yann and Mehta, Nikhil and Xiao, Tong and Hansen-Estruch, Philippe and Yu, Licheng and Wang, Xiaofang and Juefei-Xu, Felix and Zhang, Ning and Yeung-Levy, Serena and Xia, Xide}, journal={arXiv preprint arXiv:2412.10360}, year={2024} } ``` For more details, visit the [project website](https://apollo-lmms.github.io) or check out the [paper](https://arxiv.org/abs/2412.10360).
iqbalpurba26/indobert-ham-spam-detection	iqbalpurba26	2024-12-18T05:43:01Z	63	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T05:26:55Z	--- library_name: transformers tags: - generated_from_keras_callback model-index: - name: indobert-ham-spam-detection 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. --> # indobert-ham-spam-detection This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## 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: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.46.3 - TensorFlow 2.17.1 - Tokenizers 0.20.3
mradermacher/StoryTeller7b-meh-i1-GGUF	mradermacher	2024-12-18T05:41:39Z	15	0	transformers	[ "transformers", "gguf", "en", "base_model:tdh87/StoryTeller7b-meh", "base_model:quantized:tdh87/StoryTeller7b-meh", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-12-17T17:14:17Z	--- base_model: tdh87/StoryTeller7b-meh language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/tdh87/StoryTeller7b-meh <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/StoryTeller7b-meh-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/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-Q2_K.gguf) \| i1-Q2_K \| 2.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/StoryTeller7b-meh-i1-GGUF/resolve/main/StoryTeller7b-meh.i1-Q6_K.gguf) \| i1-Q6_K \| 6.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
visdata/st95	visdata	2024-12-18T05:40:05Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T05:31:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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visdata/st94	visdata	2024-12-18T05:38:36Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T05:30:44Z	--- 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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Johnson8187/Chinese-Emotion-Small	Johnson8187	2024-12-18T05:35:23Z	1,154	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "emotion", "zh", "dataset:Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset", "base_model:MoritzLaurer/mDeBERTa-v3-base-mnli-xnli", "base_model:finetune:MoritzLaurer/mDeBERTa-v3-base-mnli-xnli", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T02:54:10Z	--- license: mit language: - zh base_model: - MoritzLaurer/mDeBERTa-v3-base-mnli-xnli pipeline_tag: text-classification tags: - emotion library_name: transformers datasets: - Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset --- # Chinese-text-emotion-classifier-small Large version(560M):[Johnson8187/Chinese-Emotion](https://huggingface.co/Johnson8187/Chinese-Emotion-Small) 大參數版本模型（560M）：[Johnson8187/Chinese-Emotion](https://huggingface.co/Johnson8187/Chinese-Emotion-Small) ## 📚 Model Introduction This model is fine-tuned based on the [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) model, specializing in Chinese text emotion analysis. Through fine-tuning, the model can identify the following 8 emotion labels: - Neutral tone - Concerned tone - Happy tone - Angry tone - Sad tone - Questioning tone - Surprised tone - Disgusted tone The model is applicable to various scenarios, such as customer service emotion monitoring, social media analysis, and user feedback classification. --- ## 📚 模型簡介本模型基於[MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) 模型進行微調，專注於中文語句情感分析。通過微調，模型可以識別以下 8 種情緒標籤： - 平淡語氣 - 關切語調 - 開心語調 - 憤怒語調 - 悲傷語調 - 疑問語調 - 驚奇語調 - 厭惡語調該模型適用於多種場景，例如客服情緒監控、社交媒體分析以及用戶反饋分類。 --- ## 🚀 Quick Start ### Install Dependencies Ensure that you have installed Hugging Face's Transformers library and PyTorch: ```bash pip install transformers torch ``` ###Load the Model Use the following code to load the model and tokenizer, and perform emotion classification: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # 添加設備設定 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 標籤映射字典 label_mapping = { 0: "平淡語氣", 1: "關切語調", 2: "開心語調", 3: "憤怒語調", 4: "悲傷語調", 5: "疑問語調", 6: "驚奇語調", 7: "厭惡語調" } def predict_emotion(text, model_path="Johnson8187/Chinese-Emotion-Small"): # 載入模型和分詞器 tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForSequenceClassification.from_pretrained(model_path).to(device) # 移動模型到設備 # 將文本轉換為模型輸入格式 inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device) # 移動輸入到設備 # 進行預測 with torch.no_grad(): outputs = model(inputs) # 取得預測結果 predicted_class = torch.argmax(outputs.logits).item() predicted_emotion = label_mapping[predicted_class] return predicted_emotion if __name__ == "__main__": # 使用範例 test_texts = [ "雖然我努力了很久，但似乎總是做不到，我感到自己一無是處。", "你說的那些話真的讓我很困惑，完全不知道該怎麼反應。", "這世界真的是無情，為什麼每次都要給我這樣的考驗？", "有時候，我只希望能有一點安靜，不要再聽到這些無聊的話題。", "每次想起那段過去，我的心還是會痛，真的無法釋懷。", "我從來沒有想過會有這麼大的改變，現在我覺得自己完全失控了。", "我完全沒想到你會這麼做，這讓我驚訝到無法言喻。", "我知道我應該更堅強，但有些時候，這種情緒真的讓我快要崩潰了。" ] for text in test_texts: emotion = predict_emotion(text) print(f"文本: {text}") print(f"預測情緒: {emotion}\n") ``` --- ## 🚀 快速開始 ### 安裝依賴請確保安裝了 Hugging Face 的 Transformers 庫和 PyTorch： ```bash pip install transformers torch ``` ### 加載模型使用以下代碼加載模型和分詞器，並進行情感分類： ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # 添加設備設定 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 標籤映射字典 label_mapping = { 0: "平淡語氣", 1: "關切語調", 2: "開心語調", 3: "憤怒語調", 4: "悲傷語調", 5: "疑問語調", 6: "驚奇語調", 7: "厭惡語調" } def predict_emotion(text, model_path="Johnson8187/Chinese-Emotion-Small"): # 載入模型和分詞器 tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForSequenceClassification.from_pretrained(model_path).to(device) # 移動模型到設備 # 將文本轉換為模型輸入格式 inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device) # 移動輸入到設備 # 進行預測 with torch.no_grad(): outputs = model(inputs) # 取得預測結果 predicted_class = torch.argmax(outputs.logits).item() predicted_emotion = label_mapping[predicted_class] return predicted_emotion if __name__ == "__main__": # 使用範例 test_texts = [ "雖然我努力了很久，但似乎總是做不到，我感到自己一無是處。", "你說的那些話真的讓我很困惑，完全不知道該怎麼反應。", "這世界真的是無情，為什麼每次都要給我這樣的考驗？", "有時候，我只希望能有一點安靜，不要再聽到這些無聊的話題。", "每次想起那段過去，我的心還是會痛，真的無法釋懷。", "我從來沒有想過會有這麼大的改變，現在我覺得自己完全失控了。", "我完全沒想到你會這麼做，這讓我驚訝到無法言喻。", "我知道我應該更堅強，但有些時候，這種情緒真的讓我快要崩潰了。" ] for text in test_texts: emotion = predict_emotion(text) print(f"文本: {text}") print(f"預測情緒: {emotion}\n") ``` --- ### Dataset - The fine-tuning dataset consists of 4,000 annotated Traditional Chinese emotion samples, covering various emotion categories to ensure the model's generalization capability in emotion classification. - [Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset](https://huggingface.co/datasets/Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset) ### 數據集 - 微調數據來自4000個自行標註的高質量繁體中文情感語句數據，覆蓋了多種情緒類別，確保模型在情感分類上的泛化能力。 - [Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset](https://huggingface.co/datasets/Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset) --- 🌟 Contact and Feedback If you encounter any issues while using this model, please contact: Email: [email protected] Hugging Face Project Page: chinese-text-emotion-classifier ## 🌟 聯繫與反饋如果您在使用該模型時有任何問題，請聯繫： - 郵箱：`[email protected]` - Hugging Face 項目頁面：[chinese-Emotion-Small](https://huggingface.co/Johnson8187/Chinese-Emotion-Small)
GoodiesHere/Apollo-LMMs-Apollo-3B-t32	GoodiesHere	2024-12-18T05:32:44Z	219	17	transformers	[ "transformers", "safetensors", "apollo", "text-generation", "video", "video-understanding", "vision", "multimodal", "conversational", "custom_code", "instruction-tuning", "en", "arxiv:2412.10360", "license:other", "autotrain_compatible", "region:us" ]	text-generation	2024-12-18T05:28:40Z	--- license: other license_name: research-licence license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - video - video-understanding - vision - multimodal - conversational - custom_code - instruction-tuning library_name: transformers --- # Apollo: An Exploration of Video Understanding in Large Multimodal Models Apollo is a family of Large Multimodal Models (LMMs) that push the state-of-the-art in video understanding. It supports tasks including: - Long-form video comprehension - Temporal reasoning - Complex video question-answering - Multi-turn conversations grounded in video content Apollo models excel at handling hour-long videos, balancing speed and accuracy through strategic design decisions. Our models outperform most 7B competitors at just 3B parameters and even rival 30B-scale models. Key Highlights: - Scaling Consistency: Design decisions validated on smaller models and datasets effectively transfer to larger scales, reducing computation and experimentation costs. - Efficient Video Sampling: fps sampling and advanced token resampling strategies (e.g., Perceiver) yield stronger temporal perception. - Encoder Synergies: Combining SigLIP-SO400M (image) with InternVideo2 (video) delivers a robust representation, outperforming single encoders on temporal tasks. - ApolloBench: A streamlined evaluation benchmark (41x faster) that focuses on true video understanding capabilities. ## Quick Start Installation: ```bash pip install -e . pip install flash-attn --no-build-isolation ``` Inference Example: ```python import torch from transformers import AutoModelForCausalLM from apollo.mm_utils import ( KeywordsStoppingCriteria, tokenizer_mm_token, ApolloMMLoader ) from apollo.conversation import conv_templates, SeparatorStyle from huggingface_hub import snapshot_download model_url = "Apollo-LMMs/Apollo-3B-t32" model_path = snapshot_download(model_url, repo_type="model") device = "cuda" if torch.cuda.is_available() else "cpu" model = AutoModelForCausalLM.from_pretrained( model_path, trust_remote_code=True, low_cpu_mem_usage=True ).to(device=device, dtype=torch.bfloat16) tokenizer = model.tokenizer vision_processors = model.vision_tower.vision_processor config = model.config num_repeat_token = config.mm_connector_cfg['num_output_tokens'] mm_processor = ApolloMMLoader( vision_processors, config.clip_duration, frames_per_clip=4, clip_sampling_ratio=0.65, model_max_length=config.model_max_length, device=device, num_repeat_token=num_repeat_token ) video_path = "path/to/video.mp4" question = "Describe this video in detail" mm_data, replace_string = mm_processor.load_video(video_path) conv = conv_templates["qwen_2"].copy() conv.append_message(conv.roles[0], replace_string + "\n\n" + question) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() input_ids = tokenizer_mm_token(prompt, tokenizer, return_tensors="pt").unsqueeze(0).to(device) stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 stopping_criteria = KeywordsStoppingCriteria([stop_str], tokenizer, input_ids) with torch.inference_mode(): output_ids = model.generate( input_ids, vision_input=[mm_data], data_types=['video'], do_sample=True, temperature=0.4, max_new_tokens=256, top_p=0.7, use_cache=True, num_beams=1, stopping_criteria=[stopping_criteria] ) pred = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() print(pred) ``` ## Citation If you find this project useful, please consider citing: ```BibTeX @article{zohar2024apollo, title={Apollo: An Exploration of Video Understanding in Large Multimodal Models}, author={Zohar, Orr and Wang, Xiaohan and Dubois, Yann and Mehta, Nikhil and Xiao, Tong and Hansen-Estruch, Philippe and Yu, Licheng and Wang, Xiaofang and Juefei-Xu, Felix and Zhang, Ning and Yeung-Levy, Serena and Xia, Xide}, journal={arXiv preprint arXiv:2412.10360}, year={2024} } ``` For more details, visit the [project website](https://apollo-lmms.github.io) or check out the [paper](https://arxiv.org/abs/2412.10360).
tokyo-electron-device-ai/llama3-tedllm-8b-v0-annealing	tokyo-electron-device-ai	2024-12-18T05:28:47Z	7	0	null	[ "safetensors", "llama", "license:llama3", "region:us" ]	null	2024-12-18T04:37:21Z	--- license: llama3 --- # Llama3-tedllm-8B-v0-annealing This model is the annealing model of Llama3-tedllm-8b-v0. Please refer to [here](https://huggingface.co/tokyo-electron-device-ai/llama3-tedllm-8b-v0) about the Llama3-tedllm-8b-v0 detail.
Michael444/DistilbertModel	Michael444	2024-12-18T05:28:21Z	66	1	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T05:27:20Z	--- library_name: transformers tags: - generated_from_keras_callback model-index: - name: DistilbertModel 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. --> # DistilbertModel This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## 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: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.47.0 - TensorFlow 2.18.0 - Tokenizers 0.21.0
TheBlueObserver/Qwen2.5-Coder-3B-Instruct-MLX-393a7	TheBlueObserver	2024-12-18T05:12:12Z	78	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "code", "codeqwen", "chat", "qwen", "qwen-coder", "mlx", "conversational", "en", "base_model:Qwen/Qwen2.5-Coder-3B-Instruct", "base_model:quantized:Qwen/Qwen2.5-Coder-3B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "region:us" ]	text-generation	2024-12-18T05:10:33Z	--- base_model: Qwen/Qwen2.5-Coder-3B-Instruct language: - en library_name: transformers license: other license_name: qwen-research license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - code - codeqwen - chat - qwen - qwen-coder - mlx --- # TheBlueObserver/Qwen2.5-Coder-3B-Instruct-MLX-393a7 The Model [TheBlueObserver/Qwen2.5-Coder-3B-Instruct-MLX-393a7](https://huggingface.co/TheBlueObserver/Qwen2.5-Coder-3B-Instruct-MLX-393a7) was converted to MLX format from [Qwen/Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct) using mlx-lm version 0.20.2. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("TheBlueObserver/Qwen2.5-Coder-3B-Instruct-MLX-393a7") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
VHKE/lukkein	VHKE	2024-12-18T05:07:13Z	50	1	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-12-18T05:07:04Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym widget: - output: url: sample/lukkein_005500_00_20241217194410.png text: Lukkein base_model: black-forest-labs/FLUX.1-dev instance_prompt: Lukkein 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 --- # Lukkein A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `Lukkein` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
PrunaAI/mingxilei-gpt2-imdb-pos-eps-10-bnb-8bit-smashed	PrunaAI	2024-12-18T05:06:36Z	5	0	null	[ "safetensors", "gpt2", "pruna-ai", "base_model:mingxilei/gpt2-imdb-pos-eps-10", "base_model:quantized:mingxilei/gpt2-imdb-pos-eps-10", "8-bit", "bitsandbytes", "region:us" ]	null	2024-12-18T05:06:20Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: mingxilei/gpt2-imdb-pos-eps-10 metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer"> <img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with llm-int8. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo mingxilei/gpt2-imdb-pos-eps-10 installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/mingxilei-gpt2-imdb-pos-eps-10-bnb-8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("mingxilei/gpt2-imdb-pos-eps-10") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model mingxilei/gpt2-imdb-pos-eps-10 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Do it by yourself [here](https://docs.pruna.ai/en/latest/setup/pip.html).
