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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-16 06:27:54	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 522 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-16 06:27:41	card stringlengths 11 1.01M
lesso/3f284b18-4942-4d4b-9975-4cd01648eae8	lesso	2025-02-03T16:30:35Z	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-1.8B", "base_model:adapter:Qwen/Qwen1.5-1.8B", "license:other", "region:us" ]	null	2025-02-03T16:28:39Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-1.8B tags: - axolotl - generated_from_trainer model-index: - name: 3f284b18-4942-4d4b-9975-4cd01648eae8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen1.5-1.8B bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - fbb4df04f8bea18e_train_data.json ds_type: json format: custom path: /workspace/input_data/fbb4df04f8bea18e_train_data.json type: field_input: parameter_schema field_instruction: description field_output: result_schema format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: lesso/3f284b18-4942-4d4b-9975-4cd01648eae8 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000101 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: linear max_grad_norm: 1.0 max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/god10/fbb4df04f8bea18e_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 554451db-c2af-4e91-88e9-b69068e3eaa6 wandb_project: ab-god10 wandb_run: your_name wandb_runid: 554451db-c2af-4e91-88e9-b69068e3eaa6 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 3f284b18-4942-4d4b-9975-4cd01648eae8 This model is a fine-tuned version of [Qwen/Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9138 ## 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: 0.000101 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 46 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7385 \| 0.0656 \| 1 \| 0.9138 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ardaspear/78c3ac87-9b5c-4502-98ed-a6153e6581aa	ardaspear	2025-02-03T16:29:57Z	9	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-7B", "base_model:adapter:Qwen/Qwen2.5-7B", "license:apache-2.0", "region:us" ]	null	2025-02-03T16:04:28Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-7B tags: - axolotl - generated_from_trainer model-index: - name: 78c3ac87-9b5c-4502-98ed-a6153e6581aa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2.5-7B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 53300725652833ff_train_data.json ds_type: json format: custom path: /workspace/input_data/53300725652833ff_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: ardaspear/78c3ac87-9b5c-4502-98ed-a6153e6581aa hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: 0 logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_steps: 100 micro_batch_size: 8 mlflow_experiment_name: /tmp/53300725652833ff_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: 407e6b8b-68e7-445c-b30d-f663e4ea110d wandb_project: Gradients-On-Five wandb_run: your_name wandb_runid: 407e6b8b-68e7-445c-b30d-f663e4ea110d warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 78c3ac87-9b5c-4502-98ed-a6153e6581aa This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1013 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0018 \| 1 \| 0.6666 \| \| 0.5315 \| 0.0161 \| 9 \| 0.4113 \| \| 0.2308 \| 0.0322 \| 18 \| 0.2118 \| \| 0.1619 \| 0.0483 \| 27 \| 0.1554 \| \| 0.1472 \| 0.0645 \| 36 \| 0.1318 \| \| 0.1323 \| 0.0806 \| 45 \| 0.1189 \| \| 0.1121 \| 0.0967 \| 54 \| 0.1106 \| \| 0.0996 \| 0.1128 \| 63 \| 0.1068 \| \| 0.1002 \| 0.1289 \| 72 \| 0.1039 \| \| 0.0983 \| 0.1450 \| 81 \| 0.1019 \| \| 0.0943 \| 0.1611 \| 90 \| 0.1016 \| \| 0.1038 \| 0.1773 \| 99 \| 0.1013 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
robiual-awal/8db3c849-1ff5-466d-af6f-5db314482b0a	robiual-awal	2025-02-03T16:28:56Z	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-1.8B", "base_model:adapter:Qwen/Qwen1.5-1.8B", "license:other", "region:us" ]	null	2025-02-03T16:26:54Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-1.8B tags: - axolotl - generated_from_trainer model-index: - name: 8db3c849-1ff5-466d-af6f-5db314482b0a results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen1.5-1.8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - fbb4df04f8bea18e_train_data.json ds_type: json format: custom path: /workspace/input_data/fbb4df04f8bea18e_train_data.json type: field_input: parameter_schema field_instruction: description field_output: result_schema format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: robiual-awal/8db3c849-1ff5-466d-af6f-5db314482b0a hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: constant max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/fbb4df04f8bea18e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 554451db-c2af-4e91-88e9-b69068e3eaa6 wandb_project: Birthday-SN56-30-Gradients-On-Demand wandb_run: your_name wandb_runid: 554451db-c2af-4e91-88e9-b69068e3eaa6 warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 8db3c849-1ff5-466d-af6f-5db314482b0a This model is a fine-tuned version of [Qwen/Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3194 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0021 \| 1 \| 0.7874 \| \| 0.426 \| 0.1034 \| 50 \| 0.3556 \| \| 0.3573 \| 0.2068 \| 100 \| 0.3357 \| \| 0.3477 \| 0.3102 \| 150 \| 0.3267 \| \| 0.3193 \| 0.4137 \| 200 \| 0.3194 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
chrispbailey/chrisface	chrispbailey	2025-02-03T16:28:02Z	31	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-02-03T16:00:59Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: CBTOK --- # Chrisface <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `CBTOK` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('chrispbailey/chrisface', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
hongngo/3112362f-2140-44eb-9695-e0bc8d2fd918	hongngo	2025-02-03T16:27:42Z	9	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-7B", "base_model:adapter:Qwen/Qwen2.5-7B", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-02-03T16:06:05Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-7B tags: - axolotl - generated_from_trainer model-index: - name: 3112362f-2140-44eb-9695-e0bc8d2fd918 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2.5-7B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 53300725652833ff_train_data.json ds_type: json format: custom path: /workspace/input_data/53300725652833ff_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: hongngo/3112362f-2140-44eb-9695-e0bc8d2fd918 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/53300725652833ff_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 407e6b8b-68e7-445c-b30d-f663e4ea110d wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 407e6b8b-68e7-445c-b30d-f663e4ea110d warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 3112362f-2140-44eb-9695-e0bc8d2fd918 This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1173 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.1083 \| 0.0895 \| 200 \| 0.1173 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
botenius/2c550002-d30e-43f9-be26-64fece71109b	botenius	2025-02-03T16:25:38Z	9	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-1.8B", "base_model:adapter:Qwen/Qwen1.5-1.8B", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-02-03T16:16:20Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-1.8B tags: - axolotl - generated_from_trainer model-index: - name: 2c550002-d30e-43f9-be26-64fece71109b results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen1.5-1.8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - fbb4df04f8bea18e_train_data.json ds_type: json format: custom path: /workspace/input_data/fbb4df04f8bea18e_train_data.json type: field_input: parameter_schema field_instruction: description field_output: result_schema format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: null eval_batch_size: 2 eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: true hub_model_id: botenius/2c550002-d30e-43f9-be26-64fece71109b hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0001 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/fbb4df04f8bea18e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null saves_per_epoch: null sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: 554451db-c2af-4e91-88e9-b69068e3eaa6 wandb_project: Gradients-On-13 wandb_run: your_name wandb_runid: 554451db-c2af-4e91-88e9-b69068e3eaa6 warmup_steps: 5 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 2c550002-d30e-43f9-be26-64fece71109b This model is a fine-tuned version of [Qwen/Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3292 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.1551 \| 0.4137 \| 200 \| 0.3292 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
abenius/005f6d4e-0118-4c7b-a184-bb1d9dd9001d	abenius	2025-02-03T16:25:35Z	9	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-1.8B", "base_model:adapter:Qwen/Qwen1.5-1.8B", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-02-03T16:16:06Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-1.8B tags: - axolotl - generated_from_trainer model-index: - name: 005f6d4e-0118-4c7b-a184-bb1d9dd9001d results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen1.5-1.8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - fbb4df04f8bea18e_train_data.json ds_type: json format: custom path: /workspace/input_data/fbb4df04f8bea18e_train_data.json type: field_input: parameter_schema field_instruction: description field_output: result_schema format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: null eval_batch_size: 2 eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: true hub_model_id: abenius/005f6d4e-0118-4c7b-a184-bb1d9dd9001d hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0001 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/fbb4df04f8bea18e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null saves_per_epoch: null sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: 554451db-c2af-4e91-88e9-b69068e3eaa6 wandb_project: Gradients-On-12 wandb_run: your_name wandb_runid: 554451db-c2af-4e91-88e9-b69068e3eaa6 warmup_steps: 5 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 005f6d4e-0118-4c7b-a184-bb1d9dd9001d This model is a fine-tuned version of [Qwen/Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3293 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.1539 \| 0.4137 \| 200 \| 0.3293 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
shibajustfor/ef4dba46-58b2-41f0-8bde-6e5a45bddabb	shibajustfor	2025-02-03T16:25:05Z	8	0	peft	[ "peft", "safetensors", "gemma2", "axolotl", "generated_from_trainer", "base_model:unsloth/gemma-2-9b-it", "base_model:adapter:unsloth/gemma-2-9b-it", "license:gemma", "region:us" ]	null	2025-02-03T15:40:41Z	--- library_name: peft license: gemma base_model: unsloth/gemma-2-9b-it tags: - axolotl - generated_from_trainer model-index: - name: ef4dba46-58b2-41f0-8bde-6e5a45bddabb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/gemma-2-9b-it bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 2d21cc849bf8be73_train_data.json ds_type: json format: custom path: /workspace/input_data/2d21cc849bf8be73_train_data.json type: field_input: Company Name field_instruction: Position field_output: Long Description format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: shibajustfor/ef4dba46-58b2-41f0-8bde-6e5a45bddabb hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/2d21cc849bf8be73_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: bfa59ab2-f602-48f6-9931-94de5892fa92 wandb_project: Birthday-SN56-39-Gradients-On-Demand wandb_run: your_name wandb_runid: bfa59ab2-f602-48f6-9931-94de5892fa92 warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # ef4dba46-58b2-41f0-8bde-6e5a45bddabb This model is a fine-tuned version of [unsloth/gemma-2-9b-it](https://huggingface.co/unsloth/gemma-2-9b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1739 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| 2.8250 \| \| 2.2407 \| 0.0030 \| 50 \| 2.2163 \| \| 2.1509 \| 0.0059 \| 100 \| 2.1906 \| \| 2.2152 \| 0.0089 \| 150 \| 2.1781 \| \| 2.1887 \| 0.0119 \| 200 \| 2.1739 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
fifxus/9af23880-1729-42ec-813b-3bae036f3ee5	fifxus	2025-02-03T16:25:01Z	9	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-7B", "base_model:adapter:Qwen/Qwen2.5-7B", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-02-03T16:04:53Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-7B tags: - axolotl - generated_from_trainer model-index: - name: 9af23880-1729-42ec-813b-3bae036f3ee5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2.5-7B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 53300725652833ff_train_data.json ds_type: json format: custom path: /workspace/input_data/53300725652833ff_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: null eval_batch_size: 2 eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: true hub_model_id: fifxus/9af23880-1729-42ec-813b-3bae036f3ee5 hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0001 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/53300725652833ff_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null saves_per_epoch: null sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: 407e6b8b-68e7-445c-b30d-f663e4ea110d wandb_project: Gradients-On-10 wandb_run: your_name wandb_runid: 407e6b8b-68e7-445c-b30d-f663e4ea110d warmup_steps: 5 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 9af23880-1729-42ec-813b-3bae036f3ee5 This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1175 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.2894 \| 0.0895 \| 200 \| 0.1175 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
alchemist69/460e92f7-6a95-4263-b634-8ab37ead4067	alchemist69	2025-02-03T16:23:47Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Hermes-3-Llama-3.1-8B", "base_model:adapter:NousResearch/Hermes-3-Llama-3.1-8B", "license:llama3", "region:us" ]	null	2025-02-03T15:54:16Z	--- library_name: peft license: llama3 base_model: NousResearch/Hermes-3-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: 460e92f7-6a95-4263-b634-8ab37ead4067 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Hermes-3-Llama-3.1-8B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 0beebe02a7ff1655_train_data.json ds_type: json format: custom path: /workspace/input_data/0beebe02a7ff1655_train_data.json type: field_input: product_title field_instruction: text field_output: preds format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: alchemist69/460e92f7-6a95-4263-b634-8ab37ead4067 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/0beebe02a7ff1655_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: d5d98e9d-ebfa-48d6-a38e-cd840c5c4bcb wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: d5d98e9d-ebfa-48d6-a38e-cd840c5c4bcb warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 460e92f7-6a95-4263-b634-8ab37ead4067 This model is a fine-tuned version of [NousResearch/Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4359 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1307 \| 0.0097 \| 1 \| 1.5566 \| \| 0.7738 \| 0.4866 \| 50 \| 0.5105 \| \| 0.7379 \| 0.9732 \| 100 \| 0.4602 \| \| 0.3517 \| 1.4599 \| 150 \| 0.4453 \| \| 0.3137 \| 1.9465 \| 200 \| 0.4359 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
adammandic87/0dc54dbf-10f3-475c-be93-40d578f5ed0d	adammandic87	2025-02-03T16:23:41Z	8	0	peft	[ "peft", "safetensors", "gemma2", "axolotl", "generated_from_trainer", "base_model:unsloth/gemma-2-9b-it", "base_model:adapter:unsloth/gemma-2-9b-it", "license:gemma", "region:us" ]	null	2025-02-03T15:39:31Z	--- library_name: peft license: gemma base_model: unsloth/gemma-2-9b-it tags: - axolotl - generated_from_trainer model-index: - name: 0dc54dbf-10f3-475c-be93-40d578f5ed0d results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/gemma-2-9b-it bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 2d21cc849bf8be73_train_data.json ds_type: json format: custom path: /workspace/input_data/2d21cc849bf8be73_train_data.json type: field_input: Company Name field_instruction: Position field_output: Long Description format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: adammandic87/0dc54dbf-10f3-475c-be93-40d578f5ed0d hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: constant max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/2d21cc849bf8be73_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: bfa59ab2-f602-48f6-9931-94de5892fa92 wandb_project: Birthday-SN56-34-Gradients-On-Demand wandb_run: your_name wandb_runid: bfa59ab2-f602-48f6-9931-94de5892fa92 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 0dc54dbf-10f3-475c-be93-40d578f5ed0d This model is a fine-tuned version of [unsloth/gemma-2-9b-it](https://huggingface.co/unsloth/gemma-2-9b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1726 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| 2.6627 \| \| 2.2375 \| 0.0030 \| 50 \| 2.2133 \| \| 2.1527 \| 0.0059 \| 100 \| 2.1887 \| \| 2.216 \| 0.0089 \| 150 \| 2.1805 \| \| 2.1862 \| 0.0119 \| 200 \| 2.1726 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
devngho/llama-ablation-random	devngho	2025-02-03T16:22:22Z	233	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-02T06:26:23Z	--- 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. 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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gorizont/test2	gorizont	2025-02-03T16:19:26Z	11	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-02-03T16:12:00Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: gorizont - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
dim-eleftheriou/Llama-3.1-8B-Instruct-S22-v0.1-GGUF-Q4_k_m	dim-eleftheriou	2025-02-03T16:19:05Z	48	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:quantized:meta-llama/Llama-3.1-8B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-31T13:50:08Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: dim-eleftheriou - License: apache-2.0 - Finetuned from model : meta-llama/Llama-3.1-8B-Instruct 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)
lesso/69465edf-dc1b-4cfc-82bb-97ff35ecc7e7	lesso	2025-02-03T16:18:56Z	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-1.8B", "base_model:adapter:Qwen/Qwen1.5-1.8B", "license:other", "region:us" ]	null	2025-02-03T16:16:06Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-1.8B tags: - axolotl - generated_from_trainer model-index: - name: 69465edf-dc1b-4cfc-82bb-97ff35ecc7e7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen1.5-1.8B bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - fbb4df04f8bea18e_train_data.json ds_type: json format: custom path: /workspace/input_data/fbb4df04f8bea18e_train_data.json type: field_input: parameter_schema field_instruction: description field_output: result_schema format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true group_by_length: true hub_model_id: lesso/69465edf-dc1b-4cfc-82bb-97ff35ecc7e7 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001018 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: linear max_grad_norm: 1.0 max_steps: 200 micro_batch_size: 4 mlflow_experiment_name: /tmp/god18/fbb4df04f8bea18e_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 554451db-c2af-4e91-88e9-b69068e3eaa6 wandb_project: ab-god18 wandb_run: your_name wandb_runid: 554451db-c2af-4e91-88e9-b69068e3eaa6 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 69465edf-dc1b-4cfc-82bb-97ff35ecc7e7 This model is a fine-tuned version of [Qwen/Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3028 ## 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: 0.0001018 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.6149 \| 0.0021 \| 1 \| 0.9138 \| \| 0.8035 \| 0.1034 \| 50 \| 0.3769 \| \| 0.2065 \| 0.2068 \| 100 \| 0.3786 \| \| 0.2027 \| 0.3102 \| 150 \| 0.3202 \| \| 0.1567 \| 0.4137 \| 200 \| 0.3028 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
