Split (1)

train · 839k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
jebish7/LLAMA-8B-B80	jebish7	2025-01-24T18:21:26Z	17	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-01-24T18:14:51Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: jebish7 - 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)
Nexspear/5c4cedba-7e2b-4e9f-b4d8-c396b060b6cb	Nexspear	2025-01-24T18:20:21Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-360M", "base_model:adapter:unsloth/SmolLM-360M", "license:apache-2.0", "region:us" ]	null	2025-01-24T18:16:37Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-360M tags: - axolotl - generated_from_trainer model-index: - name: 5c4cedba-7e2b-4e9f-b4d8-c396b060b6cb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/SmolLM-360M bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - fd8c593d36d222e4_train_data.json ds_type: json format: custom path: /workspace/input_data/fd8c593d36d222e4_train_data.json type: field_input: tool_nodes field_instruction: instruction field_output: tool_steps 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: Nexspear/5c4cedba-7e2b-4e9f-b4d8-c396b060b6cb hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 5.0e-05 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/fd8c593d36d222e4_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: ba026e41-97ab-4c65-ab9a-114e413b7924 wandb_project: Gradients-On-Four wandb_run: your_name wandb_runid: ba026e41-97ab-4c65-ab9a-114e413b7924 warmup_steps: 10 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 5c4cedba-7e2b-4e9f-b4d8-c396b060b6cb This model is a fine-tuned version of [unsloth/SmolLM-360M](https://huggingface.co/unsloth/SmolLM-360M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4068 ## 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 - 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 \| 0.0046 \| 1 \| 2.0566 \| \| 2.0191 \| 0.0418 \| 9 \| 2.0483 \| \| 2.0478 \| 0.0835 \| 18 \| 1.9791 \| \| 1.9304 \| 0.1253 \| 27 \| 1.8460 \| \| 1.7329 \| 0.1671 \| 36 \| 1.7157 \| \| 1.7101 \| 0.2088 \| 45 \| 1.6132 \| \| 1.6279 \| 0.2506 \| 54 \| 1.5299 \| \| 1.5152 \| 0.2923 \| 63 \| 1.4712 \| \| 1.4687 \| 0.3341 \| 72 \| 1.4346 \| \| 1.4527 \| 0.3759 \| 81 \| 1.4155 \| \| 1.5095 \| 0.4176 \| 90 \| 1.4085 \| \| 1.3827 \| 0.4594 \| 99 \| 1.4068 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nttx/8f678b0d-a0fd-42ed-83d7-1c74caa244be	nttx	2025-01-24T18:19:36Z	7	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Hermes-3-Llama-3.1-8B", "base_model:adapter:unsloth/Hermes-3-Llama-3.1-8B", "region:us" ]	null	2025-01-24T17:46:07Z	--- library_name: peft base_model: unsloth/Hermes-3-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: 8f678b0d-a0fd-42ed-83d7-1c74caa244be 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/Hermes-3-Llama-3.1-8B bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 7498d1d10e472fec_train_data.json ds_type: json format: custom path: /workspace/input_data/7498d1d10e472fec_train_data.json type: field_instruction: title field_output: text format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: nttx/8f678b0d-a0fd-42ed-83d7-1c74caa244be 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/7498d1d10e472fec_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: 145b78ab-4197-45e4-b32d-0f5f5e9a56ab wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 145b78ab-4197-45e4-b32d-0f5f5e9a56ab warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 8f678b0d-a0fd-42ed-83d7-1c74caa244be This model is a fine-tuned version of [unsloth/Hermes-3-Llama-3.1-8B](https://huggingface.co/unsloth/Hermes-3-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4643 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.124 \| 0.0034 \| 1 \| 2.3156 \| \| 1.4484 \| 0.1718 \| 50 \| 1.6408 \| \| 1.1492 \| 0.3436 \| 100 \| 1.5502 \| \| 1.2565 \| 0.5155 \| 150 \| 1.4724 \| \| 1.3627 \| 0.6873 \| 200 \| 1.4643 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/MS-ManciousWriter-22B-v0.2-GGUF	mradermacher	2025-01-24T18:18:19Z	207	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:ThomasComics/MS-ManciousWriter-22B-v0.2", "base_model:quantized:ThomasComics/MS-ManciousWriter-22B-v0.2", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-13T20:03:45Z	--- base_model: ThomasComics/MS-ManciousWriter-22B-v0.2 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ThomasComics/MS-ManciousWriter-22B-v0.2 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MS-ManciousWriter-22B-v0.2-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/MS-ManciousWriter-22B-v0.2-GGUF/resolve/main/MS-ManciousWriter-22B-v0.2.Q2_K.gguf) \| Q2_K \| 8.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MS-ManciousWriter-22B-v0.2-GGUF/resolve/main/MS-ManciousWriter-22B-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 9.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MS-ManciousWriter-22B-v0.2-GGUF/resolve/main/MS-ManciousWriter-22B-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 10.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MS-ManciousWriter-22B-v0.2-GGUF/resolve/main/MS-ManciousWriter-22B-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 11.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MS-ManciousWriter-22B-v0.2-GGUF/resolve/main/MS-ManciousWriter-22B-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 12.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MS-ManciousWriter-22B-v0.2-GGUF/resolve/main/MS-ManciousWriter-22B-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 12.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MS-ManciousWriter-22B-v0.2-GGUF/resolve/main/MS-ManciousWriter-22B-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 13.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MS-ManciousWriter-22B-v0.2-GGUF/resolve/main/MS-ManciousWriter-22B-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 15.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MS-ManciousWriter-22B-v0.2-GGUF/resolve/main/MS-ManciousWriter-22B-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 15.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MS-ManciousWriter-22B-v0.2-GGUF/resolve/main/MS-ManciousWriter-22B-v0.2.Q6_K.gguf) \| Q6_K \| 18.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MS-ManciousWriter-22B-v0.2-GGUF/resolve/main/MS-ManciousWriter-22B-v0.2.Q8_0.gguf) \| Q8_0 \| 23.7 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF	mradermacher	2025-01-24T18:18:18Z	399	0	transformers	[ "transformers", "gguf", "en", "ko", "base_model:Changgil/K2S3-Mistral-7b-v1.3", "base_model:quantized:Changgil/K2S3-Mistral-7b-v1.3", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-01-24T17:20:05Z	--- base_model: Changgil/K2S3-Mistral-7b-v1.3 language: - en - ko library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Changgil/K2S3-Mistral-7b-v1.3 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-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/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.8 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-Q2_K.gguf) \| i1-Q2_K \| 2.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.7 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.3 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-Q4_1.gguf) \| i1-Q4_1 \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-i1-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.i1-Q6_K.gguf) \| i1-Q6_K \| 6.2 \| 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 -->
dimasik2987/ba57bca4-83be-4e04-b6a4-efbffe1e4839	dimasik2987	2025-01-24T18:18:09Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/tinyllama-chat", "base_model:adapter:unsloth/tinyllama-chat", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T18:16:45Z	--- library_name: peft license: apache-2.0 base_model: unsloth/tinyllama-chat tags: - axolotl - generated_from_trainer model-index: - name: ba57bca4-83be-4e04-b6a4-efbffe1e4839 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/tinyllama-chat bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 4f23bbe0afe56f5a_train_data.json ds_type: json format: custom path: /workspace/input_data/4f23bbe0afe56f5a_train_data.json type: field_input: '' field_instruction: content field_output: python format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: 1 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: true hub_model_id: dimasik2987/ba57bca4-83be-4e04-b6a4-efbffe1e4839 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 3 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 79GiB max_steps: 30 micro_batch_size: 4 mlflow_experiment_name: /tmp/4f23bbe0afe56f5a_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 4e637a71-9f6a-4732-9a25-972d5757d955 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 4e637a71-9f6a-4732-9a25-972d5757d955 warmup_steps: 5 weight_decay: 0.001 xformers_attention: true ``` </details><br> # ba57bca4-83be-4e04-b6a4-efbffe1e4839 This model is a fine-tuned version of [unsloth/tinyllama-chat](https://huggingface.co/unsloth/tinyllama-chat) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_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: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0078 \| 1 \| nan \| \| 0.0 \| 0.0390 \| 5 \| nan \| \| 0.0 \| 0.0780 \| 10 \| nan \| \| 0.0 \| 0.1170 \| 15 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
cvoffer/d3627e2f-6b7d-4088-9120-bf6ea9a4fb43	cvoffer	2025-01-24T18:17:45Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-360M", "base_model:adapter:unsloth/SmolLM-360M", "license:apache-2.0", "region:us" ]	null	2025-01-24T18:16:49Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-360M tags: - axolotl - generated_from_trainer model-index: - name: d3627e2f-6b7d-4088-9120-bf6ea9a4fb43 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/SmolLM-360M bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - fd8c593d36d222e4_train_data.json ds_type: json format: custom path: /workspace/input_data/fd8c593d36d222e4_train_data.json type: field_input: tool_nodes field_instruction: instruction field_output: tool_steps format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: 1 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: cvoffer/d3627e2f-6b7d-4088-9120-bf6ea9a4fb43 hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 78GiB max_steps: 30 micro_batch_size: 2 mlflow_experiment_name: /tmp/fd8c593d36d222e4_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: ba026e41-97ab-4c65-ab9a-114e413b7924 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: ba026e41-97ab-4c65-ab9a-114e413b7924 warmup_steps: 5 weight_decay: 0.001 xformers_attention: true ``` </details><br> # d3627e2f-6b7d-4088-9120-bf6ea9a4fb43 This model is a fine-tuned version of [unsloth/SmolLM-360M](https://huggingface.co/unsloth/SmolLM-360M) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0012 \| 1 \| nan \| \| 0.0 \| 0.0058 \| 5 \| nan \| \| 0.0 \| 0.0116 \| 10 \| nan \| \| 0.0 \| 0.0174 \| 15 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
prxy5606/b85a367b-a28f-4cc1-b61a-11b68c1095d1	prxy5606	2025-01-24T18:17:27Z	6	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:dltjdgh0928/test_instruction", "base_model:adapter:dltjdgh0928/test_instruction", "license:apache-2.0", "region:us" ]	null	2025-01-24T17:43:51Z	--- library_name: peft license: apache-2.0 base_model: dltjdgh0928/test_instruction tags: - axolotl - generated_from_trainer model-index: - name: b85a367b-a28f-4cc1-b61a-11b68c1095d1 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: dltjdgh0928/test_instruction bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 046705ccd51bb8b0_train_data.json ds_type: json format: custom path: /workspace/input_data/046705ccd51bb8b0_train_data.json type: field_instruction: title field_output: text format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: prxy5606/b85a367b-a28f-4cc1-b61a-11b68c1095d1 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/046705ccd51bb8b0_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: a2f542fe-1aa7-4f7b-a82c-b1cc81bf83c0 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: a2f542fe-1aa7-4f7b-a82c-b1cc81bf83c0 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # b85a367b-a28f-4cc1-b61a-11b68c1095d1 This model is a fine-tuned version of [dltjdgh0928/test_instruction](https://huggingface.co/dltjdgh0928/test_instruction) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5196 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 6.9298 \| 0.0114 \| 1 \| 1.8512 \| \| 6.0764 \| 0.5682 \| 50 \| 1.5800 \| \| 4.1832 \| 1.1364 \| 100 \| 1.5370 \| \| 3.8924 \| 1.7045 \| 150 \| 1.5123 \| \| 4.998 \| 2.2727 \| 200 \| 1.5196 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