TheBlueObserver/Qwen2.5-Coder-1.5B-Instruct-MLX-393a7	TheBlueObserver	2024-12-18T05:05:14Z	82	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "code", "codeqwen", "chat", "qwen", "qwen-coder", "mlx", "conversational", "en", "base_model:Qwen/Qwen2.5-Coder-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-Coder-1.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "region:us" ]	text-generation	2024-12-18T05:04:22Z	--- base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - code - codeqwen - chat - qwen - qwen-coder - mlx --- # TheBlueObserver/Qwen2.5-Coder-1.5B-Instruct-MLX-393a7 The Model [TheBlueObserver/Qwen2.5-Coder-1.5B-Instruct-MLX-393a7](https://huggingface.co/TheBlueObserver/Qwen2.5-Coder-1.5B-Instruct-MLX-393a7) was converted to MLX format from [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) using mlx-lm version 0.20.2. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("TheBlueObserver/Qwen2.5-Coder-1.5B-Instruct-MLX-393a7") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
tyang816/MedChatZH	tyang816	2024-12-18T05:02:21Z	63	6	transformers	[ "transformers", "pytorch", "safetensors", "baichuan", "text-generation", "custom_code", "zh", "dataset:tyang816/MedChatZH", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-22T02:23:36Z	--- license: apache-2.0 datasets: - tyang816/MedChatZH language: - zh --- # MedChatZH --- ## MedChatZH中文微调参数基于Baichuan-7B在中医书籍数据和医疗指令对话数据上训练，使其具备较强的中文医疗对话能力，能够理解中医问题。 Github：[MedChatZH-7B](https://github.com/tyang816/MedChatZH) ## 🚀 结果 \| 问题 \| 回答 \| \| :----------------------------------------------------------- \| ------------------------------------------------------------ \| \| 中医治脱发有效吗脱发5、6年了，明显比以前少了很多，脱发位置比较均衡，没有突出掉的比较多的地方，我的发质比较硬，头发生长也较快。 \| 中药治疗脱发的效果因人而异，但对于您这种头发质量好且生长速度快的患者来说，中药可能是一个不错的选择。一些常用的中药方剂包括六味地黄丸、四物汤等，这些药物可以滋养肝肾，促进血液循环和毛囊营养供应，从而改善脱发问题。此外，还可以通过针灸、艾灸等方式进行辅助治疗。但是需要注意的是，中药治疗需要长期坚持才能见效，同时还需要注意饮食和生活习惯等方面的问题，以达到更好的疗效。建议您咨询专业的中医医生或皮肤科医生，根据个人情况制定个性化的治疗方案。 \| \| 脑萎缩中医如何治疗？中医治疗脑萎缩好吗？脑萎缩中医如何治疗？中医治疗脑萎缩好吗？ \| 中药、针灸和推拿等。中药可以根据患者的具体情况进行配方，以达到调节身体机能的目的;针灸可以通过刺激穴位来改善血液循环和神经功能;推拿则可以通过按摩头部和颈部肌肉来缓解疼痛和僵硬感。需要注意的是，中医治疗需要长期坚持才能取得良好的效果，同时还需要结合其他康复措施和生活方式改变，如合理饮食、适量运动、保持心情愉悦等等。如果您有任何疑问或不适，请及时咨询医生。 \| \| 中药治疗肺癌效果好吗？麻烦医生了中药治疗肺癌效果好吗？我家老人肺癌晚期，听人说中医可以治疗肺癌真的吗？效果怎么样？ \| 中药在治疗肺癌方面有一定的作用。但是需要注意的是，中药并不是所有人都适用的，需要根据患者的具体情况来选择合适的药物和剂量。此外，中药的疗效也需要长期观察才能确定。对于您家老人的肺癌晚期，建议及时就医并进行综合治疗，包括手术、放化疗等方法。同时也可以考虑结合中医药辅助治疗，但一定要在专业医师指导下进行。希望您的家人早日康复。 \| \| 最近脸上总是冒出小痘痘……为什么呢？我用的是不吃晚饭减肥，其他都正常的。但是大便的情况很不好，我很头疼的，便秘情况太严重了。最近脸上总是冒出痘痘，额头、脸颊、法令纹那一边一个，还有点疼，有白色的浓…… \| 你好，根据你的描述，你可能患有痤疮和便秘的问题。长期不吃饭会导致身体缺乏营养物质，从而影响皮肤的健康状况。建议你在饮食方面要注意均衡摄入各种食物，尤其是蛋白质、维生素A、B族以及矿物质等。此外，你可以尝试一些中药调理，如黄连上清丸或龙胆泻肝丸来缓解症状。对于痤疮问题，可以使用外用药膏进行治疗，例如维甲酸类药物或者过氧化苯甲酰凝胶等。如果症状较为严重，也可以考虑口服抗生素。至于便秘问题，可以通过增加膳食纤维摄入量、多喝水等方式改善。同时，避免过度用力排便也是非常重要的。如果你仍然感到不适，请及时就医并咨询专业医生的意见。希望我的回答能对你有所帮助。 \| ## 🔥 引用如果您使用了我们的模型、代码或数据，请引用： ``` @article{tan2024medchatzh, title={MedChatZH: A tuning LLM for traditional Chinese medicine consultations}, author={Tan, Yang and Zhang, Zhixing and Li, Mingchen and Pan, Fei and Duan, Hao and Huang, Zijie and Deng, Hua and Yu, Zhuohang and Yang, Chen and Shen, Guoyang and others}, journal={Computers in Biology and Medicine}, pages={108290}, year={2024}, publisher={Elsevier} } ``` 也请同时引用BELLE、LLaMA等项目 ## 🐼 使用限制 - 本项目模型与数据集及其衍生物仅用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。 - 本项目不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张，因使用本项目模型、数据集等带来的任何损害、纠纷，本项目不承担任何责任。 - 在涉及事实性的指令上可能会产生违背事实的错误回答。 - 对于具备危害性的指令无法很好的鉴别，由此会产生危害性言论。 - 在一些涉及推理、代码等场景下模型的能力仍有待提高。