kk-aivio/abc43cea-8db9-448b-addd-4fb398936787	kk-aivio	2025-02-03T16:18:27Z	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-1.8B", "base_model:adapter:Qwen/Qwen1.5-1.8B", "license:other", "region:us" ]	null	2025-02-03T16:16:20Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-1.8B tags: - axolotl - generated_from_trainer model-index: - name: abc43cea-8db9-448b-addd-4fb398936787 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen1.5-1.8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - fbb4df04f8bea18e_train_data.json ds_type: json format: custom path: /workspace/input_data/fbb4df04f8bea18e_train_data.json type: field_input: parameter_schema field_instruction: description field_output: result_schema format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: kk-aivio/abc43cea-8db9-448b-addd-4fb398936787 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/fbb4df04f8bea18e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 554451db-c2af-4e91-88e9-b69068e3eaa6 wandb_project: Birthday-SN56-17-Gradients-On-Demand wandb_run: your_name wandb_runid: 554451db-c2af-4e91-88e9-b69068e3eaa6 warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # abc43cea-8db9-448b-addd-4fb398936787 This model is a fine-tuned version of [Qwen/Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3218 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0021 \| 1 \| 0.9023 \| \| 0.4253 \| 0.1034 \| 50 \| 0.3562 \| \| 0.356 \| 0.2068 \| 100 \| 0.3356 \| \| 0.3511 \| 0.3102 \| 150 \| 0.3239 \| \| 0.3245 \| 0.4137 \| 200 \| 0.3218 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nttx/c0f64ddb-735d-4c01-8dbe-b67df874a19b	nttx	2025-02-03T16:18:09Z	9	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-7B", "base_model:adapter:Qwen/Qwen2.5-7B", "license:apache-2.0", "region:us" ]	null	2025-02-03T16:04:19Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-7B tags: - axolotl - generated_from_trainer model-index: - name: c0f64ddb-735d-4c01-8dbe-b67df874a19b results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2.5-7B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 53300725652833ff_train_data.json ds_type: json format: custom path: /workspace/input_data/53300725652833ff_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: null eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: nttx/c0f64ddb-735d-4c01-8dbe-b67df874a19b hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 4 mlflow_experiment_name: /tmp/53300725652833ff_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null saves_per_epoch: null sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 407e6b8b-68e7-445c-b30d-f663e4ea110d wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 407e6b8b-68e7-445c-b30d-f663e4ea110d warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # c0f64ddb-735d-4c01-8dbe-b67df874a19b This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1102 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.1546 \| 0.1791 \| 200 \| 0.1102 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
great0001/b136e731-070d-40ba-bc3a-c5b80d9d8896	great0001	2025-02-03T16:17:45Z	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-1.8B", "base_model:adapter:Qwen/Qwen1.5-1.8B", "license:other", "region:us" ]	null	2025-02-03T16:16:22Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-1.8B tags: - axolotl - generated_from_trainer model-index: - name: b136e731-070d-40ba-bc3a-c5b80d9d8896 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen1.5-1.8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - fbb4df04f8bea18e_train_data.json ds_type: json format: custom path: /workspace/input_data/fbb4df04f8bea18e_train_data.json type: field_input: parameter_schema field_instruction: description field_output: result_schema format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: false hub_model_id: great0001/b136e731-070d-40ba-bc3a-c5b80d9d8896 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/fbb4df04f8bea18e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 554451db-c2af-4e91-88e9-b69068e3eaa6 wandb_project: Mine-SN56-20-Gradients-On-Demand wandb_run: your_name wandb_runid: 554451db-c2af-4e91-88e9-b69068e3eaa6 warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # b136e731-070d-40ba-bc3a-c5b80d9d8896 This model is a fine-tuned version of [Qwen/Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3288 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0010 \| 1 \| 0.9023 \| \| 0.3638 \| 0.0517 \| 50 \| 0.3800 \| \| 0.4424 \| 0.1034 \| 100 \| 0.3530 \| \| 0.3991 \| 0.1551 \| 150 \| 0.3318 \| \| 0.3893 \| 0.2068 \| 200 \| 0.3288 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
rak-r05/cfe41fcb-7f4d-4c8b-93cf-51c7c009a657	rak-r05	2025-02-03T16:13:10Z	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-0.5B", "base_model:adapter:unsloth/Qwen2.5-0.5B", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-02-03T16:07:46Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-0.5B tags: - axolotl - generated_from_trainer model-index: - name: cfe41fcb-7f4d-4c8b-93cf-51c7c009a657 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2.5-0.5B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 564acdc5986155c6_train_data.json ds_type: json format: custom path: /workspace/input_data/564acdc5986155c6_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: rak-r05/cfe41fcb-7f4d-4c8b-93cf-51c7c009a657 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0004 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 2 mlflow_experiment_name: /tmp/564acdc5986155c6_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: d9a9992e-4f00-425a-9bf4-59fb268eb2e2 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: d9a9992e-4f00-425a-9bf4-59fb268eb2e2 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # cfe41fcb-7f4d-4c8b-93cf-51c7c009a657 This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B](https://huggingface.co/unsloth/Qwen2.5-0.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0020 \| 1 \| nan \| \| 0.0 \| 0.0763 \| 38 \| nan \| \| 0.0 \| 0.1526 \| 76 \| nan \| \| 0.0 \| 0.2289 \| 114 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
g-assismoraes/IMDB-TeenyTinyLlama-460m-sdv-interpol-mantainscore-0middle-run1	g-assismoraes	2025-02-03T16:12:03Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-02-03T15:58:04Z	--- 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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anaiis28/zararamirez	anaiis28	2025-02-03T16:08:14Z	7	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-02-03T15:44:55Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: zara --- # Zararamirez <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `zara` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('anaiis28/zararamirez', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
liquidrichard/lr2	liquidrichard	2025-02-03T16:06:19Z	32	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-03T16:01:55Z	--- 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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VortexHunter23/LeoPARD-Shed-0.1	VortexHunter23	2025-02-03T16:06:03Z	7	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-02-03T16:03:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(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]
icefog72/Ice0.81-03.02-RP	icefog72	2025-02-03T16:06:01Z	22	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-03T15:37:14Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # Ice0.81-03.02-RP 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 SLERP merge method. ### Models Merged The following models were included in the merge: * G:\FModels\Ice0.77-02.02-RP * G:\FModels\Ice0.80-03.02-RP ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: G:\FModels\Ice0.80-03.02-RP layer_range: [0, 32] - model: G:\FModels\Ice0.77-02.02-RP layer_range: [0, 32] merge_method: slerp base_model: G:\FModels\Ice0.77-02.02-RP parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 # fallback for rest of tensors dtype: bfloat16 ```
Darkhn/Qwen-2.5-Chuluun-v0.01-6.0bpw-h8-exl2	Darkhn	2025-02-03T16:02:49Z	8	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "en", "arxiv:2403.19522", "base_model:EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2", "base_model:merge:EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2", "base_model:Sao10K/72B-Qwen2.5-Kunou-v1", "base_model:merge:Sao10K/72B-Qwen2.5-Kunou-v1", "base_model:anthracite-org/magnum-v4-72b", "base_model:merge:anthracite-org/magnum-v4-72b", "base_model:migtissera/Tess-v2.5.2-Qwen2-72B", "base_model:merge:migtissera/Tess-v2.5.2-Qwen2-72B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2025-02-03T13:03:49Z	--- base_model: - EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2 - Sao10K/72B-Qwen2.5-Kunou-v1 - anthracite-org/magnum-v4-72b - migtissera/Tess-v2.5.2-Qwen2-72B library_name: transformers tags: - mergekit - merge language: - en --- # Chuluun-Qwen2.5-72B-v0.01 ![image/png](https://huggingface.co/DatToad/Chuluun-Qwen2.5-72B-v0.01/resolve/main/00008-1523559621.png) GGUF quants available here: https://huggingface.co/bartowski/Chuluun-Qwen2.5-72B-v0.01-GGUF This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). The models in this merge are some of my favorites and I found I liked all of them for different reasons. I believe this model is greater than the sum of its parts - it has the storywriting and language of Eva and Kunou, the spiciness of Magnum, and the uncensored intelligence of Tess. It excels in handling multiple characters and keeping their thoughts, speech, and actions separate, including scene changes. It also appears to match dialogue well to the characters and their backgrounds. Model_stock was the method used, it's very straightforward and quite fast, the bottleneck seemed to be my NVMe drive. All source models use ChatML prompt formatting and it responds very well. For testing purposes I am using a temperature of 1.08, rep pen of 0.03, and DRY with 0.6 (most Qwen models seem to need DRY). All other samplers are neutralized. My sysprompt is a modified version of Konnect's, but I expect you should be able to use this with your favorite. ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using migtissera/Tess-v2.5.2-Qwen2-72B as a base. ### Models Merged The following models were included in the merge: * EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2 * Sao10K/72B-Qwen2.5-Kunou-v1 * anthracite-org/magnum-v4-72b ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2 - model: Sao10K/72B-Qwen2.5-Kunou-v1 - model: anthracite-org/magnum-v4-72b merge_method: model_stock base_model: migtissera/Tess-v2.5.2-Qwen2-72B parameters: filter_wise: false dytpe: float16 name: DatToad/Chuluun-Qwen2.5-72B-v0.01 ``` ### Thank Yous! My work is built on the backs of giants - all I did was some cooking in the kitchen. Much credit goes to all of the following: - Allura-Org, for the EVA models and their kind support as I've learned some of the finer points of working with LLMs. - Sao10k, creator of Euryale and Kunou and inspiring so many writers along the way - Sophosympatheia, their original merge of the legendary Midnight Miqu has entertained countless writers and inspired me to give merging a try - #horde in the KoboldAI Discord, who've also answered a lot of questions I've had
theship87/qwen25-14b-fork	theship87	2025-02-03T16:01:00Z	13	0	null	[ "safetensors", "region:us" ]	null	2025-02-03T14:20:10Z	Found. Redirecting to https://cdn-lfs-us-1.hf.co/repos/f3/23/f3232648405ad8ff96e77b9a8f748a8932bf5deea046bf7881d798e546fdd510/34d8c78517eb6345042cf65974c8d1f166dd8c6424a7bd8df0b09b955d4101ee?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27README.md%3B+filename%3D%22README.md%22%3B&response-content-type=text%2Fmarkdown&Expires=1739043885&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTczOTA0Mzg4NX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmhmLmNvL3JlcG9zL2YzLzIzL2YzMjMyNjQ4NDA1YWQ4ZmY5NmU3N2I5YThmNzQ4YTg5MzJiZjVkZWVhMDQ2YmY3ODgxZDc5OGU1NDZmZGQ1MTAvMzRkOGM3ODUxN2ViNjM0NTA0MmNmNjU5NzRjOGQxZjE2NmRkOGM2NDI0YTdiZDhkZjBiMDliOTU1ZDQxMDFlZT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=QPtiIgHsSVpltpOLYbL-3WtTTuhxUPsMj7arxOI8o3uHX33bh979Snr-x68r0ipumve81rPDkQn-TyNpMU46CBY2teE1r2HDEDWb9nMla8jdYPTGeRrBYQkR197n2jX73jnyDBQmy8xHhFQOZsJvAyZGDb7TS0uQoK9jLHKBCDeblDLrmWzIIi48M3a9PXAvAejGnIOYeP6SFy%7E081Y9XymiaQgdQfBp41qesxOF0CN28cLHgZ1j4f77%7EAa82GrgadcgLOpbmqJZJH3mJRgtuXCAtDriwODeltGIwRf0aqi0lE2SuTg7WciQqSgkM9fQaVjwP1zX1LNh2%7EVTYKCqFA__&Key-Pair-Id=K24J24Z295AEI9
g-assismoraes/IMDB-TeenyTinyLlama-460m-sdv-interpol-mantainscore-0final-run1	g-assismoraes	2025-02-03T15:50:52Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-02-03T15:49:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/L3-R1-Framework-70B-GGUF	mradermacher	2025-02-03T15:50:16Z	241	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Kirkito/L3-R1-Framework-70B", "base_model:quantized:Kirkito/L3-R1-Framework-70B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-03T06:25:13Z	--- base_model: Kirkito/L3-R1-Framework-70B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Kirkito/L3-R1-Framework-70B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3-R1-Framework-70B-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/L3-R1-Framework-70B-GGUF/resolve/main/L3-R1-Framework-70B.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-R1-Framework-70B-GGUF/resolve/main/L3-R1-Framework-70B.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-R1-Framework-70B-GGUF/resolve/main/L3-R1-Framework-70B.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-R1-Framework-70B-GGUF/resolve/main/L3-R1-Framework-70B.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-R1-Framework-70B-GGUF/resolve/main/L3-R1-Framework-70B.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-R1-Framework-70B-GGUF/resolve/main/L3-R1-Framework-70B.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3-R1-Framework-70B-GGUF/resolve/main/L3-R1-Framework-70B.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3-R1-Framework-70B-GGUF/resolve/main/L3-R1-Framework-70B.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-R1-Framework-70B-GGUF/resolve/main/L3-R1-Framework-70B.Q5_K_M.gguf) \| Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/L3-R1-Framework-70B-GGUF/resolve/main/L3-R1-Framework-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3-R1-Framework-70B-GGUF/resolve/main/L3-R1-Framework-70B.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/L3-R1-Framework-70B-GGUF/resolve/main/L3-R1-Framework-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3-R1-Framework-70B-GGUF/resolve/main/L3-R1-Framework-70B.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mrferr3t/4acee5c9-3461-45a4-bc58-dba0ba80e50d	mrferr3t	2025-02-03T15:47:13Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:openlm-research/open_llama_3b", "base_model:adapter:openlm-research/open_llama_3b", "license:apache-2.0", "region:us" ]	null	2025-02-03T15:45:00Z	--- library_name: peft license: apache-2.0 base_model: openlm-research/open_llama_3b tags: - axolotl - generated_from_trainer model-index: - name: 4acee5c9-3461-45a4-bc58-dba0ba80e50d results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora auto_find_batch_size: true base_model: openlm-research/open_llama_3b bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - 46cd501cfa0a0e7c_train_data.json ds_type: json format: custom path: /workspace/input_data/46cd501cfa0a0e7c_train_data.json type: field_instruction: question field_output: best_answer format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 3 early_stopping_threshold: 0.001 eval_max_new_tokens: 128 eval_steps: 40 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: false hub_model_id: mrferr3t/4acee5c9-3461-45a4-bc58-dba0ba80e50d hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0003 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 100 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine micro_batch_size: 32 mlflow_experiment_name: /tmp/46cd501cfa0a0e7c_train_data.json model_type: AutoModelForCausalLM num_epochs: 50 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true s2_attention: null sample_packing: false save_steps: 40 saves_per_epoch: 0 sequence_len: 512 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 07100248-24d8-42f6-bfe1-02c110efe579 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 07100248-24d8-42f6-bfe1-02c110efe579 warmup_ratio: 0.05 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 4acee5c9-3461-45a4-bc58-dba0ba80e50d This model is a fine-tuned version of [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9079 ## 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: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 23 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0263 \| 1 \| 0.8790 \| \| No log \| 1.0526 \| 40 \| 0.6741 \| \| No log \| 2.1053 \| 80 \| 0.6938 \| \| 0.5293 \| 3.1579 \| 120 \| 0.8056 \| \| 0.5293 \| 4.2105 \| 160 \| 0.9079 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