denbeo/430d5843-facc-4b45-ad65-62f989a76c2c	denbeo	2025-01-24T18:17:12Z	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-Math-1.5B-Instruct", "base_model:adapter:unsloth/Qwen2.5-Math-1.5B-Instruct", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T18:03:23Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-Math-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 430d5843-facc-4b45-ad65-62f989a76c2c results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2.5-Math-1.5B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - d16d347b651ede3e_train_data.json ds_type: json format: custom path: /workspace/input_data/d16d347b651ede3e_train_data.json type: field_instruction: aspect_list field_output: caption_summary 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: denbeo/430d5843-facc-4b45-ad65-62f989a76c2c 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/d16d347b651ede3e_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: 2fe0c844-e98c-476d-b9a0-1a41beb91022 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 2fe0c844-e98c-476d-b9a0-1a41beb91022 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 430d5843-facc-4b45-ad65-62f989a76c2c This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.4341 \| 0.3166 \| 200 \| 2.9850 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/zephyr-7b-beta-ExPO-GGUF	mradermacher	2025-01-24T18:16:19Z	188	0	transformers	[ "transformers", "gguf", "en", "base_model:chujiezheng/zephyr-7b-beta-ExPO", "base_model:quantized:chujiezheng/zephyr-7b-beta-ExPO", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-24T17:16:44Z	--- base_model: chujiezheng/zephyr-7b-beta-ExPO language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/chujiezheng/zephyr-7b-beta-ExPO <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-ExPO-GGUF/resolve/main/zephyr-7b-beta-ExPO.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-ExPO-GGUF/resolve/main/zephyr-7b-beta-ExPO.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-ExPO-GGUF/resolve/main/zephyr-7b-beta-ExPO.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-ExPO-GGUF/resolve/main/zephyr-7b-beta-ExPO.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-ExPO-GGUF/resolve/main/zephyr-7b-beta-ExPO.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-ExPO-GGUF/resolve/main/zephyr-7b-beta-ExPO.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-ExPO-GGUF/resolve/main/zephyr-7b-beta-ExPO.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-ExPO-GGUF/resolve/main/zephyr-7b-beta-ExPO.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-ExPO-GGUF/resolve/main/zephyr-7b-beta-ExPO.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-ExPO-GGUF/resolve/main/zephyr-7b-beta-ExPO.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-ExPO-GGUF/resolve/main/zephyr-7b-beta-ExPO.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-beta-ExPO-GGUF/resolve/main/zephyr-7b-beta-ExPO.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
prxy5604/a14c6bf0-6904-416a-ba0f-fadae84368d2	prxy5604	2025-01-24T18:15:42Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:adapter:unsloth/Llama-3.2-1B-Instruct", "license:llama3.2", "region:us" ]	null	2025-01-24T18:13:17Z	--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-1B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: a14c6bf0-6904-416a-ba0f-fadae84368d2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Llama-3.2-1B-Instruct bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 0752bb947d5ed0d4_train_data.json ds_type: json format: custom path: /workspace/input_data/0752bb947d5ed0d4_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: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: prxy5604/a14c6bf0-6904-416a-ba0f-fadae84368d2 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/0752bb947d5ed0d4_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: 4a4c67e3-e39f-42b0-b2b7-14d11d56c788 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 4a4c67e3-e39f-42b0-b2b7-14d11d56c788 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # a14c6bf0-6904-416a-ba0f-fadae84368d2 This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4880 ## 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: 57 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7011 \| 0.0526 \| 1 \| 0.8363 \| \| 0.1892 \| 2.6316 \| 50 \| 0.4880 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nhung01/7c6d997e-dfea-4a94-a3f3-12d4a258dedf	nhung01	2025-01-24T18:15:35Z	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-Math-1.5B-Instruct", "base_model:adapter:unsloth/Qwen2.5-Math-1.5B-Instruct", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T18:03:21Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-Math-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 7c6d997e-dfea-4a94-a3f3-12d4a258dedf results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2.5-Math-1.5B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - d16d347b651ede3e_train_data.json ds_type: json format: custom path: /workspace/input_data/d16d347b651ede3e_train_data.json type: field_instruction: aspect_list field_output: caption_summary format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: nhung01/7c6d997e-dfea-4a94-a3f3-12d4a258dedf 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/d16d347b651ede3e_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: 2fe0c844-e98c-476d-b9a0-1a41beb91022 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 2fe0c844-e98c-476d-b9a0-1a41beb91022 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 7c6d997e-dfea-4a94-a3f3-12d4a258dedf This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9716 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.4118 \| 0.3166 \| 200 \| 2.9716 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/merging_LLM-i1-GGUF	mradermacher	2025-01-24T18:14:08Z	104	1	transformers	[ "transformers", "gguf", "en", "base_model:MatteoKhan/merging_LLM", "base_model:quantized:MatteoKhan/merging_LLM", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-01-03T04:20:54Z	--- base_model: MatteoKhan/merging_LLM language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/MatteoKhan/merging_LLM <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/merging_LLM-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/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.6 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-Q2_K.gguf) \| i1-Q2_K \| 0.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.9 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.9 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 1.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 1.0 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-Q4_0.gguf) \| i1-Q4_0 \| 1.0 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 1.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-Q4_1.gguf) \| i1-Q4_1 \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/merging_LLM-i1-GGUF/resolve/main/merging_LLM.i1-Q6_K.gguf) \| i1-Q6_K \| 1.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
kostiantynk1205/13c1957c-0d33-4b63-9bcb-363546ad53ce	kostiantynk1205	2025-01-24T18:14:07Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Meta-Llama-3.1-8B", "base_model:adapter:unsloth/Meta-Llama-3.1-8B", "license:llama3.1", "region:us" ]	null	2025-01-24T18:13:15Z	--- library_name: peft license: llama3.1 base_model: unsloth/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: 13c1957c-0d33-4b63-9bcb-363546ad53ce results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Meta-Llama-3.1-8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - b3d963a154e55444_train_data.json ds_type: json format: custom path: /workspace/input_data/b3d963a154e55444_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: kostiantynk1205/13c1957c-0d33-4b63-9bcb-363546ad53ce hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/b3d963a154e55444_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: 9f4dab1e-eaba-44fe-b5c4-9380877bfca8 wandb_project: Birthday-SN56-23-Gradients-On-Demand wandb_run: your_name wandb_runid: 9f4dab1e-eaba-44fe-b5c4-9380877bfca8 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 13c1957c-0d33-4b63-9bcb-363546ad53ce This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0039 \| 1 \| nan \| \| 0.0 \| 0.0117 \| 3 \| nan \| \| 0.0 \| 0.0234 \| 6 \| nan \| \| 0.0 \| 0.0351 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nhung03/c3e84a01-031e-4cca-9309-f97f7313b925	nhung03	2025-01-24T18:13:48Z	6	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-Math-1.5B-Instruct", "base_model:adapter:unsloth/Qwen2.5-Math-1.5B-Instruct", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T18:03:15Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-Math-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: c3e84a01-031e-4cca-9309-f97f7313b925 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2.5-Math-1.5B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - d16d347b651ede3e_train_data.json ds_type: json format: custom path: /workspace/input_data/d16d347b651ede3e_train_data.json type: field_instruction: aspect_list field_output: caption_summary 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: nhung03/c3e84a01-031e-4cca-9309-f97f7313b925 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/d16d347b651ede3e_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: 2fe0c844-e98c-476d-b9a0-1a41beb91022 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 2fe0c844-e98c-476d-b9a0-1a41beb91022 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # c3e84a01-031e-4cca-9309-f97f7313b925 This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9711 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.4131 \| 0.3166 \| 200 \| 2.9711 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
jebish7/LLAMA-8B-A90	jebish7	2025-01-24T18:13:28Z	16	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-01-24T18:06:45Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: jebish7 - 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)
lesso11/eff392b1-4dce-45b0-a857-3bf303fa0135	lesso11	2025-01-24T18:13:15Z	7	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:Artples/L-MChat-7b", "base_model:adapter:Artples/L-MChat-7b", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T17:56:29Z	--- library_name: peft license: apache-2.0 base_model: Artples/L-MChat-7b tags: - axolotl - generated_from_trainer model-index: - name: eff392b1-4dce-45b0-a857-3bf303fa0135 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: Artples/L-MChat-7b bf16: true chat_template: llama3 datasets: - data_files: - 8b1f8661a4895405_train_data.json ds_type: json format: custom path: /workspace/input_data/8b1f8661a4895405_train_data.json type: field_input: context field_instruction: question field_output: answer0 format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 2 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: lesso11/eff392b1-4dce-45b0-a857-3bf303fa0135 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 25 micro_batch_size: 2 mlflow_experiment_name: /tmp/8b1f8661a4895405_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 512 special_tokens: pad_token: <\|end_of_turn\|> 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: ed150454-ab51-4790-a095-bcdf93825195 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: ed150454-ab51-4790-a095-bcdf93825195 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # eff392b1-4dce-45b0-a857-3bf303fa0135 This model is a fine-tuned version of [Artples/L-MChat-7b](https://huggingface.co/Artples/L-MChat-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0002 \| 1 \| nan \| \| 0.0 \| 0.0012 \| 5 \| nan \| \| 0.0 \| 0.0024 \| 10 \| nan \| \| 0.0 \| 0.0036 \| 15 \| nan \| \| 0.0 \| 0.0048 \| 20 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
philip-hightech/aaf0fdd3-91a4-4681-b6b6-4618e724d754	philip-hightech	2025-01-24T18:12:53Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Meta-Llama-3.1-8B", "base_model:adapter:unsloth/Meta-Llama-3.1-8B", "license:llama3.1", "region:us" ]	null	2025-01-24T18:12:05Z	--- library_name: peft license: llama3.1 base_model: unsloth/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: aaf0fdd3-91a4-4681-b6b6-4618e724d754 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Meta-Llama-3.1-8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - b3d963a154e55444_train_data.json ds_type: json format: custom path: /workspace/input_data/b3d963a154e55444_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: philip-hightech/aaf0fdd3-91a4-4681-b6b6-4618e724d754 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/b3d963a154e55444_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: 9f4dab1e-eaba-44fe-b5c4-9380877bfca8 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 9f4dab1e-eaba-44fe-b5c4-9380877bfca8 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # aaf0fdd3-91a4-4681-b6b6-4618e724d754 This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0039 \| 1 \| nan \| \| 0.0 \| 0.0117 \| 3 \| nan \| \| 0.0 \| 0.0234 \| 6 \| nan \| \| 0.0 \| 0.0351 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
auxyus/00a30df6-d8ef-4616-a204-487ed973d74d	auxyus	2025-01-24T18:11:58Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:adapter:codellama/CodeLlama-7b-Instruct-hf", "license:llama2", "region:us" ]	null	2025-01-24T17:38:39Z	--- library_name: peft license: llama2 base_model: codellama/CodeLlama-7b-Instruct-hf tags: - axolotl - generated_from_trainer model-index: - name: 00a30df6-d8ef-4616-a204-487ed973d74d 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: codellama/CodeLlama-7b-Instruct-hf bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 4020ca7b1bee37ed_train_data.json ds_type: json format: custom path: /workspace/input_data/4020ca7b1bee37ed_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: 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: auxyus/00a30df6-d8ef-4616-a204-487ed973d74d 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/4020ca7b1bee37ed_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: </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: e0a64651-d34f-4f2d-afb3-8417c6fa5a6f wandb_project: Gradients-On-Two wandb_run: your_name wandb_runid: e0a64651-d34f-4f2d-afb3-8417c6fa5a6f warmup_steps: 10 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 00a30df6-d8ef-4616-a204-487ed973d74d This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3860 ## 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 \| 0.0032 \| 1 \| 0.5282 \| \| 0.5322 \| 0.0285 \| 9 \| 0.4899 \| \| 0.4742 \| 0.0569 \| 18 \| 0.4461 \| \| 0.4634 \| 0.0854 \| 27 \| 0.4228 \| \| 0.407 \| 0.1138 \| 36 \| 0.4101 \| \| 0.4063 \| 0.1423 \| 45 \| 0.4016 \| \| 0.3974 \| 0.1708 \| 54 \| 0.3953 \| \| 0.4175 \| 0.1992 \| 63 \| 0.3912 \| \| 0.4076 \| 0.2277 \| 72 \| 0.3885 \| \| 0.4166 \| 0.2561 \| 81 \| 0.3868 \| \| 0.3793 \| 0.2846 \| 90 \| 0.3861 \| \| 0.4073 \| 0.3130 \| 99 \| 0.3860 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