geralt-of-rivia1569/bert_fine_tuned_question_classifier	geralt-of-rivia1569	2024-12-18T05:00:03Z	107	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-12-18T04:59:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mingxilei/gpt2-imdb-pos-eps-10	mingxilei	2024-12-18T04:59:12Z	149	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T04:58:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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. 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mergekit-community/mergekit-model_stock-rxbbxes	mergekit-community	2024-12-18T04:45:10Z	28	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:Azazelle/ANJIR-ADAPTER-128", "base_model:merge:Azazelle/ANJIR-ADAPTER-128", "base_model:Azazelle/Llama-3-8B-Abomination-LORA", "base_model:merge:Azazelle/Llama-3-8B-Abomination-LORA", "base_model:Azazelle/Nimue-8B", "base_model:merge:Azazelle/Nimue-8B", "base_model:BeastGokul/Bio-Medical-MultiModal-Llama-3-8B-Finetuned", "base_model:merge:BeastGokul/Bio-Medical-MultiModal-Llama-3-8B-Finetuned", "base_model:ResplendentAI/Smarts_Llama3", "base_model:merge:ResplendentAI/Smarts_Llama3", "base_model:Sao10K/L3-8B-Stheno-v3.2", "base_model:merge:Sao10K/L3-8B-Stheno-v3.2", "base_model:grimjim/Llama-3-Instruct-abliteration-LoRA-8B", "base_model:merge:grimjim/Llama-3-Instruct-abliteration-LoRA-8B", "base_model:kik41/lora-length-long-llama-3-8b-v2", "base_model:merge:kik41/lora-length-long-llama-3-8b-v2", "base_model:kik41/lora-type-descriptive-llama-3-8b-v2", "base_model:merge:kik41/lora-type-descriptive-llama-3-8b-v2", "base_model:surya-narayanan/anatomy", "base_model:merge:surya-narayanan/anatomy", "base_model:surya-narayanan/biology", "base_model:merge:surya-narayanan/biology", "base_model:surya-narayanan/formal_logic", "base_model:merge:surya-narayanan/formal_logic", "base_model:surya-narayanan/health", "base_model:merge:surya-narayanan/health", "base_model:surya-narayanan/human_sexuality", "base_model:merge:surya-narayanan/human_sexuality", "base_model:surya-narayanan/professional_medicine", "base_model:merge:surya-narayanan/professional_medicine", "base_model:surya-narayanan/professional_psychology", "base_model:merge:surya-narayanan/professional_psychology", "base_model:surya-narayanan/psychology", "base_model:merge:surya-narayanan/psychology", "base_model:surya-narayanan/sociology", "base_model:merge:surya-narayanan/sociology", "base_model:tannedbum/L3-Nymeria-v2-8B", "base_model:merge:tannedbum/L3-Nymeria-v2-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T04:23:32Z	--- base_model: - tannedbum/L3-Nymeria-v2-8B - surya-narayanan/human_sexuality - tannedbum/L3-Nymeria-v2-8B - surya-narayanan/professional_medicine - tannedbum/L3-Nymeria-v2-8B - Azazelle/ANJIR-ADAPTER-128 - tannedbum/L3-Nymeria-v2-8B - Azazelle/Llama-3-8B-Abomination-LORA - tannedbum/L3-Nymeria-v2-8B - surya-narayanan/biology - Sao10K/L3-8B-Stheno-v3.2 - grimjim/Llama-3-Instruct-abliteration-LoRA-8B - tannedbum/L3-Nymeria-v2-8B - BeastGokul/Bio-Medical-MultiModal-Llama-3-8B-Finetuned - tannedbum/L3-Nymeria-v2-8B - surya-narayanan/formal_logic - tannedbum/L3-Nymeria-v2-8B - kik41/lora-type-descriptive-llama-3-8b-v2 - tannedbum/L3-Nymeria-v2-8B - kik41/lora-length-long-llama-3-8b-v2 - tannedbum/L3-Nymeria-v2-8B - surya-narayanan/sociology - tannedbum/L3-Nymeria-v2-8B - ResplendentAI/Smarts_Llama3 - tannedbum/L3-Nymeria-v2-8B - surya-narayanan/anatomy - tannedbum/L3-Nymeria-v2-8B - surya-narayanan/health - tannedbum/L3-Nymeria-v2-8B - surya-narayanan/psychology - tannedbum/L3-Nymeria-v2-8B - Azazelle/Nimue-8B - tannedbum/L3-Nymeria-v2-8B - surya-narayanan/professional_psychology library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Sao10K/L3-8B-Stheno-v3.2](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2) + [grimjim/Llama-3-Instruct-abliteration-LoRA-8B](https://huggingface.co/grimjim/Llama-3-Instruct-abliteration-LoRA-8B) as a base. ### Models Merged The following models were included in the merge: * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [surya-narayanan/human_sexuality](https://huggingface.co/surya-narayanan/human_sexuality) * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [surya-narayanan/professional_medicine](https://huggingface.co/surya-narayanan/professional_medicine) * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [Azazelle/ANJIR-ADAPTER-128](https://huggingface.co/Azazelle/ANJIR-ADAPTER-128) * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [Azazelle/Llama-3-8B-Abomination-LORA](https://huggingface.co/Azazelle/Llama-3-8B-Abomination-LORA) * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [surya-narayanan/biology](https://huggingface.co/surya-narayanan/biology) * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [BeastGokul/Bio-Medical-MultiModal-Llama-3-8B-Finetuned](https://huggingface.co/BeastGokul/Bio-Medical-MultiModal-Llama-3-8B-Finetuned) * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [surya-narayanan/formal_logic](https://huggingface.co/surya-narayanan/formal_logic) * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [kik41/lora-type-descriptive-llama-3-8b-v2](https://huggingface.co/kik41/lora-type-descriptive-llama-3-8b-v2) * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [kik41/lora-length-long-llama-3-8b-v2](https://huggingface.co/kik41/lora-length-long-llama-3-8b-v2) * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [surya-narayanan/sociology](https://huggingface.co/surya-narayanan/sociology) * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [ResplendentAI/Smarts_Llama3](https://huggingface.co/ResplendentAI/Smarts_Llama3) * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [surya-narayanan/anatomy](https://huggingface.co/surya-narayanan/anatomy) * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [surya-narayanan/health](https://huggingface.co/surya-narayanan/health) * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [surya-narayanan/psychology](https://huggingface.co/surya-narayanan/psychology) * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [Azazelle/Nimue-8B](https://huggingface.co/Azazelle/Nimue-8B) * [tannedbum/L3-Nymeria-v2-8B](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B) + [surya-narayanan/professional_psychology](https://huggingface.co/surya-narayanan/professional_psychology) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: tannedbum/L3-Nymeria-v2-8B+Azazelle/ANJIR-ADAPTER-128 - model: tannedbum/L3-Nymeria-v2-8B+Azazelle/Nimue-8B - model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/formal_logic - model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/sociology - model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/health - model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/professional_medicine - model: tannedbum/L3-Nymeria-v2-8B+BeastGokul/Bio-Medical-MultiModal-Llama-3-8B-Finetuned - model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/biology - model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/psychology - model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/professional_psychology - model: tannedbum/L3-Nymeria-v2-8B+ResplendentAI/Smarts_Llama3 - model: tannedbum/L3-Nymeria-v2-8B+Azazelle/Llama-3-8B-Abomination-LORA - model: tannedbum/L3-Nymeria-v2-8B+kik41/lora-type-descriptive-llama-3-8b-v2 - model: tannedbum/L3-Nymeria-v2-8B+kik41/lora-length-long-llama-3-8b-v2 - model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/anatomy - model: tannedbum/L3-Nymeria-v2-8B+surya-narayanan/human_sexuality merge_method: model_stock base_model: Sao10K/L3-8B-Stheno-v3.2+grimjim/Llama-3-Instruct-abliteration-LoRA-8B dtype: bfloat16 ```
visdata/sn104	visdata	2024-12-18T04:37:11Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T04:28:28Z	--- 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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visdata/sn105	visdata	2024-12-18T04:36:13Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T04:27:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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visdata/sn103	visdata	2024-12-18T04:35:52Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T04:27:52Z	--- 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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visdata/sn102	visdata	2024-12-18T04:32:59Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-18T04:26:23Z	--- 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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Marcusxx/cheonanAddresses_torchmodel_model	Marcusxx	2024-12-18T04:27:04Z	14	0	null	[ "tensorboard", "safetensors", "whisper", "hf-asr-leaderboard", "generated_from_trainer", "ko", "dataset:Marcusxx/cheonanAddresses", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "region:us" ]	null	2024-10-18T06:18:29Z	