osoblanco/COPA-all_extractions-arm-rus-v4_test	osoblanco	2025-02-03T15:46:02Z	28	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-01-27T14:03:48Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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abenius/c6d8cc82-ea57-4214-b4be-3ec1c35b3c14	abenius	2025-02-03T15:45:39Z	7	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:openlm-research/open_llama_3b", "base_model:adapter:openlm-research/open_llama_3b", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-02-03T15:41:15Z	--- library_name: peft license: apache-2.0 base_model: openlm-research/open_llama_3b tags: - axolotl - generated_from_trainer model-index: - name: c6d8cc82-ea57-4214-b4be-3ec1c35b3c14 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: openlm-research/open_llama_3b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 46cd501cfa0a0e7c_train_data.json ds_type: json format: custom path: /workspace/input_data/46cd501cfa0a0e7c_train_data.json type: field_instruction: question field_output: best_answer format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: null eval_batch_size: 2 eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: true hub_model_id: abenius/c6d8cc82-ea57-4214-b4be-3ec1c35b3c14 hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0001 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/46cd501cfa0a0e7c_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null saves_per_epoch: null sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: 07100248-24d8-42f6-bfe1-02c110efe579 wandb_project: Gradients-On-12 wandb_run: your_name wandb_runid: 07100248-24d8-42f6-bfe1-02c110efe579 warmup_steps: 5 weight_decay: 0.01 xformers_attention: null ``` </details><br> # c6d8cc82-ea57-4214-b4be-3ec1c35b3c14 This model is a fine-tuned version of [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6453 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 76 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.6319 \| 0.9967 \| 75 \| 0.6544 \| \| 1.2358 \| 1.0100 \| 76 \| 0.6453 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF	mradermacher	2025-02-03T15:44:53Z	124	2	transformers	[ "transformers", "gguf", "en", "base_model:ChaoticNeutrals/Poppy_Porpoise-1.30-L3-8B", "base_model:quantized:ChaoticNeutrals/Poppy_Porpoise-1.30-L3-8B", "license:other", "endpoints_compatible", "region:us" ]	null	2024-05-31T06:16:06Z	--- base_model: ChaoticNeutrals/Poppy_Porpoise-1.30-L3-8B language: - en library_name: transformers license: other quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-1.30-L3-8B *The model creator strongly suggests using the [0.72](https://huggingface.co/mradermacher/Poppy_Porpoise-0.72-L3-8B-GGUF) model at this time, as it is better quality* <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF/resolve/main/Poppy_Porpoise-1.30-L3-8B.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF/resolve/main/Poppy_Porpoise-1.30-L3-8B.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF/resolve/main/Poppy_Porpoise-1.30-L3-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF/resolve/main/Poppy_Porpoise-1.30-L3-8B.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF/resolve/main/Poppy_Porpoise-1.30-L3-8B.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF/resolve/main/Poppy_Porpoise-1.30-L3-8B.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF/resolve/main/Poppy_Porpoise-1.30-L3-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF/resolve/main/Poppy_Porpoise-1.30-L3-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF/resolve/main/Poppy_Porpoise-1.30-L3-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF/resolve/main/Poppy_Porpoise-1.30-L3-8B.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF/resolve/main/Poppy_Porpoise-1.30-L3-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF/resolve/main/Poppy_Porpoise-1.30-L3-8B.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF/resolve/main/Poppy_Porpoise-1.30-L3-8B.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF/resolve/main/Poppy_Porpoise-1.30-L3-8B.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Poppy_Porpoise-1.30-L3-8B-GGUF/resolve/main/Poppy_Porpoise-1.30-L3-8B.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
clarxus/35a52423-da1b-4d7f-af2e-ea8c49d0f83e	clarxus	2025-02-03T15:44:19Z	6	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:adapter:EleutherAI/pythia-70m-deduped", "license:apache-2.0", "region:us" ]	null	2025-02-03T15:38:31Z	--- library_name: peft license: apache-2.0 base_model: EleutherAI/pythia-70m-deduped tags: - axolotl - generated_from_trainer model-index: - name: 35a52423-da1b-4d7f-af2e-ea8c49d0f83e results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: EleutherAI/pythia-70m-deduped bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 0887480b4ca433bf_train_data.json ds_type: json format: custom path: /workspace/input_data/0887480b4ca433bf_train_data.json type: field_input: '' field_instruction: title field_output: sum format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: clarxus/35a52423-da1b-4d7f-af2e-ea8c49d0f83e hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: 0 logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_steps: 100 micro_batch_size: 8 mlflow_experiment_name: /tmp/0887480b4ca433bf_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 1024 special_tokens: pad_token: <\|endoftext\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: 83defa9c-70d4-4a69-b9b8-05277b37d267 wandb_project: Gradients-On-Seven wandb_run: your_name wandb_runid: 83defa9c-70d4-4a69-b9b8-05277b37d267 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 35a52423-da1b-4d7f-af2e-ea8c49d0f83e This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on the None dataset. It achieves the following results on the evaluation set: - Loss: 11.5206 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0002 \| 1 \| 26.8630 \| \| 107.3514 \| 0.0015 \| 9 \| 26.7927 \| \| 108.9375 \| 0.0029 \| 18 \| 26.5881 \| \| 98.7784 \| 0.0044 \| 27 \| 25.9847 \| \| 86.807 \| 0.0059 \| 36 \| 20.4831 \| \| 64.0603 \| 0.0073 \| 45 \| 17.3812 \| \| 67.4325 \| 0.0088 \| 54 \| 16.6399 \| \| 60.6494 \| 0.0103 \| 63 \| 14.4517 \| \| 52.3924 \| 0.0117 \| 72 \| 12.4511 \| \| 47.3132 \| 0.0132 \| 81 \| 11.6997 \| \| 48.5187 \| 0.0147 \| 90 \| 11.5402 \| \| 45.148 \| 0.0162 \| 99 \| 11.5206 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Triangle104/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview-Q5_K_M-GGUF	Triangle104	2025-02-03T15:44:01Z	26	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview", "base_model:quantized:FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-03T15:40:31Z	--- license: apache-2.0 tags: - llama-cpp - gguf-my-repo base_model: FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview --- # Triangle104/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview-Q5_K_M-GGUF This model was converted to GGUF format from [`FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview`](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview) for more details on the model. --- FuseO1-Preview is our initial endeavor to enhance the System-II reasoning capabilities of large language models (LLMs) through innovative model fusion techniques. By employing our advanced SCE merging methodologies, we integrate multiple open-source o1-like LLMs into a unified model. Our goal is to incorporate the distinct knowledge and strengths from different reasoning LLMs into a single, unified model with strong System-II reasoning abilities, particularly in mathematics, coding, and science domains. To achieve this, we conduct two types of model merging: Long-Long Reasoning Merging: This approach involves model fusion across LLMs that utilize long-CoT reasoning, with the goal of enhancing long-CoT reasoning capabilities. The resulted FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview achieves a Pass@1 accuracy of 74.0 on AIME24, demonstrating significant performance improvements compared to the OpenAI o1-preview (44.6) and OpenAI o1-mini (63.4), even approaching OpenAI o1 (79.2). Long-Short Reasoning Merging: This approach involves model fusion between long-CoT and short-CoT LLMs, aiming to improve reasoning capabilities in both long and short reasoning processes. The resulted FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview and FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview is capable of utilizing both long and short reasoning processes and demonstrates relatively strong performance in long reasoning tasks. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview-Q5_K_M-GGUF --hf-file fuseo1-deepseekr1-qwq-skyt1-flash-32b-preview-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview-Q5_K_M-GGUF --hf-file fuseo1-deepseekr1-qwq-skyt1-flash-32b-preview-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview-Q5_K_M-GGUF --hf-file fuseo1-deepseekr1-qwq-skyt1-flash-32b-preview-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview-Q5_K_M-GGUF --hf-file fuseo1-deepseekr1-qwq-skyt1-flash-32b-preview-q5_k_m.gguf -c 2048 ```
adammandic87/4979bf84-da68-4f01-8109-2fad3fe71306	adammandic87	2025-02-03T15:42:52Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:openlm-research/open_llama_3b", "base_model:adapter:openlm-research/open_llama_3b", "license:apache-2.0", "region:us" ]	null	2025-02-03T15:41:42Z	--- library_name: peft license: apache-2.0 base_model: openlm-research/open_llama_3b tags: - axolotl - generated_from_trainer model-index: - name: 4979bf84-da68-4f01-8109-2fad3fe71306 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: openlm-research/open_llama_3b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 46cd501cfa0a0e7c_train_data.json ds_type: json format: custom path: /workspace/input_data/46cd501cfa0a0e7c_train_data.json type: field_instruction: question field_output: best_answer format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: adammandic87/4979bf84-da68-4f01-8109-2fad3fe71306 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/46cd501cfa0a0e7c_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 07100248-24d8-42f6-bfe1-02c110efe579 wandb_project: Birthday-SN56-13-Gradients-On-Demand wandb_run: your_name wandb_runid: 07100248-24d8-42f6-bfe1-02c110efe579 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 4979bf84-da68-4f01-8109-2fad3fe71306 This model is a fine-tuned version of [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6747 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 76 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7147 \| 0.0133 \| 1 \| 0.8986 \| \| 0.6782 \| 0.2525 \| 19 \| 0.7203 \| \| 0.5019 \| 0.5050 \| 38 \| 0.6900 \| \| 0.3634 \| 0.7575 \| 57 \| 0.6795 \| \| 1.1565 \| 1.0100 \| 76 \| 0.6747 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
liquidrichard/lr1	liquidrichard	2025-02-03T15:42:01Z	32	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-03T15:39:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
bertin-project/bertin-base-gaussian-exp-512seqlen	bertin-project	2025-02-03T15:41:09Z	81	1	transformers	[ "transformers", "pytorch", "jax", "tensorboard", "joblib", "safetensors", "roberta", "fill-mask", "spanish", "es", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: es license: cc-by-4.0 tags: - spanish - roberta pipeline_tag: fill-mask widget: - text: Fui a la librería a comprar un <mask>. --- This is a RoBERTa-base model trained from scratch in Spanish. The training dataset is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (using a Gaussian function), discarding more often documents with very large values (poor quality) of very small values (short, repetitive texts). This model takes the one using [sequence length 128](https://huggingface.co/bertin-project/bertin-base-gaussian) and trains during 25.000 steps using sequence length 512. Please see our main [card](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for more information. This is part of the [Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google. ## Team members - Eduardo González ([edugp](https://huggingface.co/edugp)) - Javier de la Rosa ([versae](https://huggingface.co/versae)) - Manu Romero ([mrm8488](https://huggingface.co/)) - María Grandury ([mariagrandury](https://huggingface.co/)) - Pablo González de Prado ([Pablogps](https://huggingface.co/Pablogps)) - Paulo Villegas ([paulo](https://huggingface.co/paulo))
mrferr3t/9752cb85-6a0f-4384-9e08-7e395f2c00c3	mrferr3t	2025-02-03T15:40:48Z	17	0	peft	[ "peft", "safetensors", "opt", "axolotl", "generated_from_trainer", "base_model:facebook/opt-1.3b", "base_model:adapter:facebook/opt-1.3b", "license:other", "region:us" ]	null	2025-02-03T14:57:54Z	--- library_name: peft license: other base_model: facebook/opt-1.3b tags: - axolotl - generated_from_trainer model-index: - name: 9752cb85-6a0f-4384-9e08-7e395f2c00c3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora auto_find_batch_size: true base_model: facebook/opt-1.3b bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - 20fc9edc61053699_train_data.json ds_type: json format: custom path: /workspace/input_data/20fc9edc61053699_train_data.json type: field_input: answer field_instruction: problem field_output: solution format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 3 early_stopping_threshold: 0.001 eval_max_new_tokens: 128 eval_steps: 40 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: false hub_model_id: mrferr3t/9752cb85-6a0f-4384-9e08-7e395f2c00c3 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0003 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 100 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine micro_batch_size: 32 mlflow_experiment_name: /tmp/20fc9edc61053699_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true s2_attention: null sample_packing: false save_steps: 40 saves_per_epoch: 0 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: efadbf9b-21a1-4759-b077-7318afa3023b wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: efadbf9b-21a1-4759-b077-7318afa3023b warmup_ratio: 0.05 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 9752cb85-6a0f-4384-9e08-7e395f2c00c3 This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3769 ## 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: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 31 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0080 \| 1 \| 1.7673 \| \| No log \| 0.3187 \| 40 \| 1.6043 \| \| No log \| 0.6375 \| 80 \| 1.5327 \| \| 3.2674 \| 0.9562 \| 120 \| 1.4930 \| \| 3.2674 \| 1.2749 \| 160 \| 1.4741 \| \| 3.0074 \| 1.5936 \| 200 \| 1.4569 \| \| 3.0074 \| 1.9124 \| 240 \| 1.4434 \| \| 3.0074 \| 2.2311 \| 280 \| 1.4310 \| \| 2.8164 \| 2.5498 \| 320 \| 1.4232 \| \| 2.8164 \| 2.8685 \| 360 \| 1.4143 \| \| 2.7232 \| 3.1873 \| 400 \| 1.4068 \| \| 2.7232 \| 3.5060 \| 440 \| 1.4026 \| \| 2.7232 \| 3.8247 \| 480 \| 1.3945 \| \| 2.6641 \| 4.1434 \| 520 \| 1.3931 \| \| 2.6641 \| 4.4622 \| 560 \| 1.3937 \| \| 2.5637 \| 4.7809 \| 600 \| 1.3833 \| \| 2.5637 \| 5.0996 \| 640 \| 1.3867 \| \| 2.5637 \| 5.4183 \| 680 \| 1.3838 \| \| 2.4995 \| 5.7371 \| 720 \| 1.3809 \| \| 2.4995 \| 6.0558 \| 760 \| 1.3788 \| \| 2.4638 \| 6.3745 \| 800 \| 1.3829 \| \| 2.4638 \| 6.6932 \| 840 \| 1.3788 \| \| 2.4638 \| 7.0120 \| 880 \| 1.3762 \| \| 2.4062 \| 7.3307 \| 920 \| 1.3788 \| \| 2.4062 \| 7.6494 \| 960 \| 1.3788 \| \| 2.3963 \| 7.9681 \| 1000 \| 1.3769 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
mrferr3t/3a83c85c-35e4-4b65-be0a-b202b746a33b	mrferr3t	2025-02-03T15:39:54Z	6	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-7B-Instruct", "base_model:adapter:Qwen/Qwen2-7B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-02-03T15:07:13Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 3a83c85c-35e4-4b65-be0a-b202b746a33b results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora auto_find_batch_size: true base_model: Qwen/Qwen2-7B-Instruct bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - cb37487dbb01a482_train_data.json ds_type: json format: custom path: /workspace/input_data/cb37487dbb01a482_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 3 early_stopping_threshold: 0.001 eval_max_new_tokens: 128 eval_steps: 20 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: false hub_model_id: mrferr3t/3a83c85c-35e4-4b65-be0a-b202b746a33b hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0003 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 100 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine micro_batch_size: 32 mlflow_experiment_name: /tmp/cb37487dbb01a482_train_data.json model_type: AutoModelForCausalLM num_epochs: 5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true s2_attention: null sample_packing: false save_steps: 20 saves_per_epoch: 0 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3dc7b34c-0e6a-4c76-9da5-8ee774ed311c wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 3dc7b34c-0e6a-4c76-9da5-8ee774ed311c warmup_ratio: 0.05 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 3a83c85c-35e4-4b65-be0a-b202b746a33b This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2381 ## 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: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 132 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0005 \| 1 \| 3.1245 \| \| No log \| 0.0094 \| 20 \| 2.6437 \| \| No log \| 0.0189 \| 40 \| 1.4498 \| \| No log \| 0.0283 \| 60 \| 1.3342 \| \| No log \| 0.0378 \| 80 \| 1.3114 \| \| 1.8194 \| 0.0472 \| 100 \| 1.3027 \| \| 1.8194 \| 0.0567 \| 120 \| 1.2915 \| \| 1.8194 \| 0.0661 \| 140 \| 1.2847 \| \| 1.8194 \| 0.0756 \| 160 \| 1.2833 \| \| 1.8194 \| 0.0850 \| 180 \| 1.2777 \| \| 1.3132 \| 0.0945 \| 200 \| 1.2766 \| \| 1.3132 \| 0.1039 \| 220 \| 1.2699 \| \| 1.3132 \| 0.1134 \| 240 \| 1.2696 \| \| 1.3132 \| 0.1228 \| 260 \| 1.2722 \| \| 1.3132 \| 0.1323 \| 280 \| 1.2662 \| \| 1.293 \| 0.1417 \| 300 \| 1.2672 \| \| 1.293 \| 0.1512 \| 320 \| 1.2607 \| \| 1.293 \| 0.1606 \| 340 \| 1.2645 \| \| 1.293 \| 0.1701 \| 360 \| 1.2535 \| \| 1.293 \| 0.1795 \| 380 \| 1.2504 \| \| 1.2733 \| 0.1890 \| 400 \| 1.2585 \| \| 1.2733 \| 0.1984 \| 420 \| 1.2496 \| \| 1.2733 \| 0.2079 \| 440 \| 1.2444 \| \| 1.2733 \| 0.2173 \| 460 \| 1.2544 \| \| 1.2733 \| 0.2268 \| 480 \| 1.2418 \| \| 1.2727 \| 0.2362 \| 500 \| 1.2435 \| \| 1.2727 \| 0.2457 \| 520 \| 1.2499 \| \| 1.2727 \| 0.2551 \| 540 \| 1.2339 \| \| 1.2727 \| 0.2646 \| 560 \| 1.2442 \| \| 1.2727 \| 0.2740 \| 580 \| 1.2438 \| \| 1.2543 \| 0.2835 \| 600 \| 1.2381 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
clarxus/e33a6025-8bdf-42c1-873d-100d543f0c82	clarxus	2025-02-03T15:37:48Z	11	0	peft	[ "peft", "safetensors", "phi", "axolotl", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2025-02-03T15:05:29Z	--- library_name: peft license: mit base_model: microsoft/phi-2 tags: - axolotl - generated_from_trainer model-index: - name: e33a6025-8bdf-42c1-873d-100d543f0c82 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: microsoft/phi-2 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 14adcf56bd267abc_train_data.json ds_type: json format: custom path: /workspace/input_data/14adcf56bd267abc_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: clarxus/e33a6025-8bdf-42c1-873d-100d543f0c82 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: 0 logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_steps: 100 micro_batch_size: 8 mlflow_experiment_name: /tmp/14adcf56bd267abc_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 1024 special_tokens: pad_token: <\|endoftext\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: 3bf53e4e-e50e-483e-a51f-f8ec21733093 wandb_project: Gradients-On-Seven wandb_run: your_name wandb_runid: 3bf53e4e-e50e-483e-a51f-f8ec21733093 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # e33a6025-8bdf-42c1-873d-100d543f0c82 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0212 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0007 \| 1 \| 1.2698 \| \| 1.2291 \| 0.0059 \| 9 \| 1.2495 \| \| 1.1927 \| 0.0118 \| 18 \| 1.1368 \| \| 1.0485 \| 0.0176 \| 27 \| 1.0832 \| \| 1.0261 \| 0.0235 \| 36 \| 1.0561 \| \| 1.1704 \| 0.0294 \| 45 \| 1.0424 \| \| 1.03 \| 0.0353 \| 54 \| 1.0352 \| \| 1.0466 \| 0.0411 \| 63 \| 1.0296 \| \| 1.0381 \| 0.0470 \| 72 \| 1.0247 \| \| 1.0368 \| 0.0529 \| 81 \| 1.0222 \| \| 0.9842 \| 0.0588 \| 90 \| 1.0214 \| \| 1.0276 \| 0.0646 \| 99 \| 1.0212 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Limett/lisandro	Limett	2025-02-03T15:37:01Z	9	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-02-03T15:00:41Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: lisandro --- # Lisandro <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `lisandro` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Limett/lisandro', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