daniel40/435aad18-b929-46e2-b79d-f4fe374e52c2	daniel40	2025-01-24T18:11:56Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Llama-3.2-1B", "base_model:adapter:NousResearch/Llama-3.2-1B", "license:llama3.2", "region:us" ]	null	2025-01-24T18:11:12Z	--- library_name: peft license: llama3.2 base_model: NousResearch/Llama-3.2-1B tags: - axolotl - generated_from_trainer model-index: - name: 435aad18-b929-46e2-b79d-f4fe374e52c2 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/Llama-3.2-1B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 9864d3b317537d3a_train_data.json ds_type: json format: custom path: /workspace/input_data/9864d3b317537d3a_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: daniel40/435aad18-b929-46e2-b79d-f4fe374e52c2 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/9864d3b317537d3a_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 special_tokens: pad_token: <\|end_of_text\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 8a06a672-d9ee-4beb-9629-89383f6b4f03 wandb_project: Birthday-SN56-28-Gradients-On-Demand wandb_run: your_name wandb_runid: 8a06a672-d9ee-4beb-9629-89383f6b4f03 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 435aad18-b929-46e2-b79d-f4fe374e52c2 This model is a fine-tuned version of [NousResearch/Llama-3.2-1B](https://huggingface.co/NousResearch/Llama-3.2-1B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3801 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.7146 \| 0.0026 \| 1 \| 3.4024 \| \| 3.2969 \| 0.0077 \| 3 \| 3.4018 \| \| 3.4655 \| 0.0155 \| 6 \| 3.3946 \| \| 3.4146 \| 0.0232 \| 9 \| 3.3801 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/K2S3-Mistral-7b-v1.3-GGUF	mradermacher	2025-01-24T18:11:55Z	202	0	transformers	[ "transformers", "gguf", "en", "ko", "base_model:Changgil/K2S3-Mistral-7b-v1.3", "base_model:quantized:Changgil/K2S3-Mistral-7b-v1.3", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2025-01-24T17:08:56Z	--- base_model: Changgil/K2S3-Mistral-7b-v1.3 language: - en - ko library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/Changgil/K2S3-Mistral-7b-v1.3 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-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/K2S3-Mistral-7b-v1.3-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.Q2_K.gguf) \| Q2_K \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.Q3_K_M.gguf) \| Q3_K_M \| 3.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.Q3_K_L.gguf) \| Q3_K_L \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.IQ4_XS.gguf) \| IQ4_XS \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.Q4_K_S.gguf) \| Q4_K_S \| 4.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.Q4_K_M.gguf) \| Q4_K_M \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.Q5_K_S.gguf) \| Q5_K_S \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.Q5_K_M.gguf) \| Q5_K_M \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.Q6_K.gguf) \| Q6_K \| 6.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.Q8_0.gguf) \| Q8_0 \| 7.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/K2S3-Mistral-7b-v1.3-GGUF/resolve/main/K2S3-Mistral-7b-v1.3.f16.gguf) \| f16 \| 14.8 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
prxy5604/07f4e296-6c7a-4f44-a8fe-802ea4743427	prxy5604	2025-01-24T18:11:34Z	7	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B", "base_model:adapter:MLP-KTLim/llama-3-Korean-Bllossom-8B", "license:llama3", "region:us" ]	null	2025-01-24T17:24:21Z	--- library_name: peft license: llama3 base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B tags: - axolotl - generated_from_trainer model-index: - name: 07f4e296-6c7a-4f44-a8fe-802ea4743427 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: MLP-KTLim/llama-3-Korean-Bllossom-8B bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 1a9b97378fcbebe1_train_data.json ds_type: json format: custom path: /workspace/input_data/1a9b97378fcbebe1_train_data.json type: field_input: captions field_instruction: raw_sentences field_output: raw_anns 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: prxy5604/07f4e296-6c7a-4f44-a8fe-802ea4743427 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/1a9b97378fcbebe1_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: 24718b6c-9560-45d6-8f4f-62368e0b0d98 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 24718b6c-9560-45d6-8f4f-62368e0b0d98 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 07f4e296-6c7a-4f44-a8fe-802ea4743427 This model is a fine-tuned version of [MLP-KTLim/llama-3-Korean-Bllossom-8B](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3565 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.6272 \| 0.0007 \| 1 \| 2.0187 \| \| 1.3778 \| 0.0339 \| 50 \| 1.4875 \| \| 1.3254 \| 0.0679 \| 100 \| 1.4757 \| \| 1.3392 \| 0.1018 \| 150 \| 1.4507 \| \| 1.3384 \| 0.1358 \| 200 \| 1.3565 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
bbytxt/8bda8736-cbac-4fc7-b910-7471dca0e5ee	bbytxt	2025-01-24T18:11:33Z	7	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	2025-01-24T17:30:53Z	--- library_name: peft license: gemma base_model: unsloth/gemma-2-9b-it tags: - axolotl - generated_from_trainer model-index: - name: 8bda8736-cbac-4fc7-b910-7471dca0e5ee 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: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - b9e57da90d2a6f27_train_data.json ds_type: json format: custom path: /workspace/input_data/b9e57da90d2a6f27_train_data.json type: field_input: target_delexicalized field_instruction: dialog_act field_output: target 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: bbytxt/8bda8736-cbac-4fc7-b910-7471dca0e5ee 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/b9e57da90d2a6f27_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: 9064ddb9-e7cd-4c18-a087-a79d45c13b40 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 9064ddb9-e7cd-4c18-a087-a79d45c13b40 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 8bda8736-cbac-4fc7-b910-7471dca0e5ee 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: 0.0223 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.8234 \| 0.0052 \| 1 \| 2.0735 \| \| 0.1032 \| 0.2594 \| 50 \| 0.1576 \| \| 0.0255 \| 0.5188 \| 100 \| 0.0464 \| \| 0.0567 \| 0.7782 \| 150 \| 0.0258 \| \| 0.0331 \| 1.0376 \| 200 \| 0.0223 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ClarenceDan/16506faf-59e5-4b6f-8cf0-40e39faed635	ClarenceDan	2025-01-24T18:11:25Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "custom_code", "base_model:NousResearch/Yarn-Solar-10b-32k", "base_model:adapter:NousResearch/Yarn-Solar-10b-32k", "license:apache-2.0", "region:us" ]	null	2025-01-24T18:05:02Z	--- library_name: peft license: apache-2.0 base_model: NousResearch/Yarn-Solar-10b-32k tags: - axolotl - generated_from_trainer model-index: - name: 16506faf-59e5-4b6f-8cf0-40e39faed635 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/Yarn-Solar-10b-32k bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - ffdc9c6c7112acb8_train_data.json ds_type: json format: custom path: /workspace/input_data/ffdc9c6c7112acb8_train_data.json type: field_instruction: instruction field_output: original_instruction format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: ClarenceDan/16506faf-59e5-4b6f-8cf0-40e39faed635 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/ffdc9c6c7112acb8_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: 113bcfa4-1f77-4d9a-972a-d332c234c9bd wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 113bcfa4-1f77-4d9a-972a-d332c234c9bd warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 16506faf-59e5-4b6f-8cf0-40e39faed635 This model is a fine-tuned version of [NousResearch/Yarn-Solar-10b-32k](https://huggingface.co/NousResearch/Yarn-Solar-10b-32k) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0004 \| 1 \| nan \| \| 0.1712 \| 0.0012 \| 3 \| nan \| \| 0.1287 \| 0.0024 \| 6 \| nan \| \| 0.0 \| 0.0035 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nhunglaaaaaaa/3b7c50ad-88cf-4cfd-885d-d72e217d3ab9	nhunglaaaaaaa	2025-01-24T18:10:59Z	6	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-Math-1.5B-Instruct", "base_model:adapter:unsloth/Qwen2.5-Math-1.5B-Instruct", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T18:03:04Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-Math-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 3b7c50ad-88cf-4cfd-885d-d72e217d3ab9 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2.5-Math-1.5B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - d16d347b651ede3e_train_data.json ds_type: json format: custom path: /workspace/input_data/d16d347b651ede3e_train_data.json type: field_instruction: aspect_list field_output: caption_summary 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: nhunglaaaaaaa/3b7c50ad-88cf-4cfd-885d-d72e217d3ab9 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/d16d347b651ede3e_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: 2fe0c844-e98c-476d-b9a0-1a41beb91022 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 2fe0c844-e98c-476d-b9a0-1a41beb91022 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 3b7c50ad-88cf-4cfd-885d-d72e217d3ab9 This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9729 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.4263 \| 0.3166 \| 200 \| 2.9729 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
daniel40/0365d723-afa0-457e-8233-e6f75e9e4081	daniel40	2025-01-24T18:09:08Z	6	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-Math-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-Math-7B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-01-24T18:00:55Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-Math-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 0365d723-afa0-457e-8233-e6f75e9e4081 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-Math-7B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f8232566451d90d2_train_data.json ds_type: json format: custom path: /workspace/input_data/f8232566451d90d2_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: daniel40/0365d723-afa0-457e-8233-e6f75e9e4081 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/f8232566451d90d2_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: c2e82a22-8bc3-4a6a-8590-0acbfe70412e wandb_project: Birthday-SN56-27-Gradients-On-Demand wandb_run: your_name wandb_runid: c2e82a22-8bc3-4a6a-8590-0acbfe70412e warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 0365d723-afa0-457e-8233-e6f75e9e4081 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.6863 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 5.6732 \| 0.0001 \| 1 \| 4.9009 \| \| 3.1542 \| 0.0004 \| 3 \| 4.8998 \| \| 3.261 \| 0.0007 \| 6 \| 4.8769 \| \| 5.3195 \| 0.0011 \| 9 \| 4.6863 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
thakkkkkk/cecba95c-7b1a-4c91-a3a9-23cbce6a44bb	thakkkkkk	2025-01-24T18:08:25Z	6	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen1.5-0.5B-Chat", "base_model:adapter:Qwen/Qwen1.5-0.5B-Chat", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T17:32:33Z	--- library_name: peft license: other base_model: Qwen/Qwen1.5-0.5B-Chat tags: - axolotl - generated_from_trainer model-index: - name: cecba95c-7b1a-4c91-a3a9-23cbce6a44bb 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/Qwen1.5-0.5B-Chat bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 13b3bd6e856866ca_train_data.json ds_type: json format: custom path: /workspace/input_data/13b3bd6e856866ca_train_data.json type: field_instruction: code field_output: docstring 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: thakkkkkk/cecba95c-7b1a-4c91-a3a9-23cbce6a44bb 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: 4 mlflow_experiment_name: /tmp/13b3bd6e856866ca_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: 7a6bce7e-cd81-4427-be7c-1a87408232d9 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 7a6bce7e-cd81-4427-be7c-1a87408232d9 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # cecba95c-7b1a-4c91-a3a9-23cbce6a44bb This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0568 ## 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: 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0304 \| 0.0074 \| 200 \| 0.0568 \| ### 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/0218c922-f13e-45d7-b4c6-2a2ba57b4fbd	robiual-awal	2025-01-24T18:07:04Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B", "base_model:adapter:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B", "region:us" ]	null	2025-01-24T17:29:30Z	--- library_name: peft base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B tags: - axolotl - generated_from_trainer model-index: - name: 0218c922-f13e-45d7-b4c6-2a2ba57b4fbd 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: Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - a84d6d236b5e1544_train_data.json ds_type: json format: custom path: /workspace/input_data/a84d6d236b5e1544_train_data.json type: field_input: '' field_instruction: instruction field_output: response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: robiual-awal/0218c922-f13e-45d7-b4c6-2a2ba57b4fbd hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/a84d6d236b5e1544_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 special_tokens: pad_token: <\|eot_id\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: f64a5563-6776-4c7a-86c0-b07b3fcc4f06 wandb_project: Birthday-SN56-29-Gradients-On-Demand wandb_run: your_name wandb_runid: f64a5563-6776-4c7a-86c0-b07b3fcc4f06 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 0218c922-f13e-45d7-b4c6-2a2ba57b4fbd This model is a fine-tuned version of [Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5032 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.7169 \| 0.0000 \| 1 \| 1.6580 \| \| 1.7426 \| 0.0001 \| 3 \| 1.6544 \| \| 1.605 \| 0.0003 \| 6 \| 1.5993 \| \| 1.6672 \| 0.0004 \| 9 \| 1.5032 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