--- language: - ko license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer base_model: openai/whisper-medium datasets: - Marcusxx/cheonanAddresses model-index: - name: cheonanAddresses_torchmodel_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. --> # cheonanAddresses_torchmodel_model This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Marcusxx/cheonanAddresses dataset. It achieves the following results on the evaluation set: - Loss: 0.0531 - Cer: 1.9003 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:-------:\| \| 0.0771 \| 0.3101 \| 1000 \| 0.0758 \| 28.1409 \| \| 0.0713 \| 0.6202 \| 2000 \| 0.0718 \| 2.2685 \| \| 0.0629 \| 0.9302 \| 3000 \| 0.0682 \| 2.3271 \| \| 0.0583 \| 1.2403 \| 4000 \| 0.0637 \| 2.1723 \| \| 0.0623 \| 1.5504 \| 5000 \| 0.0613 \| 2.1513 \| \| 0.0568 \| 1.8605 \| 6000 \| 0.0598 \| 2.2237 \| \| 0.051 \| 2.1705 \| 7000 \| 0.0568 \| 2.1897 \| \| 0.0448 \| 2.4806 \| 8000 \| 0.0561 \| 2.9282 \| \| 0.0517 \| 2.7907 \| 9000 \| 0.0539 \| 1.9350 \| \| 0.0382 \| 3.1008 \| 10000 \| 0.0531 \| 1.9003 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.2.2+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
tiiuae/Falcon3-10B-Base	tiiuae	2024-12-18T04:17:10Z	15,190	32	transformers	[ "transformers", "safetensors", "llama", "text-generation", "falcon3", "en", "fr", "es", "pt", "license:other", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-03T05:45:34Z	--- language: - en - fr - es - pt license: other library_name: transformers tags: - falcon3 license_name: falcon-llm-license license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html model-index: - name: Falcon3-10B-Base results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 36.48 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Base name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 41.38 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Base name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 24.77 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Base name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 12.75 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Base name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 14.17 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Base name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 36.0 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Base name: Open LLM Leaderboard --- <div align="center"> <img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/> </div> # Falcon3-10B-Base Falcon3 family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters. This repository contains the Falcon3-10B-Base. It achieves state-of-the-art results (at the time of release) on reasoning, language understanding, instruction following, code and mathematics tasks. Falcon3-10B-Base supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 32K. ⚠️ This is a raw, pretrained model, which should be further finetuned using SFT, RLHF, continued pretraining, etc. for most use cases. ## Model Details - Architecture - Transformer-based causal decoder-only architecture - 40 decoder blocks - Grouped Query Attention (GQA) for faster inference: 12 query heads and 4 key-value heads - Wider head dimension: 256 - High RoPE value to support long context understanding: 1000042 - Uses SwiGLu and RMSNorm - 32K context length - 131K vocab size - Depth up-scaled from Falcon3-7B-Base with continual pretraining on 2 Teratokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 1024 H100 GPU chips - Supports EN, FR, ES, PT - Developed by [Technology Innovation Institute](https://www.tii.ae) - License: TII Falcon-LLM License 2.0 - Model Release Date: December 2024 ## Getting started <details> <summary> Click to expand </summary> ```python import torch from transformers import pipeline pipe = pipeline( "text-generation", model="tiiuae/Falcon3-10B-Base", torch_dtype=torch.bfloat16, device_map="auto" ) response = pipe("Question: How many hours in one day? Answer: ") print(response[0]['generated_text']) ``` </details> <br> ## Benchmarks We report in the following table our internal