AmedeoBonatti/nlp_te_mlm_scibert	AmedeoBonatti	2025-02-03T15:36:46Z	22	0	null	[ "safetensors", "bert", "generated_from_trainer", "base_model:allenai/scibert_scivocab_uncased", "base_model:finetune:allenai/scibert_scivocab_uncased", "region:us" ]	null	2025-01-26T12:03:56Z	--- base_model: allenai/scibert_scivocab_uncased tags: - generated_from_trainer model-index: - name: nlp_te_mlm_scibert 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. --> # nlp_te_mlm_scibert This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1478 ## 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: 16 - eval_batch_size: 8 - seed: 5678 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 1.3828 \| 0.9963 \| 152 \| 1.2566 \| \| 1.3087 \| 1.9992 \| 305 \| 1.2295 \| \| 1.289 \| 2.9955 \| 457 \| 1.2237 \| \| 1.262 \| 3.9984 \| 610 \| 1.2054 \| \| 1.2516 \| 4.9947 \| 762 \| 1.1999 \| \| 1.229 \| 5.9975 \| 915 \| 1.1944 \| \| 1.2272 \| 6.9939 \| 1067 \| 1.1880 \| \| 1.2066 \| 7.9967 \| 1220 \| 1.1879 \| \| 1.1991 \| 8.9996 \| 1373 \| 1.1807 \| \| 1.1978 \| 9.9959 \| 1525 \| 1.1760 \| \| 1.1803 \| 10.9988 \| 1678 \| 1.1724 \| \| 1.1819 \| 11.9951 \| 1830 \| 1.1716 \| \| 1.1659 \| 12.9980 \| 1983 \| 1.1731 \| \| 1.1658 \| 13.9943 \| 2135 \| 1.1673 \| \| 1.1524 \| 14.9971 \| 2288 \| 1.1669 \| \| 1.1481 \| 16.0 \| 2441 \| 1.1590 \| \| 1.1468 \| 16.9963 \| 2593 \| 1.1626 \| \| 1.1361 \| 17.9992 \| 2746 \| 1.1623 \| \| 1.1371 \| 18.9955 \| 2898 \| 1.1582 \| \| 1.125 \| 19.9984 \| 3051 \| 1.1540 \| \| 1.1276 \| 20.9947 \| 3203 \| 1.1551 \| \| 1.1143 \| 21.9975 \| 3356 \| 1.1518 \| \| 1.118 \| 22.9939 \| 3508 \| 1.1550 \| \| 1.104 \| 23.9967 \| 3661 \| 1.1525 \| \| 1.1011 \| 24.9996 \| 3814 \| 1.1483 \| \| 1.1061 \| 25.9959 \| 3966 \| 1.1533 \| \| 1.0941 \| 26.9988 \| 4119 \| 1.1473 \| \| 1.0951 \| 27.9951 \| 4271 \| 1.1444 \| \| 1.0866 \| 28.9980 \| 4424 \| 1.1462 \| \| 1.089 \| 29.9943 \| 4576 \| 1.1453 \| \| 1.0768 \| 30.9971 \| 4729 \| 1.1496 \| \| 1.0744 \| 32.0 \| 4882 \| 1.1493 \| \| 1.0773 \| 32.9963 \| 5034 \| 1.1478 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.2.1 - Datasets 2.19.2 - Tokenizers 0.19.1
ajku2199/Llama-2-7b-hf_process_prob9_dataset2_n1000_seed42_epochs10_batch8_qlora	ajku2199	2025-02-03T15:36:39Z	10	0	peft	[ "peft", "safetensors", "region:us" ]	null	2025-01-10T04:22:22Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0
bane5631/af85d1b0-3edb-47c7-9737-4b4d41cae500	bane5631	2025-02-03T15:35:01Z	9	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-7B-Instruct", "base_model:adapter:Qwen/Qwen2-7B-Instruct", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-02-03T15:05:34Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: af85d1b0-3edb-47c7-9737-4b4d41cae500 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2-7B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - cb37487dbb01a482_train_data.json ds_type: json format: custom path: /workspace/input_data/cb37487dbb01a482_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: null eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: bane5631/af85d1b0-3edb-47c7-9737-4b4d41cae500 hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0001 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 4 mlflow_experiment_name: /tmp/cb37487dbb01a482_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null saves_per_epoch: null sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3dc7b34c-0e6a-4c76-9da5-8ee774ed311c wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 3dc7b34c-0e6a-4c76-9da5-8ee774ed311c warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # af85d1b0-3edb-47c7-9737-4b4d41cae500 This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2798 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5141 \| 0.0945 \| 200 \| 1.2798 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
kimsin/fdfdfd	kimsin	2025-02-03T15:32:31Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-03T14:40:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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peulsilva/reasoning-qwen-epoch1	peulsilva	2025-02-03T15:27:17Z	6	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "grpo", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-03T15:25:34Z	--- library_name: transformers tags: - trl - grpo --- # 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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(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]
laquythang/b06dd050-40ba-42dc-bf12-7f11bfb5c8e9	laquythang	2025-02-03T15:27:10Z	10	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-7B-Instruct", "base_model:adapter:Qwen/Qwen2-7B-Instruct", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-02-03T15:05:43Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: b06dd050-40ba-42dc-bf12-7f11bfb5c8e9 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2-7B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - cb37487dbb01a482_train_data.json ds_type: json format: custom path: /workspace/input_data/cb37487dbb01a482_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: laquythang/b06dd050-40ba-42dc-bf12-7f11bfb5c8e9 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/cb37487dbb01a482_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3dc7b34c-0e6a-4c76-9da5-8ee774ed311c wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 3dc7b34c-0e6a-4c76-9da5-8ee774ed311c warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # b06dd050-40ba-42dc-bf12-7f11bfb5c8e9 This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3168 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1586 \| 0.0473 \| 200 \| 1.3168 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nghiatrannnnnn/5d2f6827-bf7f-451d-b472-c92b3a4275b3	nghiatrannnnnn	2025-02-03T15:27:06Z	10	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-7B-Instruct", "base_model:adapter:Qwen/Qwen2-7B-Instruct", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-02-03T15:05:32Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 5d2f6827-bf7f-451d-b472-c92b3a4275b3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2-7B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - cb37487dbb01a482_train_data.json ds_type: json format: custom path: /workspace/input_data/cb37487dbb01a482_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: nghiatrannnnnn/5d2f6827-bf7f-451d-b472-c92b3a4275b3 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/cb37487dbb01a482_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3dc7b34c-0e6a-4c76-9da5-8ee774ed311c wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 3dc7b34c-0e6a-4c76-9da5-8ee774ed311c warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 5d2f6827-bf7f-451d-b472-c92b3a4275b3 This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3170 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1586 \| 0.0473 \| 200 \| 1.3170 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lesso/c5121a90-c63d-4aee-8236-674a47ca6658	lesso	2025-02-03T15:22:29Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2025-02-03T15:18:12Z	--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - axolotl - generated_from_trainer model-index: - name: c5121a90-c63d-4aee-8236-674a47ca6658 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - eb57db6348d4b1da_train_data.json ds_type: json format: custom path: /workspace/input_data/eb57db6348d4b1da_train_data.json type: field_instruction: context field_output: question format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true group_by_length: true hub_model_id: lesso/c5121a90-c63d-4aee-8236-674a47ca6658 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001017 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: linear max_grad_norm: 1.0 max_steps: 200 micro_batch_size: 4 mlflow_experiment_name: /tmp/god17/eb57db6348d4b1da_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: e3ab71d4-7a9b-4efa-af05-d475e3deb9d8 wandb_project: ab-god17 wandb_run: your_name wandb_runid: e3ab71d4-7a9b-4efa-af05-d475e3deb9d8 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # c5121a90-c63d-4aee-8236-674a47ca6658 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9029 ## 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: 0.0001017 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.8691 \| 0.0011 \| 1 \| 1.8664 \| \| 1.0137 \| 0.0564 \| 50 \| 1.0247 \| \| 1.1429 \| 0.1128 \| 100 \| 0.9594 \| \| 0.908 \| 0.1692 \| 150 \| 0.9257 \| \| 0.9641 \| 0.2256 \| 200 \| 0.9029 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Esperanto/whisper-large-v3-kvc-fp16-onnx	Esperanto	2025-02-03T15:19:12Z	5	0	null	[ "onnx", "text-generation-inference", "whisper", "audio", "base_model:openai/whisper-large-v3", "base_model:quantized:openai/whisper-large-v3", "region:us" ]	null	2024-08-14T15:49:58Z	--- tags: - text-generation-inference - whisper - audio base_model: - openai/whisper-large-v3 --- # Whisper Large v3 with Key-Value-Cache enabled in ONNX fp16 format - Model creator: [Open AI](https://huggingface.co/openai) - Original model: [Whisper Large v3](https://huggingface.co/openai/whisper-large-v3) <!-- description start --> ## Description This repo contains the ONNX files for the ONNX conversion of Whisper Large v3 done by Esperanto Technologies. The model is in the fp16 format and has the KVC enabled. <!-- description end --> ## How to download ONNX model and weight files The easiest way to obtain the model is to clone this whole repo. Alternatively you can download the files is using the `huggingface-hub` Python library. ```shell pip3 install huggingface-hub>=0.17.1 ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download Esperanto/whisper-large-v3-kvc-fp16-onnx --local-dir whisper-large-v3-kvc-fp16-onnx --local-dir-use-symlinks False ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). ## How to run from Python code using ONNXRuntime This model can easily be ran in a CPU using [ONNXRuntime](https://onnxruntime.ai/). Here is a sample script to run this models: ```python #!/usr/bin/env python3 import whisper import onnx import sys import time import onnxruntime from typing import Sequence, Optional import numpy as np from pathlib import Path def run_whisper_decoder(decoder_model_path, execution_provider, session_options, decoder_output_names, cross_attn_tensors, num_new_tokens, provider_options = {}): start = time.time() decoder_session = onnxruntime.InferenceSession(decoder_model_path, sess_options=session_options, providers=[execution_provider], provider_options=[provider_options]) compile_time = time.time() transcription = decoder_loop(decoder_session, decoder_output_names, cross_attn_tensors, num_new_tokens) inference_time = time.time() return transcription def decoder_loop(decoder_session, decoder_output_names, cross_attn_tensors, num_new_tokens): # Generate start of transcription tokens tokenizer = whisper.tokenizer.get_tokenizer(multilingual=True) first_tokens = np.array([tokenizer.sot, 0, tokenizer.transcribe, tokenizer.no_timestamps], dtype=np.int64) # Self attention mask key, value vectors self_attn_past_k = [] self_attn_past_v = [] for i in range(32): self_attn_past_k.append(np.zeros((1, 20, 447, 64), dtype=np.float16)) self_attn_past_v.append(np.zeros((1, 20, 447, 64), dtype=np.float16)) # Cross attention cross_attn_k = cross_attn_tensors[0::2] cross_attn_v = cross_attn_tensors[1::2] # Attention mask attn_mask_size = 448 attn_mask = np.zeros((1,attn_mask_size), dtype=np.int64) # Process first tokens for j in range(len(first_tokens)): tokens = np.array([first_tokens[j]], dtype=np.int64).reshape(1, 1) attn_mask[0,-1 - j] = 1 decoder_input = {"input_ids": tokens, "attention_mask": attn_mask} for i in range(32): decoder_input[f"past_key_values.{str(i)}.key"] = self_attn_past_k[i] decoder_input[f"past_key_values.{str(i)}.value"] = self_attn_past_v[i] decoder_input[f"cross_attn.{str(i)}.key"] = cross_attn_k[i] decoder_input[f"cross_attn.{str(i)}.value"] = cross_attn_v[i] logits, cache_tensors = decoder_session.run(decoder_output_names, decoder_input) next_token = np.argmax(logits[0,0]) self_attn_k = cache_tensors[0::2] self_attn_v = cache_tensors[1::2] for i in range(32): self_attn_past_k[i] = self_attn_k[i][:,:,1:,:] self_attn_past_v[i] = self_attn_v[i][:,:,1:,:] if (j == 0): # set language token first_tokens[1] = next_token transcribed_tokens = [next_token] for j in range(4, 4 + num_new_tokens): tokens = np.array([transcribed_tokens[-1]], dtype=np.int64).reshape(1, 1) attn_mask[0,-1 - j] = 1 decoder_input = {"input_ids": tokens, "attention_mask": attn_mask} for i in range(32): decoder_input[f"past_key_values.{str(i)}.key"] = self_attn_past_k[i] decoder_input[f"past_key_values.{str(i)}.value"] = self_attn_past_v[i] decoder_input[f"cross_attn.{str(i)}.key"] = cross_attn_k[i] decoder_input[f"cross_attn.{str(i)}.value"] = cross_attn_v[i] logits, cache_tensors = decoder_session.run(decoder_output_names, decoder_input) next_token = np.argmax(logits[0,0]) # print(j, next_token) if next_token == tokenizer.eot: # end_of_transcription break transcribed_tokens.append(next_token) self_attn_k = cache_tensors[0::2] self_attn_v = cache_tensors[1::2] for i in range(32): self_attn_past_k[i] = self_attn_k[i][:,:,1:,:] self_attn_past_v[i] = self_attn_v[i][:,:,1:,:] return tokenizer.decode(transcribed_tokens) def main(argv: Optional[Sequence[str]] = None): num_seconds = 28.8 speech_path = 'sample_audio.wav' encoder_model_path = 'whisper-large-v3-kvc-fp16-onnx/encoder/model.onnx' decoder_model_path = 'whisper-large-v3-kvc-fp16-onnx/decoder/model.onnx' # Load audio print(f"Spectrogram speech audio file {speech_path}... ", end="") audio = whisper.load_audio(speech_path) audio = whisper.pad_or_trim(audio, length=int(num_seconds*16000)) mel = whisper.log_mel_spectrogram(audio, n_mels=128).unsqueeze(0) # Unsqueeze to set batch=1 print("OK") print("Running encoder... ", end="") # Session options session_options = onnxruntime.SessionOptions() # Disable all the graph optimizations session_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL # Encode encoder = onnx.load(encoder_model_path, load_external_data=False) encoder_input = {"mel": mel.numpy().astype('float16')} encoder_output_names = [tensor.name for tensor in encoder.graph.output] # CPU encoding cpu_provider = 'CPUExecutionProvider' enc_session_cpu = onnxruntime.InferenceSession(encoder_model_path, sess_options=session_options, providers=[cpu_provider]) cross_attn_tensors_cpu = enc_session_cpu.run(encoder_output_names, encoder_input) print("OK") # DECODE API PARAMS max_context = 448 new_tokens = 20 # Run decoder model CPU decoder = onnx.load(decoder_model_path, load_external_data=False) decoder_output_names = [tensor.name for tensor in decoder.graph.output] run_whisper_decoder(decoder_model_path, cpu_provider, session_options, decoder_output_names, cross_attn_tensors_cpu, new_tokens) if __name__ == "__main__": sys.exit(main(sys.argv[1:])) ```
BitStreamX/DeepSeek-R1-Distill-Llama-8B-Q5_K_M-GGUF	BitStreamX	2025-02-03T15:18:11Z	8	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-03T15:17:43Z	--- license: mit library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B --- # BitStreamX/DeepSeek-R1-Distill-Llama-8B-Q5_K_M-GGUF This model was converted to GGUF format from [`deepseek-ai/DeepSeek-R1-Distill-Llama-8B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo BitStreamX/DeepSeek-R1-Distill-Llama-8B-Q5_K_M-GGUF --hf-file deepseek-r1-distill-llama-8b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo BitStreamX/DeepSeek-R1-Distill-Llama-8B-Q5_K_M-GGUF --hf-file deepseek-r1-distill-llama-8b-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo BitStreamX/DeepSeek-R1-Distill-Llama-8B-Q5_K_M-GGUF --hf-file deepseek-r1-distill-llama-8b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo BitStreamX/DeepSeek-R1-Distill-Llama-8B-Q5_K_M-GGUF --hf-file deepseek-r1-distill-llama-8b-q5_k_m.gguf -c 2048 ```
mrferr3t/b09b9c57-bfc8-4f04-bcf9-1b9d0c88ed36	mrferr3t	2025-02-03T15:17:40Z	6	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:adapter:EleutherAI/pythia-70m-deduped", "license:apache-2.0", "region:us" ]	null	2025-02-03T15:10:35Z	--- library_name: peft license: apache-2.0 base_model: EleutherAI/pythia-70m-deduped tags: - axolotl - generated_from_trainer model-index: - name: b09b9c57-bfc8-4f04-bcf9-1b9d0c88ed36 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora auto_find_batch_size: true base_model: EleutherAI/pythia-70m-deduped bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - 0887480b4ca433bf_train_data.json ds_type: json format: custom path: /workspace/input_data/0887480b4ca433bf_train_data.json type: field_input: '' field_instruction: title field_output: sum format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 3 early_stopping_threshold: 0.001 eval_max_new_tokens: 128 eval_steps: 20 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: false hub_model_id: mrferr3t/b09b9c57-bfc8-4f04-bcf9-1b9d0c88ed36 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0003 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 100 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine micro_batch_size: 32 mlflow_experiment_name: /tmp/0887480b4ca433bf_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true s2_attention: null sample_packing: false save_steps: 20 saves_per_epoch: 0 sequence_len: 512 special_tokens: pad_token: <\|endoftext\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 83defa9c-70d4-4a69-b9b8-05277b37d267 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 83defa9c-70d4-4a69-b9b8-05277b37d267 warmup_ratio: 0.05 weight_decay: 0.0 xformers_attention: null ``` </details><br> # b09b9c57-bfc8-4f04-bcf9-1b9d0c88ed36 This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.5859 ## 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: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1532 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0003 \| 1 \| 26.3845 \| \| No log \| 0.0065 \| 20 \| 26.3821 \| \| No log \| 0.0131 \| 40 \| 26.3735 \| \| No log \| 0.0196 \| 60 \| 26.3391 \| \| No log \| 0.0261 \| 80 \| 26.2733 \| \| 52.7645 \| 0.0326 \| 100 \| 26.1914 \| \| 52.7645 \| 0.0392 \| 120 \| 26.0699 \| \| 52.7645 \| 0.0457 \| 140 \| 25.7022 \| \| 52.7645 \| 0.0522 \| 160 \| 20.4640 \| \| 52.7645 \| 0.0587 \| 180 \| 15.0437 \| \| 42.7111 \| 0.0653 \| 200 \| 11.4296 \| \| 42.7111 \| 0.0718 \| 220 \| 9.8143 \| \| 42.7111 \| 0.0783 \| 240 \| 9.0114 \| \| 42.7111 \| 0.0848 \| 260 \| 8.3765 \| \| 42.7111 \| 0.0914 \| 280 \| 8.0982 \| \| 18.0576 \| 0.0979 \| 300 \| 7.9935 \| \| 18.0576 \| 0.1044 \| 320 \| 7.9346 \| \| 18.0576 \| 0.1109 \| 340 \| 7.9940 \| \| 18.0576 \| 0.1175 \| 360 \| 7.9014 \| \| 18.0576 \| 0.1240 \| 380 \| 7.7273 \| \| 15.9114 \| 0.1305 \| 400 \| 7.6477 \| \| 15.9114 \| 0.1371 \| 420 \| 7.5922 \| \| 15.9114 \| 0.1436 \| 440 \| 7.5891 \| \| 15.9114 \| 0.1501 \| 460 \| 7.6227 \| \| 15.9114 \| 0.1566 \| 480 \| 7.4846 \| \| 15.2478 \| 0.1632 \| 500 \| 7.4492 \| \| 15.2478 \| 0.1697 \| 520 \| 7.4490 \| \| 15.2478 \| 0.1762 \| 540 \| 7.4361 \| \| 15.2478 \| 0.1827 \| 560 \| 7.4882 \| \| 15.2478 \| 0.1893 \| 580 \| 7.4828 \| \| 15.1078 \| 0.1958 \| 600 \| 7.5859 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
Shenziqian666/deepseek-r1-dg_backup1-F16-GGUF	Shenziqian666	2025-02-03T15:17:05Z	48	0	peft	[ "peft", "gguf", "llama-cpp", "gguf-my-lora", "base_model:Shenziqian666/deepseek-r1-dg_backup1", "base_model:adapter:Shenziqian666/deepseek-r1-dg_backup1", "region:us" ]	null	2025-02-03T15:17:02Z	--- base_model: Shenziqian666/deepseek-r1-dg_backup1 library_name: peft tags: - llama-cpp - gguf-my-lora --- # Shenziqian666/deepseek-r1-dg_backup1-F16-GGUF This LoRA adapter was converted to GGUF format from [`Shenziqian666/deepseek-r1-dg_backup1`](https://huggingface.co/Shenziqian666/deepseek-r1-dg_backup1) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space. Refer to the [original adapter repository](https://huggingface.co/Shenziqian666/deepseek-r1-dg_backup1) for more details. ## Use with llama.cpp ```bash # with cli llama-cli -m base_model.gguf --lora deepseek-r1-dg_backup1-f16.gguf (...other args) # with server llama-server -m base_model.gguf --lora deepseek-r1-dg_backup1-f16.gguf (...other args) ``` To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).