great0001/33fa53b9-24fc-40d4-8c55-1935e518bb3e	great0001	2025-01-24T18:06:49Z	6	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-1.5B-Instruct", "base_model:adapter:unsloth/Qwen2-1.5B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-01-24T17:23:47Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 33fa53b9-24fc-40d4-8c55-1935e518bb3e 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/Qwen2-1.5B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 27234bdaba19980e_train_data.json ds_type: json format: custom path: /workspace/input_data/27234bdaba19980e_train_data.json type: field_input: negative field_instruction: query field_output: positive format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: great0001/33fa53b9-24fc-40d4-8c55-1935e518bb3e hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/27234bdaba19980e_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: 71a2b5e9-3069-455b-8ee8-6e17d9046ca5 wandb_project: Birthday-SN56-14-Gradients-On-Demand wandb_run: your_name wandb_runid: 71a2b5e9-3069-455b-8ee8-6e17d9046ca5 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 33fa53b9-24fc-40d4-8c55-1935e518bb3e This model is a fine-tuned version of [unsloth/Qwen2-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0000 \| 1 \| nan \| \| 0.0 \| 0.0000 \| 3 \| nan \| \| 0.0 \| 0.0001 \| 6 \| nan \| \| 0.0 \| 0.0001 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
aleegis12/2d6d5d7e-299b-4078-89f5-911c83a79a8d	aleegis12	2025-01-24T18:04:45Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:adapter:codellama/CodeLlama-7b-Instruct-hf", "license:llama2", "region:us" ]	null	2025-01-24T17:20:21Z	--- library_name: peft license: llama2 base_model: codellama/CodeLlama-7b-Instruct-hf tags: - axolotl - generated_from_trainer model-index: - name: 2d6d5d7e-299b-4078-89f5-911c83a79a8d 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: codellama/CodeLlama-7b-Instruct-hf bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 0af14c27ef012868_train_data.json ds_type: json format: custom path: /workspace/input_data/0af14c27ef012868_train_data.json type: field_input: text field_instruction: subject field_output: title format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: aleegis12/2d6d5d7e-299b-4078-89f5-911c83a79a8d 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/0af14c27ef012868_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 19c8688e-ea72-45f4-ad76-056d1e3fe378 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 19c8688e-ea72-45f4-ad76-056d1e3fe378 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 2d6d5d7e-299b-4078-89f5-911c83a79a8d This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4102 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.5252 \| 0.0008 \| 1 \| 2.3886 \| \| 2.3783 \| 0.0377 \| 50 \| 1.5656 \| \| 2.1107 \| 0.0754 \| 100 \| 1.4745 \| \| 2.1932 \| 0.1130 \| 150 \| 1.4211 \| \| 2.2388 \| 0.1507 \| 200 \| 1.4102 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Muadil/Llama-3.2-1B-Instruct_sum_PPO_Skywork_40k_2_2ep	Muadil	2025-01-24T18:04:30Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-24T18:03:09Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ardaspear/3b5f9d72-bf72-4910-836a-1a1e05f322ac	ardaspear	2025-01-24T18:02:11Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Meta-Llama-3.1-8B", "base_model:adapter:unsloth/Meta-Llama-3.1-8B", "license:llama3.1", "region:us" ]	null	2025-01-24T17:47:27Z	--- library_name: peft license: llama3.1 base_model: unsloth/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: 3b5f9d72-bf72-4910-836a-1a1e05f322ac results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Meta-Llama-3.1-8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - b3d963a154e55444_train_data.json ds_type: json format: custom path: /workspace/input_data/b3d963a154e55444_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/3b5f9d72-bf72-4910-836a-1a1e05f322ac hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 5.0e-05 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/b3d963a154e55444_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: 9f4dab1e-eaba-44fe-b5c4-9380877bfca8 wandb_project: Gradients-On-Five wandb_run: your_name wandb_runid: 9f4dab1e-eaba-44fe-b5c4-9380877bfca8 warmup_steps: 10 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 3b5f9d72-bf72-4910-836a-1a1e05f322ac This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2025 ## 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 - 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 \| 0.0156 \| 1 \| 0.4046 \| \| 0.3598 \| 0.1401 \| 9 \| 0.3213 \| \| 0.2559 \| 0.2802 \| 18 \| 0.2223 \| \| 0.2385 \| 0.4202 \| 27 \| 0.2126 \| \| 0.226 \| 0.5603 \| 36 \| 0.2080 \| \| 0.2161 \| 0.7004 \| 45 \| 0.2060 \| \| 0.211 \| 0.8405 \| 54 \| 0.2043 \| \| 0.2024 \| 0.9805 \| 63 \| 0.2036 \| \| 0.196 \| 1.1206 \| 72 \| 0.2036 \| \| 0.2201 \| 1.2607 \| 81 \| 0.2032 \| \| 0.1996 \| 1.4008 \| 90 \| 0.2029 \| \| 0.1834 \| 1.5409 \| 99 \| 0.2025 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
thaffggg/e5c1a89b-434e-432c-a7a4-53e356531f2e	thaffggg	2025-01-24T18:00:43Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:adapter:codellama/CodeLlama-7b-Instruct-hf", "license:llama2", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T17:39:57Z	--- library_name: peft license: llama2 base_model: codellama/CodeLlama-7b-Instruct-hf tags: - axolotl - generated_from_trainer model-index: - name: e5c1a89b-434e-432c-a7a4-53e356531f2e 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: codellama/CodeLlama-7b-Instruct-hf bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 4020ca7b1bee37ed_train_data.json ds_type: json format: custom path: /workspace/input_data/4020ca7b1bee37ed_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: thaffggg/e5c1a89b-434e-432c-a7a4-53e356531f2e 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/4020ca7b1bee37ed_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: e0a64651-d34f-4f2d-afb3-8417c6fa5a6f wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: e0a64651-d34f-4f2d-afb3-8417c6fa5a6f warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # e5c1a89b-434e-432c-a7a4-53e356531f2e This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4038 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.4325 \| 0.1582 \| 200 \| 0.4038 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
xotopo/atharv1500	xotopo	2025-01-24T18:00:16Z	88	1	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-01-24T17:59:28Z	--- 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: ATHARV --- # ATHARV Abtest <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `ATHARV` 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('xotopo/atharv1500', weight_name='aqV2XR2nv3X_pwJzvMKLo_pytorch_lora_weights.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)
aleegis09/86a3bfc2-a528-402d-99c2-10327af35449	aleegis09	2025-01-24T17:59:03Z	6	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	2025-01-24T17:40:16Z	--- library_name: peft license: mit base_model: microsoft/phi-2 tags: - axolotl - generated_from_trainer model-index: - name: 86a3bfc2-a528-402d-99c2-10327af35449 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: 16 dataset_prepared_path: null datasets: - data_files: - ed3dcd46464f4dad_train_data.json ds_type: json format: custom path: /workspace/input_data/ed3dcd46464f4dad_train_data.json type: field_instruction: sentence1 field_output: sentence2 format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: aleegis09/86a3bfc2-a528-402d-99c2-10327af35449 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/ed3dcd46464f4dad_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: be20a502-f469-4b17-bfde-5de1864163d7 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: be20a502-f469-4b17-bfde-5de1864163d7 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 86a3bfc2-a528-402d-99c2-10327af35449 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: 1.7021 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.4197 \| 0.0065 \| 1 \| 3.2832 \| \| 2.0381 \| 0.3273 \| 50 \| 2.0826 \| \| 2.0398 \| 0.6547 \| 100 \| 1.8337 \| \| 1.5162 \| 0.9820 \| 150 \| 1.7148 \| \| 1.6728 \| 1.3093 \| 200 \| 1.7021 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
kenzykhaled/model	kenzykhaled	2025-01-24T17:56:24Z	198	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-01-24T17:51:58Z	--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: kenzykhaled - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
fpadovani/english_wikipedia_mlm_sent_30	fpadovani	2025-01-24T17:55:50Z	5	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-01-24T15:43:15Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: wikipedia_mlm_unmasking_sent_30 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. --> # wikipedia_mlm_unmasking_sent_30 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.3443 ## 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: 16 - eval_batch_size: 16 - seed: 30 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100000 - training_steps: 400000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:-----:\|:---------------:\| \| No log \| 0.3197 \| 2000 \| 7.0282 \| \| 7.4367 \| 0.6393 \| 4000 \| 6.8433 \| \| 7.4367 \| 0.9590 \| 6000 \| 6.7744 \| \| 6.7925 \| 1.2787 \| 8000 \| 6.7245 \| \| 6.7925 \| 1.5983 \| 10000 \| 6.6008 \| \| 6.5825 \| 1.9180 \| 12000 \| 6.2640 \| \| 6.5825 \| 2.2377 \| 14000 \| 5.9391 \| \| 5.9941 \| 2.5573 \| 16000 \| 5.6755 \| \| 5.9941 \| 2.8772 \| 18000 \| 5.5099 \| \| 5.5947 \| 3.1968 \| 20000 \| 5.3885 \| \| 5.5947 \| 3.5165 \| 22000 \| 5.2905 \| \| 5.3485 \| 3.8362 \| 24000 \| 5.1960 \| \| 5.3485 \| 4.1558 \| 26000 \| 5.0899 \| \| 5.1427 \| 4.4755 \| 28000 \| 5.0341 \| \| 5.1427 \| 4.7952 \| 30000 \| 4.9389 \| \| 5.0044 \| 5.1148 \| 32000 \| 4.8858 \| \| 5.0044 \| 5.4345 \| 34000 \| 4.8288 \| \| 4.8822 \| 5.7542 \| 36000 \| 4.7583 \| \| 4.8822 \| 6.0738 \| 38000 \| 4.7035 \| \| 4.7849 \| 6.3935 \| 40000 \| 4.6815 \| \| 4.7849 \| 6.7132 \| 42000 \| 4.6535 \| \| 4.719 \| 7.0328 \| 44000 \| 4.6088 \| \| 4.719 \| 7.3525 \| 46000 \| 4.5825 \| \| 4.6493 \| 7.6722 \| 48000 \| 4.5544 \| \| 4.6493 \| 7.9918 \| 50000 \| 4.5219 \| \| 4.6051 \| 8.3115 \| 52000 \| 4.5178 \| \| 4.6051 \| 8.6312 \| 54000 \| 4.4834 \| \| 4.5757 \| 8.9509 \| 56000 \| 4.4680 \| \| 4.5757 \| 9.2705 \| 58000 \| 4.4645 \| \| 4.5431 \| 9.5902 \| 60000 \| 4.4370 \| \| 4.5431 \| 9.9099 \| 62000 \| 4.4255 \| \| 4.5314 \| 10.2295 \| 64000 \| 4.4249 \| \| 4.5314 \| 10.5492 \| 66000 \| 4.4295 \| \| 4.5195 \| 10.8689 \| 68000 \| 4.4201 \| \| 4.5195 \| 11.1885 \| 70000 \| 4.4102 \| \| 4.504 \| 11.5082 \| 72000 \| 4.3990 \| \| 4.504 \| 11.8279 \| 74000 \| 4.3719 \| \| 4.4955 \| 12.1475 \| 76000 \| 4.3765 \| \| 4.4955 \| 12.4672 \| 78000 \| 4.3538 \| \| 4.4809 \| 12.7869 \| 80000 \| 4.3353 \| \| 4.4809 \| 13.1065 \| 82000 \| 4.3930 \| \| 4.4757 \| 13.4262 \| 84000 \| 4.3629 \| \| 4.4757 \| 13.7459 \| 86000 \| 4.3443 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.5.1+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Melo1512/vit-msn-small-beta-fia-manually-enhanced_test_2	Melo1512	2025-01-24T17:55:14Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit_msn", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/vit-msn-small", "base_model:finetune:facebook/vit-msn-small", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-01-24T17:33:06Z	--- library_name: transformers license: apache-2.0 base_model: facebook/vit-msn-small tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-msn-small-beta-fia-manually-enhanced_test_2 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.7746478873239436 --- <!