pipeline benchmarks. - We use [lm-evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness). - We report raw scores. - We use same batch-size across all models. <table border="1" style="width: 100%; text-align: center; border-collapse: collapse;"> <colgroup> <col style="width: 10%;"> <col style="width: 10%;"> <col style="width: 7%;"> <col style="width: 7%;"> <col style="width: 7%;"> <col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;"> </colgroup> <thead> <tr> <th>Category</th> <th>Benchmark</th> <th>Gemma2-9B</th> <th>Yi1.5-9B</th> <th>Mistral-Nemo-Base-2407 (12B)</th> <th>Falcon3-10B-Base</th> </tr> </thead> <tbody> <tr> <td rowspan="3">General</td> <td>MMLU (5-shot)</td> <td>70.8</td> <td>69.6</td> <td>68.8</td> <td><b>73.1</b></td> </tr> <tr> <td>MMLU-PRO (5-shot)</td> <td>41.4</td> <td>39.3</td> <td>34.7</td> <td><b>42.5</b></td> </tr> <tr> <td>IFEval</td> <td>21.3</td> <td>29.1</td> <td>16.1</td> <td><b>36.4</b></td> </tr> <tr> <td rowspan="2">Math</td> <td>GSM8K (5-shot)</td> <td>69.1</td> <td>63.8</td> <td>55.3</td> <td><b>81.4</b></td> </tr> <tr> <td>MATH Lvl-5 (4-shot)</td> <td>10.5</td> <td>9.2</td> <td>4.9</td> <td><b>22.9</b></td> </tr> <tr> <td rowspan="4">Reasoning</td> <td>Arc Challenge (25-shot)</td> <td>67.5</td> <td>61.7</td> <td>64.4</td> <td><b>66.8</b></td> </tr> <tr> <td>GPQA (0-shot)</td> <td>33.4</td> <td><b>36.6</b></td> <td>28.8</td> <td>34.1</td> </tr> <tr> <td>MUSR (0-shot)</td> <td><b>45.3</b></td> <td>43.3</td> <td>39.2</td> <td>44.2</td> </tr> <tr> <td>BBH (3-shot)</td> <td>54.3</td> <td>51.3</td> <td>50.2</td> <td><b>59.7</b></td> </tr> <tr> <td rowspan="4">CommonSense Understanding</td> <td>PIQA (0-shot)</td> <td><b>83.0</b></td> <td>80.5</td> <td>82.1</td> <td>79.4</td> </tr> <tr> <td>SciQ (0-shot)</td> <td><b>97.1</b></td> <td>95.2</td> <td>95.2</td> <td>93.5</td> </tr> <tr> <td>Winogrande (0-shot)</td> <td><b>74.2</b></td> <td>72.7</td> <td>73.2</td> <td>73.6</td> </tr> <tr> <td>OpenbookQA (0-shot)</td> <td><b>47.2</b></td> <td>45.2</td> <td><b>47.2</b></td> <td>45.0</td> </tr> </tbody> </table> ## Useful links - View our [release blogpost](https://huggingface.co/blog/falcon3). - Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers. ## Technical Report Coming soon.... ## Citation If the Falcon3 family of models were helpful to your work, feel free to give us a cite. ``` @misc{Falcon3, title = {The Falcon 3 Family of Open Models}, url = {https://huggingface.co/blog/falcon3}, author = {Falcon-LLM Team}, month = {December}, year = {2024} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/tiiuae__Falcon3-10B-Base-details) \| Metric \|Value\| \|-------------------\|----:\| \|Avg. \|27.59\| \|IFEval (0-Shot) \|36.48\| \|BBH (3-Shot) \|41.38\| \|MATH Lvl 5 (4-Shot)\|24.77\| \|GPQA (0-shot) \|12.75\| \|MuSR (0-shot) \|14.17\| \|MMLU-PRO (5-shot) \|36.00\|
TheBlueObserver/Qwen2.5-14B-Instruct-MLX-0cb1b	TheBlueObserver	2024-12-18T04:12:44Z	77	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "mlx", "conversational", "en", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:quantized:Qwen/Qwen2.5-14B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "2-bit", "region:us" ]	text-generation	2024-12-18T04:10:32Z	--- base_model: Qwen/Qwen2.5-14B-Instruct language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - mlx --- # TheBlueObserver/Qwen2.5-14B-Instruct-MLX-0cb1b The Model [TheBlueObserver/Qwen2.5-14B-Instruct-MLX-0cb1b](https://huggingface.co/TheBlueObserver/Qwen2.5-14B-Instruct-MLX-0cb1b) was converted to MLX format from [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) using mlx-lm version 0.20.2. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("TheBlueObserver/Qwen2.5-14B-Instruct-MLX-0cb1b") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
ahsbdcpu/task-17-Qwen-Qwen1.5-0.5B	ahsbdcpu	2024-12-18T03:50:21Z	142	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-0.5B", "base_model:adapter:Qwen/Qwen1.5-0.5B", "region:us" ]	null	2024-12-05T03:48:45Z	--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0

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