lesso/ef3cd361-7fa3-4b90-bb8f-e4be20693249	lesso	2025-02-03T15:16:51Z	6	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:adapter:EleutherAI/pythia-70m-deduped", "region:us" ]	null	2025-02-03T15:09:26Z	--- library_name: peft base_model: EleutherAI/pythia-70m-deduped tags: - axolotl - generated_from_trainer model-index: - name: ef3cd361-7fa3-4b90-bb8f-e4be20693249 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: EleutherAI/pythia-70m-deduped bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 0887480b4ca433bf_train_data.json ds_type: json format: custom path: /workspace/input_data/0887480b4ca433bf_train_data.json type: field_input: '' field_instruction: title field_output: sum format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true group_by_length: true hub_model_id: lesso/ef3cd361-7fa3-4b90-bb8f-e4be20693249 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001017 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: linear max_grad_norm: 1.0 max_steps: 200 micro_batch_size: 4 mlflow_experiment_name: /tmp/god17/0887480b4ca433bf_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1024 special_tokens: pad_token: <\|endoftext\|> strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 83defa9c-70d4-4a69-b9b8-05277b37d267 wandb_project: ab-god17 wandb_run: your_name wandb_runid: 83defa9c-70d4-4a69-b9b8-05277b37d267 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # ef3cd361-7fa3-4b90-bb8f-e4be20693249 This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.4578 ## 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: 0.0001017 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 25.8054 \| 0.0000 \| 1 \| 31.1890 \| \| 30.3842 \| 0.0020 \| 50 \| 10.6845 \| \| 48.0773 \| 0.0041 \| 100 \| 9.7321 \| \| 30.076 \| 0.0061 \| 150 \| 8.7948 \| \| 37.3325 \| 0.0082 \| 200 \| 8.4578 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
robiulawaldev/2353fa98-22e8-401d-a3ba-d061a68ea913	robiulawaldev	2025-02-03T15:15:19Z	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-7B-Instruct", "base_model:adapter:Qwen/Qwen2-7B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-02-03T15:09:33Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 2353fa98-22e8-401d-a3ba-d061a68ea913 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2-7B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - cb37487dbb01a482_train_data.json ds_type: json format: custom path: /workspace/input_data/cb37487dbb01a482_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: false hub_model_id: robiulawaldev/2353fa98-22e8-401d-a3ba-d061a68ea913 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: constant max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/cb37487dbb01a482_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3dc7b34c-0e6a-4c76-9da5-8ee774ed311c wandb_project: Birthday-SN56-35-Gradients-On-Demand wandb_run: your_name wandb_runid: 3dc7b34c-0e6a-4c76-9da5-8ee774ed311c warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 2353fa98-22e8-401d-a3ba-d061a68ea913 This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3463 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| 3.0620 \| \| 1.3058 \| 0.0059 \| 50 \| 1.4055 \| \| 1.3329 \| 0.0118 \| 100 \| 1.3536 \| \| 1.3904 \| 0.0177 \| 150 \| 1.3366 \| \| 1.3959 \| 0.0236 \| 200 \| 1.3463 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
xwen-team/Xwen-72B-Chat-i1-GGUF	xwen-team	2025-02-03T15:15:00Z	195	3	transformers	[ "transformers", "gguf", "en", "zh", "base_model:xwen-team/Xwen-72B-Chat", "base_model:quantized:xwen-team/Xwen-72B-Chat", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-02-03T15:13:32Z	--- base_model: xwen-team/Xwen-72B-Chat language: - en - zh library_name: transformers license: apache-2.0 quantized_by: mradermacher --- > [!Important] > Big thanks to [@mradermacher](https://huggingface.co/mradermacher) for helping us build this repository of GGUFs for our [Xwen-72B-Chat](https://huggingface.co/xwen-team/Xwen-72B-Chat)! ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/xwen-team/Xwen-72B-Chat <!-- provided-files --> static quants are available at https://huggingface.co/xwen-team/Xwen-72B-Chat-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-IQ1_S.gguf) \| i1-IQ1_S \| 22.8 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-IQ1_M.gguf) \| i1-IQ1_M \| 23.8 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 27.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-IQ2_S.gguf) \| i1-IQ2_S \| 28.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-IQ2_M.gguf) \| i1-IQ2_M \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 29.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-Q2_K.gguf) \| i1-Q2_K \| 29.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 31.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 32.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-IQ3_S.gguf) \| i1-IQ3_S \| 34.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 34.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-IQ3_M.gguf) \| i1-IQ3_M \| 35.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 37.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 39.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 39.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-Q4_0.gguf) \| i1-Q4_0 \| 41.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 44.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-Q4_1.gguf) \| i1-Q4_1 \| 45.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 47.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 51.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Xwen-72B-Chat-i1-GGUF/resolve/main/Xwen-72B-Chat.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 64.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks Big thanks to [@mradermacher](https://huggingface.co/mradermacher) for helping us build this repository of GGUFs for our [Xwen-72B-Chat](https://huggingface.co/xwen-team/Xwen-72B-Chat)! <!-- end -->
brixeus/adfef053-f602-40ff-a6a2-db565e300edf	brixeus	2025-02-03T15:13:11Z	7	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:adapter:EleutherAI/pythia-70m-deduped", "license:apache-2.0", "region:us" ]	null	2025-02-03T15:07:21Z	--- library_name: peft license: apache-2.0 base_model: EleutherAI/pythia-70m-deduped tags: - axolotl - generated_from_trainer model-index: - name: adfef053-f602-40ff-a6a2-db565e300edf results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: EleutherAI/pythia-70m-deduped bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 0887480b4ca433bf_train_data.json ds_type: json format: custom path: /workspace/input_data/0887480b4ca433bf_train_data.json type: field_input: '' field_instruction: title field_output: sum format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: brixeus/adfef053-f602-40ff-a6a2-db565e300edf hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: 0 logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_steps: 100 micro_batch_size: 8 mlflow_experiment_name: /tmp/0887480b4ca433bf_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 1024 special_tokens: pad_token: <\|endoftext\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: 83defa9c-70d4-4a69-b9b8-05277b37d267 wandb_project: Gradients-On-Three wandb_run: your_name wandb_runid: 83defa9c-70d4-4a69-b9b8-05277b37d267 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # adfef053-f602-40ff-a6a2-db565e300edf This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on the None dataset. It achieves the following results on the evaluation set: - Loss: 11.4784 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0002 \| 1 \| 26.8630 \| \| 107.2866 \| 0.0015 \| 9 \| 26.7855 \| \| 109.0102 \| 0.0029 \| 18 \| 26.5956 \| \| 99.2476 \| 0.0044 \| 27 \| 26.1975 \| \| 93.0308 \| 0.0059 \| 36 \| 22.7234 \| \| 62.9235 \| 0.0073 \| 45 \| 16.8609 \| \| 60.8141 \| 0.0088 \| 54 \| 14.4365 \| \| 48.299 \| 0.0103 \| 63 \| 12.1002 \| \| 48.5988 \| 0.0117 \| 72 \| 11.7900 \| \| 46.4838 \| 0.0132 \| 81 \| 11.5957 \| \| 48.2905 \| 0.0147 \| 90 \| 11.4975 \| \| 45.2941 \| 0.0162 \| 99 \| 11.4784 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nat-hunt/52b3e9c1-5dea-41e1-923d-a95a0d53aeb0	nat-hunt	2025-02-03T15:12:52Z	7	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-7B-Instruct", "base_model:adapter:Qwen/Qwen2-7B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-02-03T15:06:17Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 52b3e9c1-5dea-41e1-923d-a95a0d53aeb0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2-7B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - cb37487dbb01a482_train_data.json ds_type: json format: custom path: /workspace/input_data/cb37487dbb01a482_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: nat-hunt/52b3e9c1-5dea-41e1-923d-a95a0d53aeb0 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/cb37487dbb01a482_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3dc7b34c-0e6a-4c76-9da5-8ee774ed311c wandb_project: Birthday-SN56-25-Gradients-On-Demand wandb_run: your_name wandb_runid: 3dc7b34c-0e6a-4c76-9da5-8ee774ed311c warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 52b3e9c1-5dea-41e1-923d-a95a0d53aeb0 This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2751 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0002 \| 1 \| 3.1501 \| \| 1.325 \| 0.0118 \| 50 \| 1.3437 \| \| 1.3561 \| 0.0236 \| 100 \| 1.3050 \| \| 1.2818 \| 0.0354 \| 150 \| 1.2822 \| \| 1.2591 \| 0.0473 \| 200 \| 1.2751 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Steven0090/Llama3.2-Instruct-1B-gguf	Steven0090	2025-02-03T15:12:17Z	9	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-03T15:02:15Z	--- license: apache-2.0 ---
daniel40/a286322f-d6e4-457f-b331-57eefcb77035	daniel40	2025-02-03T15:11:53Z	6	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:adapter:EleutherAI/pythia-70m-deduped", "license:apache-2.0", "region:us" ]	null	2025-02-03T15:08:14Z	--- library_name: peft license: apache-2.0 base_model: EleutherAI/pythia-70m-deduped tags: - axolotl - generated_from_trainer model-index: - name: a286322f-d6e4-457f-b331-57eefcb77035 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: EleutherAI/pythia-70m-deduped bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 0887480b4ca433bf_train_data.json ds_type: json format: custom path: /workspace/input_data/0887480b4ca433bf_train_data.json type: field_input: '' field_instruction: title field_output: sum format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: daniel40/a286322f-d6e4-457f-b331-57eefcb77035 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: constant max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/0887480b4ca433bf_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 special_tokens: pad_token: <\|endoftext\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 83defa9c-70d4-4a69-b9b8-05277b37d267 wandb_project: Birthday-SN56-27-Gradients-On-Demand wandb_run: your_name wandb_runid: 83defa9c-70d4-4a69-b9b8-05277b37d267 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # a286322f-d6e4-457f-b331-57eefcb77035 This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.2686 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0000 \| 1 \| 28.8329 \| \| 51.2367 \| 0.0020 \| 50 \| 12.4534 \| \| 38.9011 \| 0.0041 \| 100 \| 9.2037 \| \| 33.0198 \| 0.0061 \| 150 \| 8.0701 \| \| 37.1755 \| 0.0082 \| 200 \| 8.2686 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
kostiantynk-out/8d7697e7-6289-4c81-b9f1-3aa85e6290ca	kostiantynk-out	2025-02-03T15:11:49Z	7	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-7B-Instruct", "base_model:adapter:Qwen/Qwen2-7B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-02-03T15:06:40Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 8d7697e7-6289-4c81-b9f1-3aa85e6290ca results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2-7B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - cb37487dbb01a482_train_data.json ds_type: json format: custom path: /workspace/input_data/cb37487dbb01a482_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: false hub_model_id: kostiantynk-out/8d7697e7-6289-4c81-b9f1-3aa85e6290ca hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 250 micro_batch_size: 2 mlflow_experiment_name: /tmp/cb37487dbb01a482_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3dc7b34c-0e6a-4c76-9da5-8ee774ed311c wandb_project: Mine-SN56-1-Gradients-On-Demand wandb_run: your_name wandb_runid: 3dc7b34c-0e6a-4c76-9da5-8ee774ed311c warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 8d7697e7-6289-4c81-b9f1-3aa85e6290ca This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2959 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 250 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| 3.1501 \| \| 1.2555 \| 0.0074 \| 63 \| 1.3861 \| \| 1.2871 \| 0.0149 \| 126 \| 1.3229 \| \| 1.4075 \| 0.0223 \| 189 \| 1.2959 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
xwen-team/Xwen-72B-Chat-GGUF	xwen-team	2025-02-03T15:11:11Z	59	1	transformers	[ "transformers", "gguf", "en", "zh", "base_model:xwen-team/Xwen-72B-Chat", "base_model:quantized:xwen-team/Xwen-72B-Chat", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-03T15:08:18Z	--- base_model: xwen-team/Xwen-72B-Chat language: - en - zh library_name: transformers license: apache-2.0 quantized_by: mradermacher --- > [!Important] > Big thanks to [@mradermacher](https://huggingface.co/mradermacher) for helping us build this repository of GGUFs for our [Xwen-72B-Chat](https://huggingface.co/xwen-team/Xwen-72B-Chat)! ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/xwen-team/Xwen-72B-Chat <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/xwen-team/Xwen-72B-Chat-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/Xwen-72B-Chat-GGUF/resolve/main/Xwen-72B-Chat.Q2_K.gguf) \| Q2_K \| 29.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-GGUF/resolve/main/Xwen-72B-Chat.Q3_K_S.gguf) \| Q3_K_S \| 34.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-GGUF/resolve/main/Xwen-72B-Chat.Q3_K_M.gguf) \| Q3_K_M \| 37.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-GGUF/resolve/main/Xwen-72B-Chat.Q3_K_L.gguf) \| Q3_K_L \| 39.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-GGUF/resolve/main/Xwen-72B-Chat.IQ4_XS.gguf) \| IQ4_XS \| 40.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-GGUF/resolve/main/Xwen-72B-Chat.Q4_K_S.gguf) \| Q4_K_S \| 44.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-72B-Chat-GGUF/resolve/main/Xwen-72B-Chat.Q4_K_M.gguf) \| Q4_K_M \| 47.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Xwen-72B-Chat-GGUF/resolve/main/Xwen-72B-Chat.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Xwen-72B-Chat-GGUF/resolve/main/Xwen-72B-Chat.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 51.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Xwen-72B-Chat-GGUF/resolve/main/Xwen-72B-Chat.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Xwen-72B-Chat-GGUF/resolve/main/Xwen-72B-Chat.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Xwen-72B-Chat-GGUF/resolve/main/Xwen-72B-Chat.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Xwen-72B-Chat-GGUF/resolve/main/Xwen-72B-Chat.Q6_K.gguf.part2of2) \| Q6_K \| 64.4 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Xwen-72B-Chat-GGUF/resolve/main/Xwen-72B-Chat.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Xwen-72B-Chat-GGUF/resolve/main/Xwen-72B-Chat.Q8_0.gguf.part2of2) \| Q8_0 \| 77.4 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks Big thanks to [@mradermacher](https://huggingface.co/mradermacher) for helping us build this repository of GGUFs for our [Xwen-72B-Chat](https://huggingface.co/xwen-team/Xwen-72B-Chat)! <!-- end -->
lesso/824cf2a9-7c0b-47ea-8955-5e52aabf67bb	lesso	2025-02-03T15:08:47Z	6	0	peft	[ "peft", "safetensors", "gemma2", "axolotl", "generated_from_trainer", "base_model:unsloth/gemma-2-2b-it", "base_model:adapter:unsloth/gemma-2-2b-it", "license:gemma", "region:us" ]	null	2025-02-03T15:01:21Z	--- library_name: peft license: gemma base_model: unsloth/gemma-2-2b-it tags: - axolotl - generated_from_trainer model-index: - name: 824cf2a9-7c0b-47ea-8955-5e52aabf67bb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/gemma-2-2b-it bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 7465fecdd1b4fae8_train_data.json ds_type: json format: custom path: /workspace/input_data/7465fecdd1b4fae8_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true group_by_length: true hub_model_id: lesso/824cf2a9-7c0b-47ea-8955-5e52aabf67bb hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001017 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: linear max_grad_norm: 1.0 max_steps: 200 micro_batch_size: 4 mlflow_experiment_name: /tmp/god17/7465fecdd1b4fae8_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 96ba0598-e365-4fe4-a421-689fa74a779f wandb_project: ab-god17 wandb_run: your_name wandb_runid: 96ba0598-e365-4fe4-a421-689fa74a779f warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 824cf2a9-7c0b-47ea-8955-5e52aabf67bb This model is a fine-tuned version of [unsloth/gemma-2-2b-it](https://huggingface.co/unsloth/gemma-2-2b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0689 ## 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: 0.0001017 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.7989 \| 0.0017 \| 1 \| 3.9072 \| \| 0.007 \| 0.0875 \| 50 \| 0.6093 \| \| 0.0032 \| 0.1750 \| 100 \| 0.2054 \| \| 0.2932 \| 0.2625 \| 150 \| 0.0877 \| \| 0.0106 \| 0.3500 \| 200 \| 0.0689 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
rak-r05/e1e4fce9-6001-4770-80f2-c297c1e6474e	rak-r05	2025-02-03T15:07:41Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/llama-3-8b", "base_model:adapter:unsloth/llama-3-8b", "license:llama3", "8-bit", "bitsandbytes", "region:us" ]	null	2025-02-03T14:30:40Z	--- library_name: peft license: llama3 base_model: unsloth/llama-3-8b tags: - axolotl - generated_from_trainer model-index: - name: e1e4fce9-6001-4770-80f2-c297c1e6474e results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/llama-3-8b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 7acaec7c36203875_train_data.json ds_type: json format: custom path: /workspace/input_data/7acaec7c36203875_train_data.json type: field_input: title field_instruction: category field_output: abstract format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: rak-r05/e1e4fce9-6001-4770-80f2-c297c1e6474e hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0004 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 2 mlflow_experiment_name: /tmp/7acaec7c36203875_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 085f0a92-52f7-4082-a67a-133e6af32b64 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 085f0a92-52f7-4082-a67a-133e6af32b64 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # e1e4fce9-6001-4770-80f2-c297c1e6474e This model is a fine-tuned version of [unsloth/llama-3-8b](https://huggingface.co/unsloth/llama-3-8b) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0001 \| 1 \| nan \| \| 0.0 \| 0.0047 \| 38 \| nan \| \| 0.0 \| 0.0094 \| 76 \| nan \| \| 0.0 \| 0.0141 \| 114 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