-- 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. --> # vit-msn-small-beta-fia-manually-enhanced_test_2 This model is a fine-tuned version of [facebook/vit-msn-small](https://huggingface.co/facebook/vit-msn-small) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5203 - Accuracy: 0.7746 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:--------:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.5714 \| 1 \| 0.6037 \| 0.7465 \| \| No log \| 1.7143 \| 3 \| 0.6071 \| 0.7324 \| \| No log \| 2.8571 \| 5 \| 0.6120 \| 0.7183 \| \| No log \| 4.0 \| 7 \| 0.6188 \| 0.7183 \| \| No log \| 4.5714 \| 8 \| 0.6206 \| 0.7183 \| \| 0.4866 \| 5.7143 \| 10 \| 0.6272 \| 0.6972 \| \| 0.4866 \| 6.8571 \| 12 \| 0.6355 \| 0.6901 \| \| 0.4866 \| 8.0 \| 14 \| 0.6399 \| 0.6901 \| \| 0.4866 \| 8.5714 \| 15 \| 0.6364 \| 0.6831 \| \| 0.4866 \| 9.7143 \| 17 \| 0.6295 \| 0.6831 \| \| 0.4866 \| 10.8571 \| 19 \| 0.6288 \| 0.6901 \| \| 0.4519 \| 12.0 \| 21 \| 0.6185 \| 0.6901 \| \| 0.4519 \| 12.5714 \| 22 \| 0.6159 \| 0.6901 \| \| 0.4519 \| 13.7143 \| 24 \| 0.6113 \| 0.6972 \| \| 0.4519 \| 14.8571 \| 26 \| 0.5987 \| 0.6901 \| \| 0.4519 \| 16.0 \| 28 \| 0.6017 \| 0.6972 \| \| 0.4519 \| 16.5714 \| 29 \| 0.6067 \| 0.6972 \| \| 0.437 \| 17.7143 \| 31 \| 0.6062 \| 0.6620 \| \| 0.437 \| 18.8571 \| 33 \| 0.5966 \| 0.6901 \| \| 0.437 \| 20.0 \| 35 \| 0.5858 \| 0.7113 \| \| 0.437 \| 20.5714 \| 36 \| 0.5889 \| 0.7042 \| \| 0.437 \| 21.7143 \| 38 \| 0.5768 \| 0.7183 \| \| 0.4353 \| 22.8571 \| 40 \| 0.5752 \| 0.7183 \| \| 0.4353 \| 24.0 \| 42 \| 0.5729 \| 0.7183 \| \| 0.4353 \| 24.5714 \| 43 \| 0.5909 \| 0.6972 \| \| 0.4353 \| 25.7143 \| 45 \| 0.6038 \| 0.6761 \| \| 0.4353 \| 26.8571 \| 47 \| 0.5904 \| 0.6901 \| \| 0.4353 \| 28.0 \| 49 \| 0.5847 \| 0.6831 \| \| 0.4141 \| 28.5714 \| 50 \| 0.5615 \| 0.7113 \| \| 0.4141 \| 29.7143 \| 52 \| 0.5544 \| 0.7254 \| \| 0.4141 \| 30.8571 \| 54 \| 0.5904 \| 0.6690 \| \| 0.4141 \| 32.0 \| 56 \| 0.5948 \| 0.6831 \| \| 0.4141 \| 32.5714 \| 57 \| 0.5800 \| 0.6972 \| \| 0.4141 \| 33.7143 \| 59 \| 0.5902 \| 0.6972 \| \| 0.4066 \| 34.8571 \| 61 \| 0.5950 \| 0.6690 \| \| 0.4066 \| 36.0 \| 63 \| 0.5500 \| 0.7324 \| \| 0.4066 \| 36.5714 \| 64 \| 0.5470 \| 0.7324 \| \| 0.4066 \| 37.7143 \| 66 \| 0.5859 \| 0.6901 \| \| 0.4066 \| 38.8571 \| 68 \| 0.5955 \| 0.6831 \| \| 0.3827 \| 40.0 \| 70 \| 0.5967 \| 0.6761 \| \| 0.3827 \| 40.5714 \| 71 \| 0.5809 \| 0.6901 \| \| 0.3827 \| 41.7143 \| 73 \| 0.5721 \| 0.6972 \| \| 0.3827 \| 42.8571 \| 75 \| 0.6019 \| 0.6831 \| \| 0.3827 \| 44.0 \| 77 \| 0.6071 \| 0.6901 \| \| 0.3827 \| 44.5714 \| 78 \| 0.5962 \| 0.6972 \| \| 0.37 \| 45.7143 \| 80 \| 0.6114 \| 0.6831 \| \| 0.37 \| 46.8571 \| 82 \| 0.5594 \| 0.7183 \| \| 0.37 \| 48.0 \| 84 \| 0.5493 \| 0.7324 \| \| 0.37 \| 48.5714 \| 85 \| 0.5744 \| 0.7113 \| \| 0.37 \| 49.7143 \| 87 \| 0.5443 \| 0.7183 \| \| 0.37 \| 50.8571 \| 89 \| 0.5469 \| 0.7324 \| \| 0.3797 \| 52.0 \| 91 \| 0.6003 \| 0.6831 \| \| 0.3797 \| 52.5714 \| 92 \| 0.6048 \| 0.6901 \| \| 0.3797 \| 53.7143 \| 94 \| 0.5203 \| 0.7746 \| \| 0.3797 \| 54.8571 \| 96 \| 0.5327 \| 0.7535 \| \| 0.3797 \| 56.0 \| 98 \| 0.6414 \| 0.6338 \| \| 0.3797 \| 56.5714 \| 99 \| 0.6562 \| 0.6197 \| \| 0.3715 \| 57.7143 \| 101 \| 0.5754 \| 0.7183 \| \| 0.3715 \| 58.8571 \| 103 \| 0.5672 \| 0.7254 \| \| 0.3715 \| 60.0 \| 105 \| 0.6060 \| 0.6901 \| \| 0.3715 \| 60.5714 \| 106 \| 0.6536 \| 0.6197 \| \| 0.3715 \| 61.7143 \| 108 \| 0.6177 \| 0.6479 \| \| 0.3483 \| 62.8571 \| 110 \| 0.5385 \| 0.7535 \| \| 0.3483 \| 64.0 \| 112 \| 0.5630 \| 0.7394 \| \| 0.3483 \| 64.5714 \| 113 \| 0.5818 \| 0.7254 \| \| 0.3483 \| 65.7143 \| 115 \| 0.6055 \| 0.6972 \| \| 0.3483 \| 66.8571 \| 117 \| 0.5737 \| 0.7324 \| \| 0.3483 \| 68.0 \| 119 \| 0.5606 \| 0.7394 \| \| 0.3667 \| 68.5714 \| 120 \| 0.5829 \| 0.7183 \| \| 0.3667 \| 69.7143 \| 122 \| 0.5931 \| 0.7113 \| \| 0.3667 \| 70.8571 \| 124 \| 0.5375 \| 0.7606 \| \| 0.3667 \| 72.0 \| 126 \| 0.5797 \| 0.7113 \| \| 0.3667 \| 72.5714 \| 127 \| 0.6182 \| 0.6690 \| \| 0.3667 \| 73.7143 \| 129 \| 0.6497 \| 0.6690 \| \| 0.3357 \| 74.8571 \| 131 \| 0.6432 \| 0.6831 \| \| 0.3357 \| 76.0 \| 133 \| 0.6772 \| 0.6620 \| \| 0.3357 \| 76.5714 \| 134 \| 0.6395 \| 0.6479 \| \| 0.3357 \| 77.7143 \| 136 \| 0.5895 \| 0.7042 \| \| 0.3357 \| 78.8571 \| 138 \| 0.5921 \| 0.6972 \| \| 0.3415 \| 80.0 \| 140 \| 0.5618 \| 0.7254 \| \| 0.3415 \| 80.5714 \| 141 \| 0.5697 \| 0.7183 \| \| 0.3415 \| 81.7143 \| 143 \| 0.6535 \| 0.6197 \| \| 0.3415 \| 82.8571 \| 145 \| 0.6627 \| 0.6338 \| \| 0.3415 \| 84.0 \| 147 \| 0.6194 \| 0.6761 \| \| 0.3415 \| 84.5714 \| 148 \| 0.6301 \| 0.6901 \| \| 0.3296 \| 85.7143 \| 150 \| 0.6436 \| 0.6690 \| \| 0.3296 \| 86.8571 \| 152 \| 0.6348 \| 0.6831 \| \| 0.3296 \| 88.0 \| 154 \| 0.6704 \| 0.6479 \| \| 0.3296 \| 88.5714 \| 155 \| 0.7190 \| 0.6338 \| \| 0.3296 \| 89.7143 \| 157 \| 0.7064 \| 0.6338 \| \| 0.3296 \| 90.8571 \| 159 \| 0.6291 \| 0.6549 \| \| 0.3296 \| 92.0 \| 161 \| 0.6933 \| 0.6197 \| \| 0.3296 \| 92.5714 \| 162 \| 0.7115 \| 0.6197 \| \| 0.3296 \| 93.7143 \| 164 \| 0.6229 \| 0.6690 \| \| 0.3296 \| 94.8571 \| 166 \| 0.5727 \| 0.7183 \| \| 0.3296 \| 96.0 \| 168 \| 0.5965 \| 0.6901 \| \| 0.3296 \| 96.5714 \| 169 \| 0.6433 \| 0.6690 \| \| 0.3174 \| 97.7143 \| 171 \| 0.6634 \| 0.6408 \| \| 0.3174 \| 98.8571 \| 173 \| 0.6166 \| 0.6549 \| \| 0.3174 \| 100.0 \| 175 \| 0.5896 \| 0.6972 \| \| 0.3174 \| 100.5714 \| 176 \| 0.6092 \| 0.6549 \| \| 0.3174 \| 101.7143 \| 178 \| 0.6022 \| 0.6549 \| \| 0.3309 \| 102.8571 \| 180 \| 0.5928 \| 0.6761 \| \| 0.3309 \| 104.0 \| 182 \| 0.6327 \| 0.6408 \| \| 0.3309 \| 104.5714 \| 183 \| 0.6490 \| 0.6338 \| \| 0.3309 \| 105.7143 \| 185 \| 0.6155 \| 0.6479 \| \| 0.3309 \| 106.8571 \| 187 \| 0.6225 \| 0.6620 \| \| 0.3309 \| 108.0 \| 189 \| 0.6732 \| 0.6408 \| \| 0.3124 \| 108.5714 \| 190 \| 0.6808 \| 0.6408 \| \| 0.3124 \| 109.7143 \| 192 \| 0.6585 \| 0.6479 \| \| 0.3124 \| 110.8571 \| 194 \| 0.6122 \| 0.6761 \| \| 0.3124 \| 112.0 \| 196 \| 0.6510 \| 0.6549 \| \| 0.3124 \| 112.5714 \| 197 \| 0.7099 \| 0.6408 \| \| 0.3124 \| 113.7143 \| 199 \| 0.7192 \| 0.6338 \| \| 0.3158 \| 114.8571 \| 201 \| 0.6186 \| 0.6901 \| \| 0.3158 \| 116.0 \| 203 \| 0.6071 \| 0.7042 \| \| 0.3158 \| 116.5714 \| 204 \| 0.6419 \| 0.6831 \| \| 0.3158 \| 117.7143 \| 206 \| 0.6679 \| 0.6549 \| \| 0.3158 \| 118.8571 \| 208 \| 0.6825 \| 0.6268 \| \| 0.3026 \| 120.0 \| 210 \| 0.6091 \| 0.6972 \| \| 0.3026 \| 120.5714 \| 211 \| 0.5861 \| 0.7394 \| \| 0.3026 \| 121.7143 \| 213 \| 0.6037 \| 0.7113 \| \| 0.3026 \| 122.8571 \| 215 \| 0.6315 \| 0.6761 \| \| 0.3026 \| 124.0 \| 217 \| 0.6328 \| 0.6690 \| \| 0.3026 \| 124.5714 \| 218 \| 0.6187 \| 0.6831 \| \| 0.2968 \| 125.7143 \| 220 \| 0.5843 \| 0.7394 \| \| 0.2968 \| 126.8571 \| 222 \| 0.6126 \| 0.7042 \| \| 0.2968 \| 128.0 \| 224 \| 0.6785 \| 0.6549 \| \| 0.2968 \| 128.5714 \| 225 \| 0.6706 \| 0.6479 \| \| 0.2968 \| 129.7143 \| 227 \| 0.6070 \| 0.7113 \| \| 0.2968 \| 130.8571 \| 229 \| 0.5984 \| 0.7254 \| \| 0.294 \| 132.0 \| 231 \| 0.6533 \| 0.6620 \| \| 0.294 \| 132.5714 \| 232 \| 0.6802 \| 0.6408 \| \| 0.294 \| 133.7143 \| 234 \| 0.6804 \| 0.6408 \| \| 0.294 \| 134.8571 \| 236 \| 0.6228 \| 0.7042 \| \| 0.294 \| 136.0 \| 238 \| 0.5849 \| 0.7676 \| \| 0.294 \| 136.5714 \| 239 \| 0.5874 \| 0.7676 \| \| 0.3009 \| 137.7143 \| 241 \| 0.6230 \| 0.7042 \| \| 0.3009 \| 138.8571 \| 243 \| 0.6641 \| 0.6549 \| \| 0.3009 \| 140.0 \| 245 \| 0.6435 \| 0.6972 \| \| 0.3009 \| 140.5714 \| 246 \| 0.6134 \| 0.7254 \| \| 0.3009 \| 141.7143 \| 248 \| 0.6063 \| 0.7394 \| \| 0.2873 \| 142.8571 \| 250 \| 0.6347 \| 0.6972 \| \| 0.2873 \| 144.0 \| 252 \| 0.6992 \| 0.6690 \| \| 0.2873 \| 144.5714 \| 253 \| 0.7137 \| 0.6408 \| \| 0.2873 \| 145.7143 \| 255 \| 0.6738 \| 0.6690 \| \| 0.2873 \| 146.8571 \| 257 \| 0.6321 \| 0.7113 \| \| 0.2873 \| 148.0 \| 259 \| 0.6135 \| 0.7183 \| \| 0.2821 \| 148.5714 \| 260 \| 0.6195 \| 0.7113 \| \| 0.2821 \| 149.7143 \| 262 \| 0.6544 \| 0.6761 \| \| 0.2821 \| 150.8571 \| 264 \| 0.6464 \| 0.6831 \| \| 0.2821 \| 152.0 \| 266 \| 0.6087 \| 0.7324 \| \| 0.2821 \| 152.5714 \| 267 \| 0.6000 \| 0.7394 \| \| 0.2821 \| 153.7143 \| 269 \| 0.6170 \| 0.7113 \| \| 0.3017 \| 154.8571 \| 271 \| 0.6674 \| 0.6831 \| \| 0.3017 \| 156.0 \| 273 \| 0.7137 \| 0.6338 \| \| 0.3017 \| 156.5714 \| 274 \| 0.7014 \| 0.6479 \| \| 0.3017 \| 157.7143 \| 276 \| 0.6091 \| 0.7254 \| \| 0.3017 \| 158.8571 \| 278 \| 0.5626 \| 0.7676 \| \| 0.2857 \| 160.0 \| 280 \| 0.5685 \| 0.7606 \| \| 0.2857 \| 160.5714 \| 281 \| 0.5941 \| 0.7113 \| \| 0.2857 \| 161.7143 \| 283 \| 0.6219 \| 0.7113 \| \| 0.2857 \| 162.8571 \| 285 \| 0.6283 \| 0.7113 \| \| 0.2857 \| 164.0 \| 287 \| 0.6314 \| 0.7042 \| \| 0.2857 \| 164.5714 \| 288 \| 0.6369 \| 0.6972 \| \| 0.2819 \| 165.7143 \| 290 \| 0.6446 \| 0.6972 \| \| 0.2819 \| 166.8571 \| 292 \| 0.6541 \| 0.6901 \| \| 0.2819 \| 168.0 \| 294 \| 0.6286 \| 0.7183 \| \| 0.2819 \| 168.5714 \| 295 \| 0.6064 \| 0.7183 \| \| 0.2819 \| 169.7143 \| 297 \| 0.5995 \| 0.7254 \| \| 0.2819 \| 170.8571 \| 299 \| 0.6431 \| 0.7254 \| \| 0.2744 \| 172.0 \| 301 \| 0.6797 \| 0.6901 \| \| 0.2744 \| 172.5714 \| 302 \| 0.6716 \| 0.6972 \| \| 0.2744 \| 173.7143 \| 304 \| 0.6510 \| 0.7254 \| \| 0.2744 \| 174.8571 \| 306 \| 0.6362 \| 0.7465 \| \| 0.2744 \| 176.0 \| 308 \| 0.6158 \| 0.7606 \| \| 0.2744 \| 176.5714 \| 309 \| 0.6099 \| 0.7676 \| \| 0.2867 \| 177.7143 \| 311 \| 0.6112 \| 0.7535 \| \| 0.2867 \| 178.8571 \| 313 \| 0.6035 \| 0.7465 \| \| 0.2867 \| 180.0 \| 315 \| 0.5816 \| 0.7676 \| \| 0.2867 \| 180.5714 \| 316 \| 0.5818 \| 0.7676 \| \| 0.2867 \| 181.7143 \| 318 \| 0.6078 \| 0.7676 \| \| 0.2883 \| 182.8571 \| 320 \| 0.6083 \| 0.7535 \| \| 0.2883 \| 184.0 \| 322 \| 0.5928 \| 0.7465 \| \| 0.2883 \| 184.5714 \| 323 \| 0.5862 \| 0.7535 \| \| 0.2883 \| 185.7143 \| 325 \| 0.5625 \| 0.7676 \| \| 0.2883 \| 186.8571 \| 327 \| 0.5580 \| 0.7817 \| \| 0.2883 \| 188.0 \| 329 \| 0.5945 \| 0.7535 \| \| 0.2852 \| 188.5714 \| 330 \| 0.6321 \| 0.6972 \| \| 0.2852 \| 189.7143 \| 332 \| 0.6650 \| 0.6620 \| \| 0.2852 \| 190.8571 \| 334 \| 0.6612 \| 0.6690 \| \| 0.2852 \| 192.0 \| 336 \| 0.6455 \| 0.6761 \| \| 0.2852 \| 192.5714 \| 337 \| 0.6290 \| 0.7113 \| \| 0.2852 \| 193.7143 \| 339 \| 0.6036 \| 0.7394 \| \| 0.2941 \| 194.8571 \| 341 \| 0.5879 \| 0.7535 \| \| 0.2941 \| 196.0 \| 343 \| 0.6135 \| 0.7254 \| \| 0.2941 \| 196.5714 \| 344 \| 0.6295 \| 0.7113 \| \| 0.2941 \| 197.7143 \| 346 \| 0.6445 \| 0.6831 \| \| 0.2941 \| 198.8571 \| 348 \| 0.6591 \| 0.6690 \| \| 0.2692 \| 200.0 \| 350 \| 0.6557 \| 0.6831 \| \| 0.2692 \| 200.5714 \| 351 \| 0.6485 \| 0.7113 \| \| 0.2692 \| 201.7143 \| 353 \| 0.6520 \| 0.7183 \| \| 0.2692 \| 202.8571 \| 355 \| 0.6673 \| 0.7113 \| \| 0.2692 \| 204.0 \| 357 \| 0.6814 \| 0.7183 \| \| 0.2692 \| 204.5714 \| 358 \| 0.6694 \| 0.7113 \| \| 0.2666 \| 205.7143 \| 360 \| 0.6350 \| 0.7254 \| \| 0.2666 \| 206.8571 \| 362 \| 0.6091 \| 0.7465 \| \| 0.2666 \| 208.0 \| 364 \| 0.6222 \| 0.7394 \| \| 0.2666 \| 208.5714 \| 365 \| 0.6363 \| 0.7394 \| \| 0.2666 \| 209.7143 \| 367 \| 0.6398 \| 0.7394 \| \| 0.2666 \| 210.8571 \| 369 \| 0.6555 \| 0.7254 \| \| 0.2745 \| 212.0 \| 371 \| 0.6555 \| 0.7254 \| \| 0.2745 \| 212.5714 \| 372 \| 0.6467 \| 0.7394 \| \| 0.2745 \| 213.7143 \| 374 \| 0.6216 \| 0.7606 \| \| 0.2745 \| 214.8571 \| 376 \| 0.6066 \| 0.7676 \| \| 0.2745 \| 216.0 \| 378 \| 0.6083 \| 0.7606 \| \| 0.2745 \| 216.5714 \| 379 \| 0.6152 \| 0.7535 \| \| 0.2578 \| 217.7143 \| 381 \| 0.6162 \| 0.7535 \| \| 0.2578 \| 218.8571 \| 383 \| 0.6097 \| 0.7535 \| \| 0.2578 \| 220.0 \| 385 \| 0.6003 \| 0.7465 \| \| 0.2578 \| 220.5714 \| 386 \| 0.6064 \| 0.7535 \| \| 0.2578 \| 221.7143 \| 388 \| 0.6182 \| 0.7535 \| \| 0.2637 \| 222.8571 \| 390 \| 0.6465 \| 0.7465 \| \| 0.2637 \| 224.0 \| 392 \| 0.6461 \| 0.7535 \| \| 0.2637 \| 224.5714 \| 393 \| 0.6352 \| 0.7535 \| \| 0.2637 \| 225.7143 \| 395 \| 0.6018 \| 0.7606 \| \| 0.2637 \| 226.8571 \| 397 \| 0.5855 \| 0.7746 \| \| 0.2637 \| 228.0 \| 399 \| 0.5916 \| 0.7606 \| \| 0.2696 \| 228.5714 \| 400 \| 0.6031 \| 0.7606 \| \| 0.2696 \| 229.7143 \| 402 \| 0.6308 \| 0.7606 \| \| 0.2696 \| 230.8571 \| 404 \| 0.6435 \| 0.7465 \| \| 0.2696 \| 232.0 \| 406 \| 0.6325 \| 0.7465 \| \| 0.2696 \| 232.5714 \| 407 \| 0.6212 \| 0.7535 \| \| 0.2696 \| 233.7143 \| 409 \| 0.5986 \| 0.7535 \| \| 0.2697 \| 234.8571 \| 411 \| 0.5964 \| 0.7465 \| \| 0.2697 \| 236.0 \| 413 \| 0.5950 \| 0.7465 \| \| 0.2697 \| 236.5714 \| 414 \| 0.5986 \| 0.7465 \| \| 0.2697 \| 237.7143 \| 416 \| 0.6066 \| 0.7535 \| \| 0.2697 \| 238.8571 \| 418 \| 0.6035 \| 0.7535 \| \| 0.2659 \| 240.0 \| 420 \| 0.6039 \| 0.7535 \| \| 0.2659 \| 240.5714 \| 421 \| 0.6004 \| 0.7535 \| \| 0.2659 \| 241.7143 \| 423 \| 0.6001 \| 0.7535 \| \| 0.2659 \| 242.8571 \| 425 \| 0.5941 \| 0.7465 \| \| 0.2659 \| 244.0 \| 427 \| 0.5942 \| 0.7394 \| \| 0.2659 \| 244.5714 \| 428 \| 0.5972 \| 0.7465 \| \| 0.2529 \| 245.7143 \| 430 \| 0.6077 \| 0.7535 \| \| 0.2529 \| 246.8571 \| 432 \| 0.6173 \| 0.7465 \| \| 0.2529 \| 248.0 \| 434 \| 0.6129 \| 0.7606 \| \| 0.2529 \| 248.5714 \| 435 \| 0.6099 \| 0.7606 \| \| 0.2529 \| 249.7143 \| 437 \| 0.6005 \| 0.7606 \| \| 0.2529 \| 250.8571 \| 439 \| 0.5920 \| 0.7606 \| \| 0.261 \| 252.0 \| 441 \| 0.5946 \| 0.7606 \| \| 0.261 \| 252.5714 \| 442 \| 0.5992 \| 0.7606 \| \| 0.261 \| 253.7143 \| 444 \| 0.6142 \| 0.7606 \| \| 0.261 \| 254.8571 \| 446 \| 0.6289 \| 0.7465 \| \| 0.261 \| 256.0 \| 448 \| 0.6316 \| 0.7465 \| \| 0.261 \| 256.5714 \| 449 \| 0.6302 \| 0.7535 \| \| 0.2675 \| 257.7143 \| 451 \| 0.6241 \| 0.7535 \| \| 0.2675 \| 258.8571 \| 453 \| 0.6129 \| 0.7535 \| \| 0.2675 \| 260.0 \| 455 \| 0.6066 \| 0.7465 \| \| 0.2675 \| 260.5714 \| 456 \| 0.6061 \| 0.7465 \| \| 0.2675 \| 261.7143 \| 458 \| 0.6098 \| 0.7535 \| \| 0.2737 \| 262.8571 \| 460 \| 0.6172 \| 0.7394 \| \| 0.2737 \| 264.0 \| 462 \| 0.6274 \| 0.7324 \| \| 0.2737 \| 264.5714 \| 463 \| 0.6298 \| 0.7324 \| \| 0.2737 \| 265.7143 \| 465 \| 0.6296 \| 0.7324 \| \| 0.2737 \| 266.8571 \| 467 \| 0.6285 \| 0.7324 \| \| 0.2737 \| 268.0 \| 469 \| 0.6265 \| 0.7324 \| \| 0.2504 \| 268.5714 \| 470 \| 0.6274 \| 0.7465 \| \| 0.2504 \| 269.7143 \| 472 \| 0.6286 \| 0.7394 \| \| 0.2504 \| 270.8571 \| 474 \| 0.6236 \| 0.7465 \| \| 0.2504 \| 272.0 \| 476 \| 0.6178 \| 0.7465 \| \| 0.2504 \| 272.5714 \| 477 \| 0.6164 \| 0.7465 \| \| 0.2504 \| 273.7143 \| 479 \| 0.6161 \| 0.7465 \| \| 0.2539 \| 274.8571 \| 481 \| 0.6193 \| 0.7465 \| \| 0.2539 \| 276.0 \| 483 \| 0.6236 \| 0.7394 \| \| 0.2539 \| 276.5714 \| 484 \| 0.6258 \| 0.7394 \| \| 0.2539 \| 277.7143 \| 486 \| 0.6308 \| 0.7394 \| \| 0.2539 \| 278.8571 \| 488 \| 0.6349 \| 0.7394 \| \| 0.2508 \| 280.0 \| 490 \| 0.6352 \| 0.7394 \| \| 0.2508 \| 280.5714 \| 491 \| 0.6346 \| 0.7394 \| \| 0.2508 \| 281.7143 \| 493 \| 0.6336 \| 0.7394 \| \| 0.2508 \| 282.8571 \| 495 \| 0.6331 \| 0.7394 \| \| 0.2508 \| 284.0 \| 497 \| 0.6324 \| 0.7394 \| \| 0.2508 \| 284.5714 \| 498 \| 0.6319 \| 0.7394 \| \| 0.2393 \| 285.7143 \| 500 \| 0.6316 \| 0.7394 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.2.0 - Tokenizers 0.19.1
lesso15/2e8bc4fa-d6cb-43a8-a829-70d21b0cac3d	lesso15	2025-01-24T17:54:09Z	7	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:unsloth/Mistral-Nemo-Instruct-2407", "base_model:adapter:unsloth/Mistral-Nemo-Instruct-2407", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T16:39:16Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Mistral-Nemo-Instruct-2407 tags: - axolotl - generated_from_trainer model-index: - name: 2e8bc4fa-d6cb-43a8-a829-70d21b0cac3d 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/Mistral-Nemo-Instruct-2407 bf16: auto chat_template: llama3 datasets: - data_files: - 661c83ddd68a38d2_train_data.json ds_type: json format: custom path: /workspace/input_data/661c83ddd68a38d2_train_data.json type: field_instruction: doc field_output: summary 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: 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: lesso15/2e8bc4fa-d6cb-43a8-a829-70d21b0cac3d 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: 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: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/661c83ddd68a38d2_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: 2249679b-6e87-4f27-bd19-24d76ede50f1 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 2249679b-6e87-4f27-bd19-24d76ede50f1 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 2e8bc4fa-d6cb-43a8-a829-70d21b0cac3d This model is a fine-tuned version of [unsloth/Mistral-Nemo-Instruct-2407](https://huggingface.co/unsloth/Mistral-Nemo-Instruct-2407) 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: 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.2740 \| 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
0x1202/2aa3e0ca-06a8-4584-8e8a-9e33fe39fafe	0x1202	2025-01-24T17:51:34Z	6	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Capybara-7B-V1.9", "base_model:adapter:NousResearch/Nous-Capybara-7B-V1.9", "license:mit", "region:us" ]	null	2025-01-24T17:23:24Z	--- library_name: peft license: mit base_model: NousResearch/Nous-Capybara-7B-V1.9 tags: - axolotl - generated_from_trainer model-index: - name: 2aa3e0ca-06a8-4584-8e8a-9e33fe39fafe 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/Nous-Capybara-7B-V1.9 bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - a22157a53c308254_train_data.json ds_type: json format: custom path: /workspace/input_data/a22157a53c308254_train_data.json type: field_input: paraphrase_types field_instruction: sentence1 field_output: sentence2 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: 0x1202/2aa3e0ca-06a8-4584-8e8a-9e33fe39fafe 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/a22157a53c308254_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: 50bc6a97-3376-4f9e-9686-b3782db5faa9 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 50bc6a97-3376-4f9e-9686-b3782db5faa9 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 2aa3e0ca-06a8-4584-8e8a-9e33fe39fafe This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1.9](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9736 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 5.5572 \| 0.0059 \| 1 \| 1.5554 \| \| 3.3254 \| 0.2954 \| 50 \| 1.0482 \| \| 3.1593 \| 0.5908 \| 100 \| 1.0086 \| \| 3.2459 \| 0.8863 \| 150 \| 0.9722 \| \| 2.4835 \| 1.1817 \| 200 \| 0.9736 \| ### 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/88e7a543-a8de-4394-8699-5e369ebd5369	havinash-ai	2025-01-24T17:48:26Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Hermes-3-Llama-3.1-8B", "base_model:adapter:unsloth/Hermes-3-Llama-3.1-8B", "region:us" ]	null	2025-01-24T17:46:21Z	--- library_name: peft base_model: unsloth/Hermes-3-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: 88e7a543-a8de-4394-8699-5e369ebd5369 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/Hermes-3-Llama-3.1-8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 7498d1d10e472fec_train_data.json ds_type: json format: custom path: /workspace/input_data/7498d1d10e472fec_train_data.json type: field_instruction: title field_output: text 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: havinash-ai/88e7a543-a8de-4394-8699-5e369ebd5369 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/7498d1d10e472fec_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: 145b78ab-4197-45e4-b32d-0f5f5e9a56ab wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 145b78ab-4197-45e4-b32d-0f5f5e9a56ab warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 88e7a543-a8de-4394-8699-5e369ebd5369 This model is a fine-tuned version of [unsloth/Hermes-3-Llama-3.1-8B](https://huggingface.co/unsloth/Hermes-3-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0009 \| 1 \| nan \| \| 0.0 \| 0.0026 \| 3 \| nan \| \| 0.0 \| 0.0052 \| 6 \| nan \| \| 0.0 \| 0.0077 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
IAmSkyDra/BARTBana_Translation_Swap_v0	IAmSkyDra	2025-01-24T17:48:12Z	15	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:IAmSkyDra/BARTBana_v4", "base_model:finetune:IAmSkyDra/BARTBana_v4", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-01-24T03:50:58Z	--- base_model: IAmSkyDra/BARTBana_v4 library_name: transformers license: mit metrics: - sacrebleu tags: - generated_from_trainer model-index: - name: BARTBana_Translation_Swap_v0 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. --> # BARTBana_Translation_Swap_v0 This model is a fine-tuned version of [IAmSkyDra/BARTBana_v4](https://huggingface.co/IAmSkyDra/BARTBana_v4) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0094 - Sacrebleu: 21.0811 ## 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: 100 - eval_batch_size: 100 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Sacrebleu \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\| \| 0.0167 \| 1.0 \| 21086 \| 0.0149 \| 20.8485 \| \| 0.0148 \| 2.0 \| 42172 \| 0.0109 \| 20.9983 \| \| 0.0103 \| 3.0 \| 63258 \| 0.0094 \| 21.0811 \| ### Framework versions - Transformers 4.48.1 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
tuanna08go/dd72e981-18a9-41f4-92f4-5064706d8d21	tuanna08go	2025-01-24T17:45:19Z	10	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:sethuiyer/Medichat-Llama3-8B", "base_model:adapter:sethuiyer/Medichat-Llama3-8B", "license:other", "region:us" ]	null	2025-01-24T17:29:22Z	--- library_name: peft license: other base_model: sethuiyer/Medichat-Llama3-8B tags: - axolotl - generated_from_trainer model-index: - name: dd72e981-18a9-41f4-92f4-5064706d8d21 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: sethuiyer/Medichat-Llama3-8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 26ca62faf35f3871_train_data.json ds_type: json format: custom path: /workspace/input_data/26ca62faf35f3871_train_data.json type: field_instruction: question field_output: answer format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 5 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: tuanna08go/dd72e981-18a9-41f4-92f4-5064706d8d21 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 5 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 50 micro_batch_size: 2 mlflow_experiment_name: /tmp/26ca62faf35f3871_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: 5011dd9e-451a-4f4a-b556-44c0cfbcd585 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 5011dd9e-451a-4f4a-b556-44c0cfbcd585 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # dd72e981-18a9-41f4-92f4-5064706d8d21 This model is a fine-tuned version of [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/sethuiyer/Medichat-Llama3-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2274 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0006 \| 1 \| 3.3160 \| \| 3.1726 \| 0.0060 \| 10 \| 2.8142 \| \| 2.3084 \| 0.0120 \| 20 \| 2.3126 \| \| 2.3688 \| 0.0180 \| 30 \| 2.2768 \| \| 1.9317 \| 0.0240 \| 40 \| 2.2329 \| \| 1.9194 \| 0.0299 \| 50 \| 2.2274 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
prxy5608/38461bf2-a926-4639-bb3e-ad2f99eff7d6	prxy5608	2025-01-24T17:43:26Z	6	0	peft	[ "peft", "safetensors", "phi3", "axolotl", "generated_from_trainer", "custom_code", "base_model:microsoft/Phi-3.5-mini-instruct", "base_model:adapter:microsoft/Phi-3.5-mini-instruct", "license:mit", "region:us" ]	null	2025-01-24T15:37:32Z	--- library_name: peft license: mit base_model: microsoft/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: 38461bf2-a926-4639-bb3e-ad2f99eff7d6 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-3.5-mini-instruct bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - e64308cf3a0ae5b0_train_data.json ds_type: json format: custom path: /workspace/input_data/e64308cf3a0ae5b0_train_data.json type: field_input: src_doc_title field_instruction: src_text field_output: trg_text format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: prxy5608/38461bf2-a926-4639-bb3e-ad2f99eff7d6 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/e64308cf3a0ae5b0_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: 7176bde4-6840-4177-a6e5-4ae7849f205d wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 7176bde4-6840-4177-a6e5-4ae7849f205d warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 38461bf2-a926-4639-bb3e-ad2f99eff7d6 This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4606 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 7.7055 \| 0.0001 \| 1 \| 3.2970 \| \| 8.7409 \| 0.0031 \| 50 \| 1.7901 \| \| 6.4935 \| 0.0062 \| 100 \| 1.5863 \| \| 7.9466 \| 0.0093 \| 150 \| 1.4815 \| \| 7.6805 \| 0.0123 \| 200 \| 1.4606 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
arcars/reactor-mk1-I1	arcars	2025-01-24T17:42:40Z	8	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-24T16:59:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
RichardErkhov/waraml_-_ViLinh-3B-4bits	RichardErkhov	2025-01-24T17:42:32Z	5	0	null	[ "safetensors", "qwen2", "4-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T17:39:35Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) ViLinh-3B - bnb 4bits - Model creator: https://huggingface.co/waraml/ - Original model: https://huggingface.co/waraml/ViLinh-3B/ Original model description: --- library_name: transformers license: other base_model: qnguyen3/vylinh-qwen-3b-merged tags: - llama-factory - full - generated_from_trainer model-index: - name: vylinh-dpo-v4 results: [] ---
nttx/9ed5806e-9a59-4228-a359-9f40a7f9445a	nttx	2025-01-24T17:39:04Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3.5-mini-instruct", "base_model:adapter:unsloth/Phi-3.5-mini-instruct", "license:mit", "region:us" ]	null	2025-01-24T17:08:06Z	--- library_name: peft license: mit base_model: unsloth/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: 9ed5806e-9a59-4228-a359-9f40a7f9445a 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/Phi-3.5-mini-instruct bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 14e7f20aa0ab15bf_train_data.json ds_type: json format: custom path: /workspace/input_data/14e7f20aa0ab15bf_train_data.json type: field_input: input field_instruction: system field_output: response 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: nttx/9ed5806e-9a59-4228-a359-9f40a7f9445a 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/14e7f20aa0ab15bf_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: 5c66e9d0-7fa2-4ea5-83d0-56a779006d22 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 5c66e9d0-7fa2-4ea5-83d0-56a779006d22 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 9ed5806e-9a59-4228-a359-9f40a7f9445a This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 10.5411 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 11.0985 \| 0.0005 \| 1 \| 9.1647 \| \| 10.3675 \| 0.0257 \| 50 \| 11.0540 \| \| 9.8685 \| 0.0514 \| 100 \| 10.3661 \| \| 10.3118 \| 0.0771 \| 150 \| 10.5581 \| \| 10.339 \| 0.1028 \| 200 \| 10.5411 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