earnxus/d7fb7174-cc88-4c8a-97bf-6ca814340a72	earnxus	2025-02-03T15:06:41Z	7	0	peft	[ "peft", "safetensors", "opt", "axolotl", "generated_from_trainer", "base_model:facebook/opt-1.3b", "base_model:adapter:facebook/opt-1.3b", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-02-03T14:54:50Z	--- library_name: peft license: other base_model: facebook/opt-1.3b tags: - axolotl - generated_from_trainer model-index: - name: d7fb7174-cc88-4c8a-97bf-6ca814340a72 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: facebook/opt-1.3b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 20fc9edc61053699_train_data.json ds_type: json format: custom path: /workspace/input_data/20fc9edc61053699_train_data.json type: field_input: answer field_instruction: problem field_output: solution format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: null eval_batch_size: 2 eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: true hub_model_id: earnxus/d7fb7174-cc88-4c8a-97bf-6ca814340a72 hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0001 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/20fc9edc61053699_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null saves_per_epoch: null sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: efadbf9b-21a1-4759-b077-7318afa3023b wandb_project: Gradients-On-Nine wandb_run: your_name wandb_runid: efadbf9b-21a1-4759-b077-7318afa3023b warmup_steps: 5 weight_decay: 0.01 xformers_attention: null ``` </details><br> # d7fb7174-cc88-4c8a-97bf-6ca814340a72 This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7053 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 8.0987 \| 0.3984 \| 200 \| 1.7053 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
emre/Qwen-0.5B-GRPO	emre	2025-02-03T15:06:30Z	40	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "grpo", "qwen", "gsm8k", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-03T12:33:01Z	--- library_name: transformers tags: - trl - grpo - qwen - gsm8k --- # Qwen-0.5B-GRPO: A Fine-Tuned Math Reasoner This model is a fine-tuned version of the Qwen 0.5B model (based on [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)) using GRPO (Generative Reward Policy Optimization). It has been trained on the GSM8K math dataset to improve its ability to generate step-by-step reasoning for math problems, following a structured output format with explicit `<reasoning>` and `<answer>` sections. ## Model Details ### Model Description Qwen-0.5B-GRPO is designed to serve as a lightweight math reasoning assistant. By fine-tuning with reinforcement learning using GRPO, the model learns to produce responses that include both intermediate reasoning and final answers. Key adaptations include: - Base Model: Qwen/Qwen2.5-0.5B-Instruct - Fine-Tuning Method: GRPO (reinforcement learning with custom reward functions) - Dataset: GSM8K – a collection of challenging grade-school math problems - Generation Engine: Utilizes vLLM for faster inference on a single GPU setup - Precision: BF16 training for efficiency on Colab GPUs - Developed by: Davut Emre Taşar - License: Please refer to the license of the base model on its Hugging Face Hub page ### Model Sources - Repository (this model): [https://huggingface.co/emre/Qwen-0.5B-GRPO](https://huggingface.co/emre/Qwen-0.5B-GRPO) - Base Model Repository: [https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) - Dataset: [https://huggingface.co/datasets/openai/gsm8k](https://huggingface.co/datasets/openai/gsm8k) ## Uses ### Intended Use This model is intended for educational and research purposes, particularly to demonstrate and support math problem solving with clear, step-by-step reasoning. It is well-suited for: - Generating structured explanations for math problems. - Serving as a lightweight assistant in educational applications focused on math reasoning. ### Out-of-Scope Use - High-Stakes Decision Making: This model is not designed for critical decision making. - Non-Math Domains: Its performance is tailored to math problems; performance on other domains may be limited. - Over-Reliance on Automated Reasoning: The reward functions used during fine-tuning (e.g., exact string matching) may not capture all nuances, so human oversight is recommended. ## Bias, Risks, and Limitations - Model Size: With only 0.5B parameters, it may not perform as robustly as larger models. - Training Duration: Fine-tuning was performed for a single epoch; further training might be needed for more challenging tasks. - Reward Function Limitations: The custom reward functions (checking for correct formatting and numerical correctness) are heuristic and may occasionally miss subtleties in reasoning. - Generalization: The structured format (with `<reasoning>` and `<answer>` tags) is enforced during training and may require adaptation for other use cases. ### Recommendations Users should: - Validate model outputs on a case-by-case basis. - Consider further fine-tuning for domain-specific applications. - Use the model as a supplementary tool rather than the sole resource for critical math reasoning tasks. ## How to Get Started with the Model Below is an example code snippet to load and use the model: ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_name = "emre/Qwen-0.5B-GRPO" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to("cuda") # Example prompt: structured with <reasoning> and <answer> tags. prompt = """<reasoning> Step-by-step reasoning: </reasoning> <answer> """ inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_length=300) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Shenziqian666/deepseek-r1-dg_backup1	Shenziqian666	2025-02-03T15:06:26Z	8	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:deepseek-ai/DeepSeek-R1", "base_model:adapter:deepseek-ai/DeepSeek-R1", "region:us" ]	null	2025-02-03T14:41:36Z	--- base_model: deepseek-ai/DeepSeek-R1 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 0.12.0
xwen-team/Xwen-7B-Chat-i1-GGUF	xwen-team	2025-02-03T15:04:39Z	643	3	transformers	[ "transformers", "gguf", "en", "zh", "base_model:xwen-team/Xwen-7B-Chat", "base_model:quantized:xwen-team/Xwen-7B-Chat", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-02-03T14:48:26Z	--- base_model: xwen-team/Xwen-7B-Chat language: - en - zh library_name: transformers license: apache-2.0 quantized_by: mradermacher --- > [!Important] > Big thanks to [@mradermacher](https://huggingface.co/mradermacher) for helping us build this repository of GGUFs for our [Xwen-7B-Chat](https://huggingface.co/xwen-team/Xwen-7B-Chat)! ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/xwen-team/Xwen-7B-Chat <!-- provided-files --> static quants are available at https://huggingface.co/xwen-team/Xwen-7B-Chat-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.9 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Xwen-7B-Chat-i1-GGUF/resolve/main/Xwen-7B-Chat.i1-Q6_K.gguf) \| i1-Q6_K \| 6.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks Big thanks to [@mradermacher](https://huggingface.co/mradermacher) for helping us build this repository of GGUFs for our [Xwen-7B-Chat](https://huggingface.co/xwen-team/Xwen-7B-Chat)! <!-- end -->
ardaspear/c4ff905c-4354-45aa-a814-350b8c8cfeb2	ardaspear	2025-02-03T14:59:30Z	9	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2025-02-03T14:54:22Z	--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - axolotl - generated_from_trainer model-index: - name: c4ff905c-4354-45aa-a814-350b8c8cfeb2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - eb57db6348d4b1da_train_data.json ds_type: json format: custom path: /workspace/input_data/eb57db6348d4b1da_train_data.json type: field_instruction: context field_output: question format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: ardaspear/c4ff905c-4354-45aa-a814-350b8c8cfeb2 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: 0 logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_steps: 100 micro_batch_size: 8 mlflow_experiment_name: /tmp/eb57db6348d4b1da_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: e3ab71d4-7a9b-4efa-af05-d475e3deb9d8 wandb_project: Gradients-On-Five wandb_run: your_name wandb_runid: e3ab71d4-7a9b-4efa-af05-d475e3deb9d8 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # c4ff905c-4354-45aa-a814-350b8c8cfeb2 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9504 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0045 \| 1 \| 1.8489 \| \| 1.6539 \| 0.0406 \| 9 \| 1.5172 \| \| 1.1136 \| 0.0812 \| 18 \| 1.1379 \| \| 1.0675 \| 0.1218 \| 27 \| 1.0481 \| \| 0.8939 \| 0.1623 \| 36 \| 1.0173 \| \| 0.9378 \| 0.2029 \| 45 \| 0.9899 \| \| 1.012 \| 0.2435 \| 54 \| 0.9733 \| \| 0.8966 \| 0.2841 \| 63 \| 0.9615 \| \| 0.9167 \| 0.3247 \| 72 \| 0.9572 \| \| 0.8716 \| 0.3653 \| 81 \| 0.9545 \| \| 0.855 \| 0.4059 \| 90 \| 0.9515 \| \| 0.8846 \| 0.4464 \| 99 \| 0.9504 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mpkmkk/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2-Q4_K_M-GGUF	mpkmkk	2025-02-03T14:58:51Z	3,133	1	transformers	[ "transformers", "gguf", "abliterated", "uncensored", "llama-cpp", "gguf-my-repo", "base_model:huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2", "base_model:quantized:huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-03T14:58:12Z	--- base_model: huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 library_name: transformers tags: - abliterated - uncensored - llama-cpp - gguf-my-repo --- # mpkmkk/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2-Q4_K_M-GGUF This model was converted to GGUF format from [`huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2`](https://huggingface.co/huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo mpkmkk/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-14b-abliterated-v2-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo mpkmkk/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-14b-abliterated-v2-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo mpkmkk/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-14b-abliterated-v2-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo mpkmkk/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-14b-abliterated-v2-q4_k_m.gguf -c 2048 ```
kostiantynk/e4554b97-85d6-49eb-b786-e7fb547ea242	kostiantynk	2025-02-03T14:56:43Z	8	0	peft	[ "peft", "safetensors", "opt", "axolotl", "generated_from_trainer", "base_model:facebook/opt-1.3b", "base_model:adapter:facebook/opt-1.3b", "license:other", "region:us" ]	null	2025-02-03T14:55:27Z	--- library_name: peft license: other base_model: facebook/opt-1.3b tags: - axolotl - generated_from_trainer model-index: - name: e4554b97-85d6-49eb-b786-e7fb547ea242 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: facebook/opt-1.3b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 20fc9edc61053699_train_data.json ds_type: json format: custom path: /workspace/input_data/20fc9edc61053699_train_data.json type: field_input: answer field_instruction: problem field_output: solution format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: false hub_model_id: kostiantynk/e4554b97-85d6-49eb-b786-e7fb547ea242 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: constant max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/20fc9edc61053699_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: efadbf9b-21a1-4759-b077-7318afa3023b wandb_project: Mine-SN56-22-Gradients-On-Demand wandb_run: your_name wandb_runid: efadbf9b-21a1-4759-b077-7318afa3023b warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # e4554b97-85d6-49eb-b786-e7fb547ea242 This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5877 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0010 \| 1 \| 1.7956 \| \| 3.1387 \| 0.0498 \| 50 \| 1.6722 \| \| 3.2776 \| 0.0996 \| 100 \| 1.6338 \| \| 3.2946 \| 0.1494 \| 150 \| 1.6095 \| \| 3.1317 \| 0.1992 \| 200 \| 1.5877 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
havinash-ai/82d08f21-4bb1-4308-9065-148b065a4aff	havinash-ai	2025-02-03T14:56:41Z	8	0	peft	[ "peft", "safetensors", "opt", "axolotl", "generated_from_trainer", "base_model:facebook/opt-1.3b", "base_model:adapter:facebook/opt-1.3b", "license:other", "region:us" ]	null	2025-02-03T14:54:55Z	--- library_name: peft license: other base_model: facebook/opt-1.3b tags: - axolotl - generated_from_trainer model-index: - name: 82d08f21-4bb1-4308-9065-148b065a4aff results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: facebook/opt-1.3b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 20fc9edc61053699_train_data.json ds_type: json format: custom path: /workspace/input_data/20fc9edc61053699_train_data.json type: field_input: answer field_instruction: problem field_output: solution format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: havinash-ai/82d08f21-4bb1-4308-9065-148b065a4aff hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/20fc9edc61053699_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: efadbf9b-21a1-4759-b077-7318afa3023b wandb_project: Birthday-SN56-9-Gradients-On-Demand wandb_run: your_name wandb_runid: efadbf9b-21a1-4759-b077-7318afa3023b warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 82d08f21-4bb1-4308-9065-148b065a4aff This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5972 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0020 \| 1 \| 1.8020 \| \| 6.7263 \| 0.0996 \| 50 \| 1.6665 \| \| 6.8055 \| 0.1992 \| 100 \| 1.6211 \| \| 6.4924 \| 0.2988 \| 150 \| 1.6010 \| \| 6.3948 \| 0.3984 \| 200 \| 1.5972 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
laquythang/b9a3e559-1ad5-4539-ba55-6c392c5b9b85	laquythang	2025-02-03T14:54:57Z	23	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:databricks/dolly-v2-3b", "base_model:adapter:databricks/dolly-v2-3b", "license:mit", "8-bit", "bitsandbytes", "region:us" ]	null	2025-02-03T13:36:46Z	--- library_name: peft license: mit base_model: databricks/dolly-v2-3b tags: - axolotl - generated_from_trainer model-index: - name: b9a3e559-1ad5-4539-ba55-6c392c5b9b85 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: databricks/dolly-v2-3b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 4a429ee5f3ef6fbc_train_data.json ds_type: json format: custom path: /workspace/input_data/4a429ee5f3ef6fbc_train_data.json type: field_input: output_masked field_instruction: input field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: laquythang/b9a3e559-1ad5-4539-ba55-6c392c5b9b85 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/4a429ee5f3ef6fbc_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: fb408b50-b335-445c-8ea1-12c49353acab wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: fb408b50-b335-445c-8ea1-12c49353acab warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # b9a3e559-1ad5-4539-ba55-6c392c5b9b85 This model is a fine-tuned version of [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0007 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0004 \| 0.0026 \| 200 \| 0.0007 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/Liberated-Qwen1.5-7B-GGUF	mradermacher	2025-02-03T14:54:10Z	89	1	transformers	[ "transformers", "gguf", "en", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/Code-Feedback", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:abacusai/SystemChat", "base_model:abacusai/Liberated-Qwen1.5-7B", "base_model:quantized:abacusai/Liberated-Qwen1.5-7B", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-22T08:41:19Z	--- base_model: abacusai/Liberated-Qwen1.5-7B datasets: - teknium/OpenHermes-2.5 - m-a-p/Code-Feedback - m-a-p/CodeFeedback-Filtered-Instruction - abacusai/SystemChat language: - en library_name: transformers license: other license_link: https://huggingface.co/Qwen/Qwen1.5-72B/blob/main/LICENSE license_name: tongyi-qianwen quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/abacusai/Liberated-Qwen1.5-7B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Liberated-Qwen1.5-7B-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/Liberated-Qwen1.5-7B-GGUF/resolve/main/Liberated-Qwen1.5-7B.Q2_K.gguf) \| Q2_K \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Liberated-Qwen1.5-7B-GGUF/resolve/main/Liberated-Qwen1.5-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Liberated-Qwen1.5-7B-GGUF/resolve/main/Liberated-Qwen1.5-7B.Q3_K_M.gguf) \| Q3_K_M \| 4.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Liberated-Qwen1.5-7B-GGUF/resolve/main/Liberated-Qwen1.5-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Liberated-Qwen1.5-7B-GGUF/resolve/main/Liberated-Qwen1.5-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Liberated-Qwen1.5-7B-GGUF/resolve/main/Liberated-Qwen1.5-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Liberated-Qwen1.5-7B-GGUF/resolve/main/Liberated-Qwen1.5-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Liberated-Qwen1.5-7B-GGUF/resolve/main/Liberated-Qwen1.5-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Liberated-Qwen1.5-7B-GGUF/resolve/main/Liberated-Qwen1.5-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Liberated-Qwen1.5-7B-GGUF/resolve/main/Liberated-Qwen1.5-7B.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Liberated-Qwen1.5-7B-GGUF/resolve/main/Liberated-Qwen1.5-7B.Q8_0.gguf) \| Q8_0 \| 8.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Liberated-Qwen1.5-7B-GGUF/resolve/main/Liberated-Qwen1.5-7B.f16.gguf) \| f16 \| 15.5 \| 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 -->
fifxus/570ecc57-3241-4504-ad15-76f3fcc45e68	fifxus	2025-02-03T14:52:54Z	9	0	peft	[ "peft", "safetensors", "gemma2", "axolotl", "generated_from_trainer", "base_model:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "base_model:adapter:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "license:gemma", "8-bit", "bitsandbytes", "region:us" ]	null	2025-02-03T14:20:56Z	--- library_name: peft license: gemma base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 tags: - axolotl - generated_from_trainer model-index: - name: 570ecc57-3241-4504-ad15-76f3fcc45e68 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - bb477885411926f5_train_data.json ds_type: json format: custom path: /workspace/input_data/bb477885411926f5_train_data.json type: field_input: comment field_instruction: prompt field_output: chosen format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: null eval_batch_size: 2 eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: true hub_model_id: fifxus/570ecc57-3241-4504-ad15-76f3fcc45e68 hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0001 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/bb477885411926f5_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null saves_per_epoch: null sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: 34a20bc2-fcbe-44a0-988b-720bf4465c7f wandb_project: Gradients-On-10 wandb_run: your_name wandb_runid: 34a20bc2-fcbe-44a0-988b-720bf4465c7f warmup_steps: 5 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 570ecc57-3241-4504-ad15-76f3fcc45e68 This model is a fine-tuned version of [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2812 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.294 \| 0.1427 \| 200 \| 0.2812 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
corranm/square_run_age_gender	corranm	2025-02-03T14:52:07Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-02-03T14:51:59Z	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: square_run_age_gender 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. --> # square_run_age_gender This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4067 - F1 Macro: 0.4365 - F1 Micro: 0.5152 - F1 Weighted: 0.4956 - Precision Macro: 0.4384 - Precision Micro: 0.5152 - Precision Weighted: 0.4986 - Recall Macro: 0.4561 - Recall Micro: 0.5152 - Recall Weighted: 0.5152 - Accuracy: 0.5152 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 35 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Macro \| F1 Micro \| F1 Weighted \| Precision Macro \| Precision Micro \| Precision Weighted \| Recall Macro \| Recall Micro \| Recall Weighted \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------:\|:-----------:\|:---------------:\|:---------------:\|:------------------:\|:------------:\|:------------:\|:---------------:\|:--------:\| \| 1.8891 \| 1.0 \| 29 \| 1.8671 \| 0.1742 \| 0.2576 \| 0.2101 \| 0.1681 \| 0.2576 \| 0.2045 \| 0.2142 \| 0.2576 \| 0.2576 \| 0.2576 \| \| 1.8327 \| 2.0 \| 58 \| 1.8124 \| 0.1570 \| 0.3182 \| 0.1937 \| 0.1335 \| 0.3182 \| 0.1611 \| 0.2508 \| 0.3182 \| 0.3182 \| 0.3182 \| \| 1.9127 \| 3.0 \| 87 \| 1.7830 \| 0.2085 \| 0.3182 \| 0.2576 \| 0.2128 \| 0.3182 \| 0.2618 \| 0.2625 \| 0.3182 \| 0.3182 \| 0.3182 \| \| 1.4498 \| 4.0 \| 116 \| 1.5796 \| 0.2936 \| 0.3864 \| 0.3438 \| 0.4342 \| 0.3864 \| 0.4527 \| 0.3179 \| 0.3864 \| 0.3864 \| 0.3864 \| \| 1.2166 \| 5.0 \| 145 \| 1.3485 \| 0.3868 \| 0.4773 \| 0.4442 \| 0.5068 \| 0.4773 \| 0.5373 \| 0.4077 \| 0.4773 \| 0.4773 \| 0.4773 \| \| 1.5704 \| 6.0 \| 174 \| 1.2560 \| 0.4853 \| 0.5606 \| 0.5510 \| 0.4906 \| 0.5606 \| 0.5679 \| 0.5026 \| 0.5606 \| 0.5606 \| 0.5606 \| \| 1.2465 \| 7.0 \| 203 \| 1.4968 \| 0.3854 \| 0.4924 \| 0.4393 \| 0.5611 \| 0.4924 \| 0.5975 \| 0.4107 \| 0.4924 \| 0.4924 \| 0.4924 \| \| 1.2531 \| 8.0 \| 232 \| 1.4663 \| 0.4380 \| 0.5 \| 0.4841 \| 0.4623 \| 0.5 \| 0.5302 \| 0.4693 \| 0.5 \| 0.5 \| 0.5 \| \| 0.5318 \| 9.0 \| 261 \| 1.1161 \| 0.4938 \| 0.5909 \| 0.5646 \| 0.4892 \| 0.5909 \| 0.5595 \| 0.5176 \| 0.5909 \| 0.5909 \| 0.5909 \| \| 0.6824 \| 10.0 \| 290 \| 1.1811 \| 0.4802 \| 0.5909 \| 0.5515 \| 0.4814 \| 0.5909 \| 0.5498 \| 0.5148 \| 0.5909 \| 0.5909 \| 0.5909 \| \| 0.6324 \| 11.0 \| 319 \| 1.2358 \| 0.4927 \| 0.5758 \| 0.5506 \| 0.5015 \| 0.5758 \| 0.5690 \| 0.5226 \| 0.5758 \| 0.5758 \| 0.5758 \| \| 0.4145 \| 12.0 \| 348 \| 1.1608 \| 0.5846 \| 0.6742 \| 0.6643 \| 0.5822 \| 0.6742 \| 0.6681 \| 0.6005 \| 0.6742 \| 0.6742 \| 0.6742 \| \| 0.4805 \| 13.0 \| 377 \| 1.3200 \| 0.5276 \| 0.5758 \| 0.5689 \| 0.5767 \| 0.5758 \| 0.6138 \| 0.5269 \| 0.5758 \| 0.5758 \| 0.5758 \| \| 0.6232 \| 14.0 \| 406 \| 1.3190 \| 0.4790 \| 0.5758 \| 0.5517 \| 0.5025 \| 0.5758 \| 0.5734 \| 0.5006 \| 0.5758 \| 0.5758 \| 0.5758 \| \| 0.3475 \| 15.0 \| 435 \| 1.1853 \| 0.6303 \| 0.6970 \| 0.6894 \| 0.6717 \| 0.6970 \| 0.7088 \| 0.6312 \| 0.6970 \| 0.6970 \| 0.6970 \| \| 0.1956 \| 16.0 \| 464 \| 1.5695 \| 0.4323 \| 0.5152 \| 0.4974 \| 0.4755 \| 0.5152 \| 0.5334 \| 0.4358 \| 0.5152 \| 0.5152 \| 0.5152 \| \| 0.1519 \| 17.0 \| 493 \| 1.4404 \| 0.5819 \| 0.6439 \| 0.6317 \| 0.6438 \| 0.6439 \| 0.6577 \| 0.5706 \| 0.6439 \| 0.6439 \| 0.6439 \| \| 0.1031 \| 18.0 \| 522 \| 1.4877 \| 0.5370 \| 0.6136 \| 0.6041 \| 0.5351 \| 0.6136 \| 0.5975 \| 0.5422 \| 0.6136 \| 0.6136 \| 0.6136 \| \| 0.0615 \| 19.0 \| 551 \| 1.4801 \| 0.6013 \| 0.6061 \| 0.6106 \| 0.6476 \| 0.6061 \| 0.6581 \| 0.5951 \| 0.6061 \| 0.6061 \| 0.6061 \| \| 0.0249 \| 20.0 \| 580 \| 1.6082 \| 0.5198 \| 0.5909 \| 0.5825 \| 0.5149 \| 0.5909 \| 0.5770 \| 0.5272 \| 0.5909 \| 0.5909 \| 0.5909 \| \| 0.374 \| 21.0 \| 609 \| 1.7594 \| 0.6084 \| 0.6288 \| 0.6185 \| 0.6712 \| 0.6288 \| 0.6679 \| 0.6049 \| 0.6288 \| 0.6288 \| 0.6288 \| \| 0.025 \| 22.0 \| 638 \| 1.4723 \| 0.6446 \| 0.6515 \| 0.6520 \| 0.6543 \| 0.6515 \| 0.6660 \| 0.6479 \| 0.6515 \| 0.6515 \| 0.6515 \| \| 0.0096 \| 23.0 \| 667 \| 1.5689 \| 0.5899 \| 0.6136 \| 0.6089 \| 0.6170 \| 0.6136 \| 0.6315 \| 0.5878 \| 0.6136 \| 0.6136 \| 0.6136 \| \| 0.0661 \| 24.0 \| 696 \| 1.6276 \| 0.6056 \| 0.6667 \| 0.6576 \| 0.6690 \| 0.6667 \| 0.6867 \| 0.5949 \| 0.6667 \| 0.6667 \| 0.6667 \| \| 0.0463 \| 25.0 \| 725 \| 1.6761 \| 0.5591 \| 0.6136 \| 0.6085 \| 0.6193 \| 0.6136 \| 0.6401 \| 0.5521 \| 0.6136 \| 0.6136 \| 0.6136 \| \| 0.0118 \| 26.0 \| 754 \| 1.6210 \| 0.5353 \| 0.6288 \| 0.6075 \| 0.5716 \| 0.6288 \| 0.6263 \| 0.5410 \| 0.6288 \| 0.6288 \| 0.6288 \| \| 0.0018 \| 27.0 \| 783 \| 1.6073 \| 0.5860 \| 0.6742 \| 0.6575 \| 0.5956 \| 0.6742 \| 0.6587 \| 0.5929 \| 0.6742 \| 0.6742 \| 0.6742 \| \| 0.0336 \| 28.0 \| 812 \| 1.5964 \| 0.6086 \| 0.6439 \| 0.6411 \| 0.6379 \| 0.6439 \| 0.6566 \| 0.5979 \| 0.6439 \| 0.6439 \| 0.6439 \| \| 0.0014 \| 29.0 \| 841 \| 1.5290 \| 0.6873 \| 0.7121 \| 0.7083 \| 0.7263 \| 0.7121 \| 0.7308 \| 0.6734 \| 0.7121 \| 0.7121 \| 0.7121 \| \| 0.021 \| 30.0 \| 870 \| 1.5440 \| 0.6982 \| 0.6970 \| 0.6974 \| 0.7076 \| 0.6970 \| 0.7170 \| 0.7086 \| 0.6970 \| 0.6970 \| 0.6970 \| \| 0.0065 \| 31.0 \| 899 \| 1.6576 \| 0.6869 \| 0.6970 \| 0.6915 \| 0.7430 \| 0.6970 \| 0.7270 \| 0.6699 \| 0.6970 \| 0.6970 \| 0.6970 \| \| 0.0013 \| 32.0 \| 928 \| 1.5603 \| 0.7124 \| 0.7197 \| 0.7173 \| 0.7508 \| 0.7197 \| 0.7411 \| 0.6987 \| 0.7197 \| 0.7197 \| 0.7197 \| \| 0.0129 \| 33.0 \| 957 \| 1.6028 \| 0.6842 \| 0.6894 \| 0.6870 \| 0.7153 \| 0.6894 \| 0.7059 \| 0.6731 \| 0.6894 \| 0.6894 \| 0.6894 \| \| 0.0006 \| 34.0 \| 986 \| 1.6075 \| 0.6787 \| 0.6818 \| 0.6800 \| 0.7094 \| 0.6818 \| 0.6991 \| 0.6678 \| 0.6818 \| 0.6818 \| 0.6818 \| \| 0.0022 \| 35.0 \| 1015 \| 1.6009 \| 0.6848 \| 0.6894 \| 0.6869 \| 0.7171 \| 0.6894 \| 0.7062 \| 0.6731 \| 0.6894 \| 0.6894 \| 0.6894 \| ### Framework versions - Transformers 4.48.2 - Pytorch 2.6.0+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
adammandic87/5f8d10c2-fdea-4b03-b493-30f51a8fb88d	adammandic87	2025-02-03T14:51:59Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2025-02-03T14:50:00Z	--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - axolotl - generated_from_trainer model-index: - name: 5f8d10c2-fdea-4b03-b493-30f51a8fb88d results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - eb57db6348d4b1da_train_data.json ds_type: json format: custom path: /workspace/input_data/eb57db6348d4b1da_train_data.json type: field_instruction: context field_output: question format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: adammandic87/5f8d10c2-fdea-4b03-b493-30f51a8fb88d hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: constant max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/eb57db6348d4b1da_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: e3ab71d4-7a9b-4efa-af05-d475e3deb9d8 wandb_project: Birthday-SN56-34-Gradients-On-Demand wandb_run: your_name wandb_runid: e3ab71d4-7a9b-4efa-af05-d475e3deb9d8 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 5f8d10c2-fdea-4b03-b493-30f51a8fb88d This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9357 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0011 \| 1 \| 1.8509 \| \| 1.0101 \| 0.0564 \| 50 \| 1.0259 \| \| 0.9268 \| 0.1128 \| 100 \| 0.9990 \| \| 0.8607 \| 0.1693 \| 150 \| 0.9542 \| \| 0.9075 \| 0.2257 \| 200 \| 0.9357 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
JacksonBrune/babf8d25-a8e4-4bfc-a299-7c4984252328	JacksonBrune	2025-02-03T14:51:56Z	9	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2025-02-03T14:49:33Z	--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - axolotl - generated_from_trainer model-index: - name: babf8d25-a8e4-4bfc-a299-7c4984252328 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - eb57db6348d4b1da_train_data.json ds_type: json format: custom path: /workspace/input_data/eb57db6348d4b1da_train_data.json type: field_instruction: context field_output: question format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: JacksonBrune/babf8d25-a8e4-4bfc-a299-7c4984252328 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 250 micro_batch_size: 2 mlflow_experiment_name: /tmp/eb57db6348d4b1da_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: e3ab71d4-7a9b-4efa-af05-d475e3deb9d8 wandb_project: birthdya-sn56-18-Gradients-On-Demand wandb_run: your_name wandb_runid: e3ab71d4-7a9b-4efa-af05-d475e3deb9d8 warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # babf8d25-a8e4-4bfc-a299-7c4984252328 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9344 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 250 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0011 \| 1 \| 1.8858 \| \| 1.0102 \| 0.0711 \| 63 \| 1.0147 \| \| 0.8598 \| 0.1422 \| 126 \| 0.9722 \| \| 0.9627 \| 0.2133 \| 189 \| 0.9344 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
botenius/c496f2c0-697a-4234-ae12-a7514b8e097e	botenius	2025-02-03T14:51:48Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-02-03T14:44:17Z	--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - axolotl - generated_from_trainer model-index: - name: c496f2c0-697a-4234-ae12-a7514b8e097e results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - eb57db6348d4b1da_train_data.json ds_type: json format: custom path: /workspace/input_data/eb57db6348d4b1da_train_data.json type: field_instruction: context field_output: question format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: null eval_batch_size: 2 eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: true hub_model_id: botenius/c496f2c0-697a-4234-ae12-a7514b8e097e hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0001 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/eb57db6348d4b1da_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null saves_per_epoch: null sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: e3ab71d4-7a9b-4efa-af05-d475e3deb9d8 wandb_project: Gradients-On-13 wandb_run: your_name wandb_runid: e3ab71d4-7a9b-4efa-af05-d475e3deb9d8 warmup_steps: 5 weight_decay: 0.01 xformers_attention: null ``` </details><br> # c496f2c0-697a-4234-ae12-a7514b8e097e This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9844 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0684 \| 0.2257 \| 200 \| 0.9844 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
adammandic87/9390d91d-f9ff-4593-adb8-e9da48d01658	adammandic87	2025-02-03T14:48:43Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:trl-internal-testing/tiny-random-LlamaForCausalLM", "base_model:adapter:trl-internal-testing/tiny-random-LlamaForCausalLM", "region:us" ]	null	2025-02-03T14:48:17Z	--- library_name: peft base_model: trl-internal-testing/tiny-random-LlamaForCausalLM tags: - axolotl - generated_from_trainer model-index: - name: 9390d91d-f9ff-4593-adb8-e9da48d01658 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: trl-internal-testing/tiny-random-LlamaForCausalLM bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 8512442b605c78da_train_data.json ds_type: json format: custom path: /workspace/input_data/8512442b605c78da_train_data.json type: field_instruction: Input field_output: Rephrased Content format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: adammandic87/9390d91d-f9ff-4593-adb8-e9da48d01658 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: constant max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/8512442b605c78da_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: bc22d19e-3c5a-4a4e-ab3c-1133a5b4060b wandb_project: Birthday-SN56-34-Gradients-On-Demand wandb_run: your_name wandb_runid: bc22d19e-3c5a-4a4e-ab3c-1133a5b4060b warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 9390d91d-f9ff-4593-adb8-e9da48d01658 This model is a fine-tuned version of [trl-internal-testing/tiny-random-LlamaForCausalLM](https://huggingface.co/trl-internal-testing/tiny-random-LlamaForCausalLM) on the None dataset. It achieves the following results on the evaluation set: - Loss: 10.3296 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0028 \| 1 \| 10.3775 \| \| 10.3745 \| 0.1400 \| 50 \| 10.3720 \| \| 10.3588 \| 0.2799 \| 100 \| 10.3546 \| \| 10.3409 \| 0.4199 \| 150 \| 10.3352 \| \| 10.335 \| 0.5598 \| 200 \| 10.3296 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Best000/a6a42969-7ae7-4974-a5e5-c4149ef6a2ed	Best000	2025-02-03T14:47:31Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2025-02-03T14:44:48Z	--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - axolotl - generated_from_trainer model-index: - name: a6a42969-7ae7-4974-a5e5-c4149ef6a2ed 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) # a6a42969-7ae7-4974-a5e5-c4149ef6a2ed This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9370 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Cran-May/SCE-3-24B	Cran-May	2025-02-03T14:47:27Z	30	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2408.07990", "base_model:AlSamCur123/Mistral-Small3-24B-InstructContinuedFine", "base_model:merge:AlSamCur123/Mistral-Small3-24B-InstructContinuedFine", "base_model:huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated", "base_model:merge:huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated", "base_model:trashpanda-org/MS-24B-Instruct-Mullein-v0", "base_model:merge:trashpanda-org/MS-24B-Instruct-Mullein-v0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-03T14:35:48Z	--- base_model: - AlSamCur123/Mistral-Small3-24B-InstructContinuedFine - trashpanda-org/MS-24B-Instruct-Mullein-v0 - huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using [huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated](https://huggingface.co/huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated) as a base. ### Models Merged The following models were included in the merge: * [AlSamCur123/Mistral-Small3-24B-InstructContinuedFine](https://huggingface.co/AlSamCur123/Mistral-Small3-24B-InstructContinuedFine) * [trashpanda-org/MS-24B-Instruct-Mullein-v0](https://huggingface.co/trashpanda-org/MS-24B-Instruct-Mullein-v0) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: sce models: - model: trashpanda-org/MS-24B-Instruct-Mullein-v0 - model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated - model: AlSamCur123/Mistral-Small3-24B-InstructContinuedFine base_model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated tokenizer: source: base parameters: select_topk: 0.8 dtype: float32 out_dtype: bfloat16 normalize: true ```
havinash-ai/c5feaada-eb55-42bc-9402-2d6bf3824df4	havinash-ai	2025-02-03T14:47:11Z	8	0	peft	[ "peft", "safetensors", "gemma2", "axolotl", "generated_from_trainer", "base_model:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "base_model:adapter:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", "license:gemma", "region:us" ]	null	2025-02-03T14:35:52Z	--- library_name: peft license: gemma base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 tags: - axolotl - generated_from_trainer model-index: - name: c5feaada-eb55-42bc-9402-2d6bf3824df4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - bb477885411926f5_train_data.json ds_type: json format: custom path: /workspace/input_data/bb477885411926f5_train_data.json type: field_input: comment field_instruction: prompt field_output: chosen format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: havinash-ai/c5feaada-eb55-42bc-9402-2d6bf3824df4 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/bb477885411926f5_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 34a20bc2-fcbe-44a0-988b-720bf4465c7f wandb_project: Birthday-SN56-9-Gradients-On-Demand wandb_run: your_name wandb_runid: 34a20bc2-fcbe-44a0-988b-720bf4465c7f warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # c5feaada-eb55-42bc-9402-2d6bf3824df4 This model is a fine-tuned version of [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2901 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0007 \| 1 \| 0.6165 \| \| 0.2918 \| 0.0357 \| 50 \| 0.3071 \| \| 0.2869 \| 0.0714 \| 100 \| 0.2972 \| \| 0.2623 \| 0.1070 \| 150 \| 0.2917 \| \| 0.3361 \| 0.1427 \| 200 \| 0.2901 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/Visual-LaylelemonMaidRP-7B-GGUF	mradermacher	2025-02-03T14:46:01Z	310	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:ChaoticNeutrals/Visual-LaylelemonMaidRP-7B", "base_model:quantized:ChaoticNeutrals/Visual-LaylelemonMaidRP-7B", "license:other", "endpoints_compatible", "region:us" ]	null	2025-01-10T01:57:32Z	--- base_model: ChaoticNeutrals/Visual-LaylelemonMaidRP-7B language: - en library_name: transformers license: other quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ChaoticNeutrals/Visual-LaylelemonMaidRP-7B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-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/Visual-LaylelemonMaidRP-7B-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-GGUF/resolve/main/Visual-LaylelemonMaidRP-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/Visual-LaylelemonMaidRP-7B-i1-GGUF	mradermacher	2025-02-03T14:45:58Z	1,155	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:ChaoticNeutrals/Visual-LaylelemonMaidRP-7B", "base_model:quantized:ChaoticNeutrals/Visual-LaylelemonMaidRP-7B", "license:other", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-01-10T02:13:21Z	--- base_model: ChaoticNeutrals/Visual-LaylelemonMaidRP-7B language: - en library_name: transformers license: other quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/ChaoticNeutrals/Visual-LaylelemonMaidRP-7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-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/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-Q2_K.gguf) \| i1-Q2_K \| 2.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-Q4_1.gguf) \| i1-Q4_1 \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-7B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Visual-LaylelemonMaidRP-7B-i1-GGUF/resolve/main/Visual-LaylelemonMaidRP-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 -->
lesso/6ce42d15-afc9-4c19-bd9e-0541d6854588	lesso	2025-02-03T14:44:09Z	11	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-14B", "base_model:adapter:unsloth/Qwen2.5-14B", "license:apache-2.0", "region:us" ]	null	2025-02-03T12:55:35Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-14B tags: - axolotl - generated_from_trainer model-index: - name: 6ce42d15-afc9-4c19-bd9e-0541d6854588 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2.5-14B bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - c1486cc2f4ac5a54_train_data.json ds_type: json format: custom path: /workspace/input_data/c1486cc2f4ac5a54_train_data.json type: field_input: '' field_instruction: problem field_output: solution format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true group_by_length: true hub_model_id: lesso/6ce42d15-afc9-4c19-bd9e-0541d6854588 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000101 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: linear max_grad_norm: 1.0 max_steps: 200 micro_batch_size: 4 mlflow_experiment_name: /tmp/god16/c1486cc2f4ac5a54_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: ed31ad34-9ed6-494f-ba2a-b66db696a5b7 wandb_project: ab-god16 wandb_run: your_name wandb_runid: ed31ad34-9ed6-494f-ba2a-b66db696a5b7 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 6ce42d15-afc9-4c19-bd9e-0541d6854588 This model is a fine-tuned version of [unsloth/Qwen2.5-14B](https://huggingface.co/unsloth/Qwen2.5-14B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4812 ## 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: 0.000101 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.4355 \| 0.0055 \| 1 \| 0.6778 \| \| 0.5004 \| 0.2732 \| 50 \| 0.4980 \| \| 0.5165 \| 0.5464 \| 100 \| 0.4891 \| \| 0.4751 \| 0.8197 \| 150 \| 0.4859 \| \| 0.4903 \| 1.0929 \| 200 \| 0.4812 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
earnxus/69ac1801-70f2-4b2a-8ce1-01d56c9a36d3	earnxus	2025-02-03T14:42:47Z	9	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Orenguteng/Llama-3-8B-Lexi-Uncensored", "base_model:adapter:Orenguteng/Llama-3-8B-Lexi-Uncensored", "license:llama3", "8-bit", "bitsandbytes", "region:us" ]	null	2025-02-03T14:15:29Z	--- library_name: peft license: llama3 base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored tags: - axolotl - generated_from_trainer model-index: - name: 69ac1801-70f2-4b2a-8ce1-01d56c9a36d3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 54138ae07d40afb3_train_data.json ds_type: json format: custom path: /workspace/input_data/54138ae07d40afb3_train_data.json type: field_input: my_solu field_instruction: prompt field_output: solution format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: null eval_batch_size: 2 eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: true hub_model_id: earnxus/69ac1801-70f2-4b2a-8ce1-01d56c9a36d3 hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0001 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/54138ae07d40afb3_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null saves_per_epoch: null sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: 510dbfd5-ca5a-47fe-aa88-ee9a4e2a191e wandb_project: Gradients-On-Nine wandb_run: your_name wandb_runid: 510dbfd5-ca5a-47fe-aa88-ee9a4e2a191e warmup_steps: 5 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 69ac1801-70f2-4b2a-8ce1-01d56c9a36d3 This model is a fine-tuned version of [Orenguteng/Llama-3-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3-8B-Lexi-Uncensored) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6873 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7869 \| 0.0851 \| 200 \| 0.6873 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