datlaaaaaaa/40db0833-088d-4333-b8a7-d9c429000a03	datlaaaaaaa	2025-01-24T17:38:27Z	8	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:unsloth/Mistral-Nemo-Instruct-2407", "base_model:adapter:unsloth/Mistral-Nemo-Instruct-2407", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T16:38:36Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Mistral-Nemo-Instruct-2407 tags: - axolotl - generated_from_trainer model-index: - name: 40db0833-088d-4333-b8a7-d9c429000a03 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/Mistral-Nemo-Instruct-2407 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 661c83ddd68a38d2_train_data.json ds_type: json format: custom path: /workspace/input_data/661c83ddd68a38d2_train_data.json type: field_instruction: doc field_output: summary format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: datlaaaaaaa/40db0833-088d-4333-b8a7-d9c429000a03 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/661c83ddd68a38d2_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: 2249679b-6e87-4f27-bd19-24d76ede50f1 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 2249679b-6e87-4f27-bd19-24d76ede50f1 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 40db0833-088d-4333-b8a7-d9c429000a03 This model is a fine-tuned version of [unsloth/Mistral-Nemo-Instruct-2407](https://huggingface.co/unsloth/Mistral-Nemo-Instruct-2407) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8337 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.7979 \| 0.2740 \| 200 \| 0.8337 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
MaziyarPanahi/xLAM-7b-fc-r-GGUF	MaziyarPanahi	2025-01-24T17:37:03Z	199	0	null	[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:Salesforce/xLAM-7b-fc-r", "base_model:quantized:Salesforce/xLAM-7b-fc-r", "region:us", "conversational" ]	text-generation	2025-01-24T17:18:18Z	--- base_model: Salesforce/xLAM-7b-fc-r inference: false model_creator: Salesforce model_name: xLAM-7b-fc-r-GGUF pipeline_tag: text-generation quantized_by: MaziyarPanahi tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation --- # [MaziyarPanahi/xLAM-7b-fc-r-GGUF](https://huggingface.co/MaziyarPanahi/xLAM-7b-fc-r-GGUF) - Model creator: [Salesforce](https://huggingface.co/Salesforce) - Original model: [Salesforce/xLAM-7b-fc-r](https://huggingface.co/Salesforce/xLAM-7b-fc-r) ## Description [MaziyarPanahi/xLAM-7b-fc-r-GGUF](https://huggingface.co/MaziyarPanahi/xLAM-7b-fc-r-GGUF) contains GGUF format model files for [Salesforce/xLAM-7b-fc-r](https://huggingface.co/Salesforce/xLAM-7b-fc-r). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
8friends/martini	8friends	2025-01-24T17:36:45Z	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	2025-01-24T16:46:47Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: MartiniFLUX --- # Martini <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `MartiniFLUX` 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('8friends/martini', 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)
Romain-XV/8d3367e3-cfd2-44de-a49c-9adebd44dadd	Romain-XV	2025-01-24T17:36:15Z	7	0	peft	[ "peft", "safetensors", "phi", "axolotl", "generated_from_trainer", "base_model:echarlaix/tiny-random-PhiForCausalLM", "base_model:adapter:echarlaix/tiny-random-PhiForCausalLM", "license:apache-2.0", "region:us" ]	null	2025-01-24T17:33:52Z	--- library_name: peft license: apache-2.0 base_model: echarlaix/tiny-random-PhiForCausalLM tags: - axolotl - generated_from_trainer model-index: - name: 8d3367e3-cfd2-44de-a49c-9adebd44dadd 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: echarlaix/tiny-random-PhiForCausalLM bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 6be8b7f2d665f721_train_data.json ds_type: json format: custom path: /workspace/input_data/6be8b7f2d665f721_train_data.json type: field_input: prompt_name field_instruction: prompt field_output: completion format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 5 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 16 gradient_checkpointing: true group_by_length: false hub_model_id: Romain-XV/8d3367e3-cfd2-44de-a49c-9adebd44dadd hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_best_model_at_end: true load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: true lora_model_dir: null lora_r: 16 lora_target_linear: true lora_target_modules: - q_proj - k_proj - v_proj lr_scheduler: cosine max_steps: 207 micro_batch_size: 4 mlflow_experiment_name: /tmp/6be8b7f2d665f721_train_data.json model_type: AutoModelForCausalLM optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 100 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: bc04a606-258f-4bff-8fbe-78a2b190e142 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: bc04a606-258f-4bff-8fbe-78a2b190e142 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 8d3367e3-cfd2-44de-a49c-9adebd44dadd This model is a fine-tuned version of [echarlaix/tiny-random-PhiForCausalLM](https://huggingface.co/echarlaix/tiny-random-PhiForCausalLM) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.8135 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - 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: 207 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 6.9289 \| 0.0005 \| 1 \| 6.9372 \| \| 6.86 \| 0.0234 \| 50 \| 6.8584 \| \| 6.8303 \| 0.0469 \| 100 \| 6.8300 \| \| 6.8214 \| 0.0703 \| 150 \| 6.8157 \| \| 6.8078 \| 0.0937 \| 200 \| 6.8135 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ehristoforu/tmoe-v2	ehristoforu	2025-01-24T17:29:36Z	14	0	transformers	[ "transformers", "safetensors", "qwen2_moe", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-01-24T17:22:21Z	--- library_name: transformers license: apache-2.0 --- ``` base_model: Qwen/Qwen2.5-1.5B-Instruct gate_mode: cheap_embed architecture: qwen experts_per_token: 4 dtype: bfloat16 experts: - source_model: Qwen/Qwen2.5-1.5B-Instruct positive_prompts: ["chat assistant"] - source_model: Qwen/Qwen2.5-Coder-1.5B-Instruct positive_prompts: ["code assistant"] - source_model: Qwen/Qwen2.5-Math-1.5B-Instruct positive_prompts: ["math assistant"] - source_model: huihui-ai/Qwen2.5-1.5B-Instruct-abliterated positive_prompts: ["uncensored assistant"] - source_model: Rombo-Org/Rombo-LLM-V2.5-Qwen-1.5b positive_prompts: ["review assistant"] - source_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B positive_prompts: ["logical assistant"] - source_model: Vikhrmodels/Vikhr-Qwen-2.5-1.5B-Instruct positive_prompts: ["writing assistant"] - source_model: RefalMachine/RuadaptQwen2.5-1.5B-instruct positive_prompts: ["text editing assistant"] shared_experts: - source_model: Qwen/Qwen2.5-1.5B-Instruct positive_prompts: ["chat assistant"] residual_scale: 0.1 ```
lhong4759/b0027da3-2f57-4fec-82a1-491799a29dcc	lhong4759	2025-01-24T17:28:40Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer", "base_model:adapter:NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T17:00:46Z	--- library_name: peft license: other base_model: NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer tags: - axolotl - generated_from_trainer model-index: - name: b0027da3-2f57-4fec-82a1-491799a29dcc 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/Meta-Llama-3-8B-Alternate-Tokenizer bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 7349255a0722d332_train_data.json ds_type: json format: custom path: /workspace/input_data/7349255a0722d332_train_data.json type: field_input: author field_instruction: title field_output: text format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: lhong4759/b0027da3-2f57-4fec-82a1-491799a29dcc 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/7349255a0722d332_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: 13fce366-6808-46f8-8029-0af5c3620a55 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 13fce366-6808-46f8-8029-0af5c3620a55 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # b0027da3-2f57-4fec-82a1-491799a29dcc This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3166 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.165 \| 0.1703 \| 200 \| 2.3166 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
duyphu/6e04d534-2b9c-40a7-9892-fbd2a9d5ff8b	duyphu	2025-01-24T17:28:31Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3.5-mini-instruct", "base_model:adapter:unsloth/Phi-3.5-mini-instruct", "license:mit", "region:us" ]	null	2025-01-24T17:08:27Z	--- library_name: peft license: mit base_model: unsloth/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: 6e04d534-2b9c-40a7-9892-fbd2a9d5ff8b 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/Phi-3.5-mini-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 14e7f20aa0ab15bf_train_data.json ds_type: json format: custom path: /workspace/input_data/14e7f20aa0ab15bf_train_data.json type: field_input: input field_instruction: system field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 5 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: duyphu/6e04d534-2b9c-40a7-9892-fbd2a9d5ff8b hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 5 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 50 micro_batch_size: 2 mlflow_experiment_name: /tmp/14e7f20aa0ab15bf_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: 5c66e9d0-7fa2-4ea5-83d0-56a779006d22 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 5c66e9d0-7fa2-4ea5-83d0-56a779006d22 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 6e04d534-2b9c-40a7-9892-fbd2a9d5ff8b This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| nan \| \| 0.0 \| 0.0013 \| 10 \| nan \| \| 0.0 \| 0.0026 \| 20 \| nan \| \| 0.0 \| 0.0039 \| 30 \| nan \| \| 0.0 \| 0.0051 \| 40 \| nan \| \| 0.0 \| 0.0064 \| 50 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
hackint0sh/phi-3-clinical	hackint0sh	2025-01-24T17:28:29Z	264	1	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-06T13:26:33Z	# 🤖 Phi-3-Clinical Welcome to the repository for Phi-3-Clinical, a fine-tuned model designed to empower medical researchers and developers in the Bio-Pharma domain. This model has been meticulously trained on clinical trial datasets from the U.S. government to deliver high-quality insights and facilitate research and development in healthcare and pharmaceutical innovation. This model is currently being actively updated and improved as part of my ongoing research and work in Retrieval-Augmented Generation (RAG). --- ## 🚀 Key Features - Fine-Tuned on: Clinical - Primary Use Case(s): [Summarization, Question Answering, etc.] - Updates in Progress: - Optimizing for better accuracy with RAG workflows. - Incorporating new datasets and training strategies. - Fine-tuning with community feedback. --- ## 📅 What's Next? I am actively working on: 1. Integrating this model into a RAG pipeline for enhanced retrieval-augmented tasks. 2. Regular updates to improve performance and reduce inference time. 3. Expanding support for [languages/domains/etc.]. Stay tuned for updates and improvements in the coming weeks! --- ## 🛠️ How to Use Here's a quick example of how you can use this model: ```python from transformers import pipeline # Load the model model = pipeline("task_name", model="hackint0sh/phi-3-clinical") # Example usage input_text = "Your input here" output = model(input_text) print(output) ``` Replace task_name with the appropriate task (e.g., "text-classification", "question-answering", "Clinical Trial Format"). 🙋‍♂️ Need Help? I’m here to help! If you have any questions, suggestions, or encounter any issues while using the model, feel free to: • Open an Issue on this repository. • DM me directly on Hugging Face. I’m always happy to collaborate and improve this model further based on your feedback. 😊 🌟 Contributing Contributions are welcome! If you have ideas for improvements or want to contribute, feel free to fork this repository and open a pull request. 📝 License This model is released under the MIT license. See the LICENSE file for more details. Thank you for your interest in Phi-3-Clinical! Your support and feedback help make this model better for everyone. ❤️
visdata/cook16	visdata	2025-01-24T17:24:53Z	29	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-24T17:06:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
thangla01/f4d5a30d-119b-4d05-a18b-d90e9d562e4d	thangla01	2025-01-24T17:24:07Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:heegyu/WizardVicuna-open-llama-3b-v2", "base_model:adapter:heegyu/WizardVicuna-open-llama-3b-v2", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T15:24:49Z	--- library_name: peft license: apache-2.0 base_model: heegyu/WizardVicuna-open-llama-3b-v2 tags: - axolotl - generated_from_trainer model-index: - name: f4d5a30d-119b-4d05-a18b-d90e9d562e4d 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: heegyu/WizardVicuna-open-llama-3b-v2 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 575c32387ecb28f7_train_data.json ds_type: json format: custom path: /workspace/input_data/575c32387ecb28f7_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: thangla01/f4d5a30d-119b-4d05-a18b-d90e9d562e4d 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/575c32387ecb28f7_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 077294c5-cf74-4889-92d2-814219f70be0 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 077294c5-cf74-4889-92d2-814219f70be0 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # f4d5a30d-119b-4d05-a18b-d90e9d562e4d This model is a fine-tuned version of [heegyu/WizardVicuna-open-llama-3b-v2](https://huggingface.co/heegyu/WizardVicuna-open-llama-3b-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0137 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1301 \| 0.0017 \| 200 \| 1.0137 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Muadil/Llama-3.2-1B-Instruct_sum_PPO_Skywork_1k_1_3ep	Muadil	2025-01-24T17:24:01Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-01-24T17:22:49Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/bruphin-lambda-i1-GGUF	mradermacher	2025-01-24T17:23:36Z	359	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:nbeerbower/bruphin-lambda", "base_model:quantized:nbeerbower/bruphin-lambda", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-01-24T14:36:40Z	--- base_model: nbeerbower/bruphin-lambda 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: nicoboss --> weighted/imatrix quants of https://huggingface.co/nbeerbower/bruphin-lambda <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/bruphin-lambda-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/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-Q2_K.gguf) \| i1-Q2_K \| 2.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-Q4_1.gguf) \| i1-Q4_1 \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/bruphin-lambda-i1-GGUF/resolve/main/bruphin-lambda.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 -->