awinml/distilbart-sec-10k-meta-pfizer-costco	awinml	2025-02-03T14:42:13Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-23T06:54:13Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: distilbart-cnn-12-6-sec 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. --> # distilbart-cnn-12-6-sec This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0798 - Rouge1: 72.1665 - Rouge2: 62.2601 - Rougel: 67.8376 - Rougelsum: 71.1407 - Gen Len: 121.62 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 99 \| 0.3526 \| 53.3978 \| 38.6395 \| 45.6271 \| 51.0477 \| 111.48 \| \| No log \| 2.0 \| 198 \| 0.1961 \| 55.7397 \| 43.6293 \| 50.9595 \| 54.0764 \| 111.46 \| \| No log \| 3.0 \| 297 \| 0.1483 \| 66.9443 \| 54.8966 \| 62.6678 \| 65.6787 \| 118.64 \| \| No log \| 4.0 \| 396 \| 0.1218 \| 67.2661 \| 56.1852 \| 63.1339 \| 65.8066 \| 124.92 \| \| No log \| 5.0 \| 495 \| 0.1139 \| 67.2097 \| 55.8694 \| 62.7508 \| 65.9706 \| 123.02 \| \| 0.4156 \| 6.0 \| 594 \| 0.0940 \| 71.607 \| 60.6697 \| 66.7873 \| 70.339 \| 122.84 \| \| 0.4156 \| 7.0 \| 693 \| 0.0888 \| 71.3792 \| 61.8326 \| 68.25 \| 70.5113 \| 124.4 \| \| 0.4156 \| 8.0 \| 792 \| 0.0870 \| 72.7472 \| 62.6968 \| 68.2853 \| 71.5789 \| 124.34 \| \| 0.4156 \| 9.0 \| 891 \| 0.0799 \| 73.4438 \| 63.5966 \| 68.8737 \| 72.3014 \| 119.88 \| \| 0.4156 \| 10.0 \| 990 \| 0.0798 \| 72.1665 \| 62.2601 \| 67.8376 \| 71.1407 \| 121.62 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
politeles/distilhubert-finetuned-gtzan	politeles	2025-02-03T14:41:00Z	167	0	transformers	[ "transformers", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-11-01T10:07:45Z	--- library_name: transformers license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan 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. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1988 - Accuracy: 0.9404 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.7008 \| 1.0 \| 76 \| 1.6010 \| 0.5497 \| \| 0.8918 \| 2.0 \| 152 \| 0.9346 \| 0.6954 \| \| 0.6802 \| 3.0 \| 228 \| 0.6734 \| 0.7815 \| \| 0.3291 \| 4.0 \| 304 \| 0.4803 \| 0.8543 \| \| 0.2609 \| 5.0 \| 380 \| 0.3473 \| 0.8808 \| \| 0.1061 \| 6.0 \| 456 \| 0.2439 \| 0.9272 \| \| 0.1252 \| 7.0 \| 532 \| 0.2127 \| 0.9536 \| \| 0.084 \| 8.0 \| 608 \| 0.1980 \| 0.9404 \| \| 0.0374 \| 9.0 \| 684 \| 0.2005 \| 0.9404 \| \| 0.0431 \| 10.0 \| 760 \| 0.1988 \| 0.9404 \| ### Framework versions - Transformers 4.49.0.dev0 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
jssky/205c890f-35cd-4212-87ec-0f02231f5331	jssky	2025-02-03T14:40:44Z	10	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:fxmarty/tiny-random-GemmaForCausalLM", "base_model:adapter:fxmarty/tiny-random-GemmaForCausalLM", "license:mit", "region:us" ]	null	2025-02-03T14:38:37Z	--- library_name: peft license: mit base_model: fxmarty/tiny-random-GemmaForCausalLM tags: - axolotl - generated_from_trainer model-index: - name: 205c890f-35cd-4212-87ec-0f02231f5331 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.6.0` ```yaml adapter: lora base_model: fxmarty/tiny-random-GemmaForCausalLM bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - fdd6181bd48eebb0_train_data.json ds_type: json format: custom path: /workspace/input_data/fdd6181bd48eebb0_train_data.json type: field_instruction: Question field_output: Answers format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: jssky/205c890f-35cd-4212-87ec-0f02231f5331 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/fdd6181bd48eebb0_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3ce43b7c-a05f-4c96-a0ad-4322c88107a2 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 3ce43b7c-a05f-4c96-a0ad-4322c88107a2 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 205c890f-35cd-4212-87ec-0f02231f5331 This model is a fine-tuned version of [fxmarty/tiny-random-GemmaForCausalLM](https://huggingface.co/fxmarty/tiny-random-GemmaForCausalLM) on the None dataset. It achieves the following results on the evaluation set: - Loss: 12.3124 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 12.4193 \| 0.0562 \| 50 \| 12.4284 \| \| 12.3125 \| 0.1125 \| 100 \| 12.3442 \| \| 12.2859 \| 0.1687 \| 150 \| 12.3156 \| \| 12.2807 \| 0.2249 \| 200 \| 12.3124 \| ### Framework versions - PEFT 0.14.0 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
adammandic87/9e628bdf-2cea-4b1a-a87f-8a058c4ae9f2	adammandic87	2025-02-03T14:40:23Z	8	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:fxmarty/tiny-random-GemmaForCausalLM", "base_model:adapter:fxmarty/tiny-random-GemmaForCausalLM", "license:mit", "region:us" ]	null	2025-02-03T14:39:09Z	--- library_name: peft license: mit base_model: fxmarty/tiny-random-GemmaForCausalLM tags: - axolotl - generated_from_trainer model-index: - name: 9e628bdf-2cea-4b1a-a87f-8a058c4ae9f2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: fxmarty/tiny-random-GemmaForCausalLM bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - fdd6181bd48eebb0_train_data.json ds_type: json format: custom path: /workspace/input_data/fdd6181bd48eebb0_train_data.json type: field_instruction: Question field_output: Answers format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: adammandic87/9e628bdf-2cea-4b1a-a87f-8a058c4ae9f2 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/fdd6181bd48eebb0_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3ce43b7c-a05f-4c96-a0ad-4322c88107a2 wandb_project: Birthday-SN56-13-Gradients-On-Demand wandb_run: your_name wandb_runid: 3ce43b7c-a05f-4c96-a0ad-4322c88107a2 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 9e628bdf-2cea-4b1a-a87f-8a058c4ae9f2 This model is a fine-tuned version of [fxmarty/tiny-random-GemmaForCausalLM](https://huggingface.co/fxmarty/tiny-random-GemmaForCausalLM) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0003 \| 1 \| nan \| \| 0.0 \| 0.0141 \| 50 \| nan \| \| 0.0 \| 0.0281 \| 100 \| nan \| \| 0.0 \| 0.0422 \| 150 \| nan \| \| 0.0 \| 0.0562 \| 200 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
latiao1999/task-3-Qwen-Qwen1.5-7B	latiao1999	2025-02-03T14:39:55Z	124	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-7B", "base_model:adapter:Qwen/Qwen1.5-7B", "region:us" ]	null	2025-02-03T14:34:32Z	--- base_model: Qwen/Qwen1.5-7B 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 0.13.2
outlookAi/nMo2HhuBtY	outlookAi	2025-02-03T14:35:31Z	7	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-02-03T14:18:33Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Yumeno aika --- # Nmo2Hhubty <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Yumeno aika` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('outlookAi/nMo2HhuBtY', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
Best000/85080883-9830-4f8a-854c-ea8bb7489614	Best000	2025-02-03T14:34:47Z	7	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:databricks/dolly-v2-3b", "base_model:adapter:databricks/dolly-v2-3b", "license:mit", "region:us" ]	null	2025-02-03T13:36:20Z	--- library_name: peft license: mit base_model: databricks/dolly-v2-3b tags: - axolotl - generated_from_trainer model-index: - name: 85080883-9830-4f8a-854c-ea8bb7489614 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) # 85080883-9830-4f8a-854c-ea8bb7489614 This model is a fine-tuned version of [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0376 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ciloku/8d9f03d7-2e68-4199-825e-3ff35c279898	ciloku	2025-02-03T14:31:35Z	8	0	peft	[ "peft", "safetensors", "phi", "axolotl", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2025-02-03T14:03:30Z	--- library_name: peft license: mit base_model: microsoft/phi-2 tags: - axolotl - generated_from_trainer model-index: - name: 8d9f03d7-2e68-4199-825e-3ff35c279898 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: microsoft/phi-2 bf16: true chat_template: llama3 data_processes: 24 dataset_prepared_path: null datasets: - data_files: - 14adcf56bd267abc_train_data.json ds_type: json format: custom path: /workspace/input_data/14adcf56bd267abc_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 4 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: ciloku/8d9f03d7-2e68-4199-825e-3ff35c279898 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 6.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.04 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine lr_scheduler_warmup_steps: 50 max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/14adcf56bd267abc_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-8 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null seed: 17333 sequence_len: 1024 special_tokens: pad_token: <\|endoftext\|> strict: false tf32: true tokenizer_type: AutoTokenizer total_train_batch_size: 32 train_batch_size: 8 train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3bf53e4e-e50e-483e-a51f-f8ec21733093 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 3bf53e4e-e50e-483e-a51f-f8ec21733093 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 8d9f03d7-2e68-4199-825e-3ff35c279898 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0028 ## 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: 6e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 17333 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-8 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.9838 \| 0.0007 \| 1 \| 1.7666 \| \| 2.1489 \| 0.0326 \| 50 \| 1.1396 \| \| 1.295 \| 0.0653 \| 100 \| 1.0394 \| \| 1.5576 \| 0.0979 \| 150 \| 1.0104 \| \| 1.3632 \| 0.1306 \| 200 \| 1.0028 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
jongheeyun/Mistral-7B-Instruct-v0.2-Q5_K_M-GGUF	jongheeyun	2025-02-03T14:28:06Z	18	0	null	[ "gguf", "finetuned", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:quantized:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-02-03T14:27:44Z	--- license: apache-2.0 tags: - finetuned - llama-cpp - gguf-my-repo pipeline_tag: text-generation new_version: mistralai/Mistral-7B-Instruct-v0.3 inference: true widget: - messages: - role: user content: What is your favorite condiment? extra_gated_description: If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>. base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # jongheeyun/Mistral-7B-Instruct-v0.2-Q5_K_M-GGUF This model was converted to GGUF format from [`mistralai/Mistral-7B-Instruct-v0.2`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo jongheeyun/Mistral-7B-Instruct-v0.2-Q5_K_M-GGUF --hf-file mistral-7b-instruct-v0.2-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo jongheeyun/Mistral-7B-Instruct-v0.2-Q5_K_M-GGUF --hf-file mistral-7b-instruct-v0.2-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo jongheeyun/Mistral-7B-Instruct-v0.2-Q5_K_M-GGUF --hf-file mistral-7b-instruct-v0.2-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo jongheeyun/Mistral-7B-Instruct-v0.2-Q5_K_M-GGUF --hf-file mistral-7b-instruct-v0.2-q5_k_m.gguf -c 2048 ```
cimol/93b1760a-0a73-4275-90ab-51fe627c6b99	cimol	2025-02-03T14:26:23Z	9	0	peft	[ "peft", "safetensors", "gemma2", "axolotl", "generated_from_trainer", "base_model:unsloth/gemma-2-2b-it", "base_model:adapter:unsloth/gemma-2-2b-it", "license:gemma", "region:us" ]	null	2025-02-03T14:10:23Z	--- library_name: peft license: gemma base_model: unsloth/gemma-2-2b-it tags: - axolotl - generated_from_trainer model-index: - name: 93b1760a-0a73-4275-90ab-51fe627c6b99 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/gemma-2-2b-it bf16: true chat_template: llama3 data_processes: 24 dataset_prepared_path: null datasets: - data_files: - 7465fecdd1b4fae8_train_data.json ds_type: json format: custom path: /workspace/input_data/7465fecdd1b4fae8_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 4 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: cimol/93b1760a-0a73-4275-90ab-51fe627c6b99 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 7.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.04 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine lr_scheduler_warmup_steps: 50 max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/7465fecdd1b4fae8_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-8 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null seed: 17333 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer total_train_batch_size: 32 train_batch_size: 8 train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 96ba0598-e365-4fe4-a421-689fa74a779f wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 96ba0598-e365-4fe4-a421-689fa74a779f warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 93b1760a-0a73-4275-90ab-51fe627c6b99 This model is a fine-tuned version of [unsloth/gemma-2-2b-it](https://huggingface.co/unsloth/gemma-2-2b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0530 ## 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: 7e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 17333 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-8 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.4341 \| 0.0070 \| 1 \| 3.9003 \| \| 0.112 \| 0.3497 \| 50 \| 0.0839 \| \| 0.0215 \| 0.6993 \| 100 \| 0.0665 \| \| 0.0475 \| 1.0490 \| 150 \| 0.0573 \| \| 0.0357 \| 1.3986 \| 200 \| 0.0530 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
JacksonBrune/4bd7f578-4671-4ee0-9c1c-0009a68b91b2	JacksonBrune	2025-02-03T14:26:05Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Orenguteng/Llama-3-8B-Lexi-Uncensored", "base_model:adapter:Orenguteng/Llama-3-8B-Lexi-Uncensored", "license:llama3", "region:us" ]	null	2025-02-03T14:16:47Z	--- library_name: peft license: llama3 base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored tags: - axolotl - generated_from_trainer model-index: - name: 4bd7f578-4671-4ee0-9c1c-0009a68b91b2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 54138ae07d40afb3_train_data.json ds_type: json format: custom path: /workspace/input_data/54138ae07d40afb3_train_data.json type: field_input: my_solu field_instruction: prompt field_output: solution format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: JacksonBrune/4bd7f578-4671-4ee0-9c1c-0009a68b91b2 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 250 micro_batch_size: 2 mlflow_experiment_name: /tmp/54138ae07d40afb3_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 510dbfd5-ca5a-47fe-aa88-ee9a4e2a191e wandb_project: birthdya-sn56-18-Gradients-On-Demand wandb_run: your_name wandb_runid: 510dbfd5-ca5a-47fe-aa88-ee9a4e2a191e warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 4bd7f578-4671-4ee0-9c1c-0009a68b91b2 This model is a fine-tuned version of [Orenguteng/Llama-3-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3-8B-Lexi-Uncensored) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6604 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 250 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0004 \| 1 \| 0.9751 \| \| 0.6871 \| 0.0268 \| 63 \| 0.7150 \| \| 0.6935 \| 0.0536 \| 126 \| 0.6741 \| \| 0.6068 \| 0.0804 \| 189 \| 0.6604 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/Captain-Eris_Twilight-V0.420-12B-GGUF	mradermacher	2025-02-03T14:22:57Z	297	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:ChaoticNeutrals/Captain-Eris_Twilight-V0.420-12B", "base_model:quantized:ChaoticNeutrals/Captain-Eris_Twilight-V0.420-12B", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-18T12:21:55Z	--- base_model: ChaoticNeutrals/Captain-Eris_Twilight-V0.420-12B language: - en library_name: transformers license: other quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/ChaoticNeutrals/Captain-Eris_Twilight-V0.420-12B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Captain-Eris_Twilight-V0.420-12B-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/Captain-Eris_Twilight-V0.420-12B-GGUF/resolve/main/Captain-Eris_Twilight-V0.420-12B.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Captain-Eris_Twilight-V0.420-12B-GGUF/resolve/main/Captain-Eris_Twilight-V0.420-12B.Q3_K_S.gguf) \| Q3_K_S \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Captain-Eris_Twilight-V0.420-12B-GGUF/resolve/main/Captain-Eris_Twilight-V0.420-12B.Q3_K_M.gguf) \| Q3_K_M \| 6.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Captain-Eris_Twilight-V0.420-12B-GGUF/resolve/main/Captain-Eris_Twilight-V0.420-12B.Q3_K_L.gguf) \| Q3_K_L \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Captain-Eris_Twilight-V0.420-12B-GGUF/resolve/main/Captain-Eris_Twilight-V0.420-12B.IQ4_XS.gguf) \| IQ4_XS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Captain-Eris_Twilight-V0.420-12B-GGUF/resolve/main/Captain-Eris_Twilight-V0.420-12B.Q4_K_S.gguf) \| Q4_K_S \| 7.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Captain-Eris_Twilight-V0.420-12B-GGUF/resolve/main/Captain-Eris_Twilight-V0.420-12B.Q4_K_M.gguf) \| Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Captain-Eris_Twilight-V0.420-12B-GGUF/resolve/main/Captain-Eris_Twilight-V0.420-12B.Q5_K_S.gguf) \| Q5_K_S \| 8.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Captain-Eris_Twilight-V0.420-12B-GGUF/resolve/main/Captain-Eris_Twilight-V0.420-12B.Q5_K_M.gguf) \| Q5_K_M \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Captain-Eris_Twilight-V0.420-12B-GGUF/resolve/main/Captain-Eris_Twilight-V0.420-12B.Q6_K.gguf) \| Q6_K \| 10.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Captain-Eris_Twilight-V0.420-12B-GGUF/resolve/main/Captain-Eris_Twilight-V0.420-12B.Q8_0.gguf) \| Q8_0 \| 13.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mrferr3t/bf375029-8b59-4d88-9323-ff88834727c9	mrferr3t	2025-02-03T14:22:08Z	8	0	peft	[ "peft", "safetensors", "phi", "axolotl", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2025-02-03T14:04:17Z	--- library_name: peft license: mit base_model: microsoft/phi-2 tags: - axolotl - generated_from_trainer model-index: - name: bf375029-8b59-4d88-9323-ff88834727c9 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora auto_find_batch_size: true base_model: microsoft/phi-2 bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - 14adcf56bd267abc_train_data.json ds_type: json format: custom path: /workspace/input_data/14adcf56bd267abc_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 3 early_stopping_threshold: 0.001 eval_max_new_tokens: 128 eval_steps: 20 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: false hub_model_id: mrferr3t/bf375029-8b59-4d88-9323-ff88834727c9 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0003 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 100 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine micro_batch_size: 32 mlflow_experiment_name: /tmp/14adcf56bd267abc_train_data.json model_type: AutoModelForCausalLM num_epochs: 5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true s2_attention: null sample_packing: false save_steps: 20 saves_per_epoch: 0 sequence_len: 512 special_tokens: pad_token: <\|endoftext\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3bf53e4e-e50e-483e-a51f-f8ec21733093 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 3bf53e4e-e50e-483e-a51f-f8ec21733093 warmup_ratio: 0.05 weight_decay: 0.0 xformers_attention: null ``` </details><br> # bf375029-8b59-4d88-9323-ff88834727c9 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9845 ## 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: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 191 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0003 \| 1 \| 1.2455 \| \| No log \| 0.0065 \| 20 \| 1.2359 \| \| No log \| 0.0131 \| 40 \| 1.1506 \| \| No log \| 0.0196 \| 60 \| 1.0540 \| \| No log \| 0.0261 \| 80 \| 1.0263 \| \| 1.1432 \| 0.0326 \| 100 \| 1.0077 \| \| 1.1432 \| 0.0392 \| 120 \| 0.9978 \| \| 1.1432 \| 0.0457 \| 140 \| 0.9887 \| \| 1.1432 \| 0.0522 \| 160 \| 0.9763 \| \| 1.1432 \| 0.0588 \| 180 \| 0.9711 \| \| 1.0125 \| 0.0653 \| 200 \| 0.9689 \| \| 1.0125 \| 0.0718 \| 220 \| 1.0579 \| \| 1.0125 \| 0.0784 \| 240 \| 0.9813 \| \| 1.0125 \| 0.0849 \| 260 \| 0.9845 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1

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