sergioalves/6b63f873-aa24-49b9-b345-1c0709510c83	sergioalves	2025-01-24T17:23:15Z	6	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3-medium-4k-instruct", "base_model:adapter:unsloth/Phi-3-medium-4k-instruct", "license:mit", "region:us" ]	null	2025-01-24T17:07:32Z	--- library_name: peft license: mit base_model: unsloth/Phi-3-medium-4k-instruct tags: - axolotl - generated_from_trainer model-index: - name: 6b63f873-aa24-49b9-b345-1c0709510c83 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/Phi-3-medium-4k-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - ebc79fbfad09ef54_train_data.json ds_type: json format: custom path: /workspace/input_data/ebc79fbfad09ef54_train_data.json type: field_instruction: message_1 field_output: message_2 format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: 1 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: sergioalves/6b63f873-aa24-49b9-b345-1c0709510c83 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 79GiB max_steps: 30 micro_batch_size: 4 mlflow_experiment_name: /tmp/ebc79fbfad09ef54_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 714dd9ad-7ff4-43d7-ab89-ab528ee5d7c9 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 714dd9ad-7ff4-43d7-ab89-ab528ee5d7c9 warmup_steps: 5 weight_decay: 0.001 xformers_attention: true ``` </details><br> # 6b63f873-aa24-49b9-b345-1c0709510c83 This model is a fine-tuned version of [unsloth/Phi-3-medium-4k-instruct](https://huggingface.co/unsloth/Phi-3-medium-4k-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0009 \| 1 \| nan \| \| 0.0 \| 0.0043 \| 5 \| nan \| \| 0.0 \| 0.0085 \| 10 \| nan \| \| 0.0 \| 0.0128 \| 15 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
dimasik1987/3436eb18-86f9-4e2c-b232-019f42f1c8d0	dimasik1987	2025-01-24T17:23:11Z	6	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	2025-01-24T17:20:29Z	--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - axolotl - generated_from_trainer model-index: - name: 3436eb18-86f9-4e2c-b232-019f42f1c8d0 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: - ae575f57377b68c1_train_data.json ds_type: json format: custom path: /workspace/input_data/ae575f57377b68c1_train_data.json type: field_instruction: url field_output: text format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: 1 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: dimasik1987/3436eb18-86f9-4e2c-b232-019f42f1c8d0 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 79GiB max_steps: 30 micro_batch_size: 4 mlflow_experiment_name: /tmp/ae575f57377b68c1_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: ee5b7b78-ea0d-47d8-abcc-509e81fc5c60 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: ee5b7b78-ea0d-47d8-abcc-509e81fc5c60 warmup_steps: 5 weight_decay: 0.001 xformers_attention: true ``` </details><br> # 3436eb18-86f9-4e2c-b232-019f42f1c8d0 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: 2.7879 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0093 \| 1 \| 3.4304 \| \| 3.0488 \| 0.0467 \| 5 \| 3.2876 \| \| 3.1991 \| 0.0935 \| 10 \| 3.0793 \| \| 3.175 \| 0.1402 \| 15 \| 2.9395 \| \| 2.9589 \| 0.1869 \| 20 \| 2.8401 \| \| 2.911 \| 0.2336 \| 25 \| 2.7981 \| \| 2.5735 \| 0.2804 \| 30 \| 2.7879 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
0x1202/ecb8379e-81b0-4bd5-a690-c05a5108b46a	0x1202	2025-01-24T17:21:31Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Hermes-3-Llama-3.1-8B", "base_model:adapter:unsloth/Hermes-3-Llama-3.1-8B", "region:us" ]	null	2025-01-24T16:47:38Z	--- library_name: peft base_model: unsloth/Hermes-3-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: ecb8379e-81b0-4bd5-a690-c05a5108b46a 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/Hermes-3-Llama-3.1-8B bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 7498d1d10e472fec_train_data.json ds_type: json format: custom path: /workspace/input_data/7498d1d10e472fec_train_data.json type: field_instruction: title field_output: text format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: 0x1202/ecb8379e-81b0-4bd5-a690-c05a5108b46a 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/7498d1d10e472fec_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: 145b78ab-4197-45e4-b32d-0f5f5e9a56ab wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 145b78ab-4197-45e4-b32d-0f5f5e9a56ab warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # ecb8379e-81b0-4bd5-a690-c05a5108b46a This model is a fine-tuned version of [unsloth/Hermes-3-Llama-3.1-8B](https://huggingface.co/unsloth/Hermes-3-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4644 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.124 \| 0.0034 \| 1 \| 2.3156 \| \| 1.4537 \| 0.1718 \| 50 \| 1.6402 \| \| 1.1582 \| 0.3436 \| 100 \| 1.5493 \| \| 1.2614 \| 0.5155 \| 150 \| 1.4730 \| \| 1.3643 \| 0.6873 \| 200 \| 1.4644 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
JacksonBrune/d3ecb40b-4ec4-48e8-94b2-c49ee500b18f	JacksonBrune	2025-01-24T17:21:21Z	6	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Capybara-7B-V1.9", "base_model:adapter:NousResearch/Nous-Capybara-7B-V1.9", "license:mit", "region:us" ]	null	2025-01-24T17:20:12Z	--- library_name: peft license: mit base_model: NousResearch/Nous-Capybara-7B-V1.9 tags: - axolotl - generated_from_trainer model-index: - name: d3ecb40b-4ec4-48e8-94b2-c49ee500b18f 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/Nous-Capybara-7B-V1.9 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - a22157a53c308254_train_data.json ds_type: json format: custom path: /workspace/input_data/a22157a53c308254_train_data.json type: field_input: paraphrase_types field_instruction: sentence1 field_output: sentence2 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/d3ecb40b-4ec4-48e8-94b2-c49ee500b18f hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/a22157a53c308254_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: 50bc6a97-3376-4f9e-9686-b3782db5faa9 wandb_project: birthdya-sn56-18-Gradients-On-Demand wandb_run: your_name wandb_runid: 50bc6a97-3376-4f9e-9686-b3782db5faa9 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # d3ecb40b-4ec4-48e8-94b2-c49ee500b18f This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1.9](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0015 \| 1 \| nan \| \| 0.0 \| 0.0044 \| 3 \| nan \| \| 0.0 \| 0.0089 \| 6 \| nan \| \| 0.0 \| 0.0133 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
masint/tiny-random-llama	masint	2025-01-24T17:20:41Z	3,681	0	null	[ "safetensors", "llama", "license:apache-2.0", "region:us" ]	null	2024-11-13T21:44:32Z	--- license: apache-2.0 ---
kk-aivio/63cd7910-a4ac-4ac2-b733-dd3f6a82b707	kk-aivio	2025-01-24T17:20:23Z	9	0	peft	[ "peft", "safetensors", "mixtral", "axolotl", "generated_from_trainer", "base_model:Eurdem/Defne_llama3_2x8B", "base_model:adapter:Eurdem/Defne_llama3_2x8B", "license:llama3", "region:us" ]	null	2025-01-24T17:15:08Z	--- library_name: peft license: llama3 base_model: Eurdem/Defne_llama3_2x8B tags: - axolotl - generated_from_trainer model-index: - name: 63cd7910-a4ac-4ac2-b733-dd3f6a82b707 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: Eurdem/Defne_llama3_2x8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 8fba98d9ee650ad7_train_data.json ds_type: json format: custom path: /workspace/input_data/8fba98d9ee650ad7_train_data.json type: field_instruction: original field_output: generation format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: kk-aivio/63cd7910-a4ac-4ac2-b733-dd3f6a82b707 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/8fba98d9ee650ad7_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: eb9ecb18-093a-4fda-8179-596682f7f582 wandb_project: Birthday-SN56-17-Gradients-On-Demand wandb_run: your_name wandb_runid: eb9ecb18-093a-4fda-8179-596682f7f582 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 63cd7910-a4ac-4ac2-b733-dd3f6a82b707 This model is a fine-tuned version of [Eurdem/Defne_llama3_2x8B](https://huggingface.co/Eurdem/Defne_llama3_2x8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0003 \| 1 \| nan \| \| 0.0 \| 0.0010 \| 3 \| nan \| \| 0.0 \| 0.0020 \| 6 \| nan \| \| 0.0 \| 0.0030 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nhung03/8ce4202e-77f6-4672-9d8c-472b53c319d8	nhung03	2025-01-24T17:19:36Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer", "base_model:adapter:NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T17:00:34Z	--- library_name: peft license: other base_model: NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer tags: - axolotl - generated_from_trainer model-index: - name: 8ce4202e-77f6-4672-9d8c-472b53c319d8 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/Meta-Llama-3-8B-Alternate-Tokenizer bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 7349255a0722d332_train_data.json ds_type: json format: custom path: /workspace/input_data/7349255a0722d332_train_data.json type: field_input: author field_instruction: title field_output: text format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 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: nhung03/8ce4202e-77f6-4672-9d8c-472b53c319d8 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/7349255a0722d332_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: 13fce366-6808-46f8-8029-0af5c3620a55 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 13fce366-6808-46f8-8029-0af5c3620a55 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 8ce4202e-77f6-4672-9d8c-472b53c319d8 This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3156 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.156 \| 0.1703 \| 200 \| 2.3156 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
aleegis12/9f3f5a89-654e-4064-a6c3-0dab1e098823	aleegis12	2025-01-24T17:18:23Z	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	2025-01-24T14:11:08Z	--- library_name: peft license: apache-2.0 base_model: openlm-research/open_llama_3b tags: - axolotl - generated_from_trainer model-index: - name: 9f3f5a89-654e-4064-a6c3-0dab1e098823 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: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - aa8537f7fabb44d3_train_data.json ds_type: json format: custom path: /workspace/input_data/aa8537f7fabb44d3_train_data.json type: field_instruction: en field_output: hi format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: aleegis12/9f3f5a89-654e-4064-a6c3-0dab1e098823 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/aa8537f7fabb44d3_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 64219759-3ade-4844-b427-6bd1bf891a27 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 64219759-3ade-4844-b427-6bd1bf891a27 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 9f3f5a89-654e-4064-a6c3-0dab1e098823 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: 0.3199 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.4842 \| 0.0000 \| 1 \| 0.6386 \| \| 0.7284 \| 0.0020 \| 50 \| 0.4892 \| \| 0.5061 \| 0.0039 \| 100 \| 0.3692 \| \| 0.4737 \| 0.0059 \| 150 \| 0.3279 \| \| 0.5555 \| 0.0079 \| 200 \| 0.3199 \| ### 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/2fef6e97-a25b-4623-aa01-4851a1b43758	kostiantynk-out	2025-01-24T17:17:28Z	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-360M-Instruct", "base_model:adapter:unsloth/SmolLM-360M-Instruct", "license:apache-2.0", "region:us" ]	null	2025-01-24T16:41:17Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-360M-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 2fef6e97-a25b-4623-aa01-4851a1b43758 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/SmolLM-360M-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f806d2d7fa7067cd_train_data.json ds_type: json format: custom path: /workspace/input_data/f806d2d7fa7067cd_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: kostiantynk-out/2fef6e97-a25b-4623-aa01-4851a1b43758 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/f806d2d7fa7067cd_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: 8b585115-23cd-4a56-9fba-b57318c01a85 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 8b585115-23cd-4a56-9fba-b57318c01a85 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 2fef6e97-a25b-4623-aa01-4851a1b43758 This model is a fine-tuned version of [unsloth/SmolLM-360M-Instruct](https://huggingface.co/unsloth/SmolLM-360M-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0000 \| 1 \| nan \| \| 0.0 \| 0.0001 \| 3 \| nan \| \| 0.0 \| 0.0001 \| 6 \| nan \| \| 0.0 \| 0.0002 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
nhung01/4db533a6-45ba-4b24-8517-f3834b544f68	nhung01	2025-01-24T17:16:09Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:heegyu/WizardVicuna-open-llama-3b-v2", "base_model:adapter:heegyu/WizardVicuna-open-llama-3b-v2", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T15:26:23Z	--- library_name: peft license: apache-2.0 base_model: heegyu/WizardVicuna-open-llama-3b-v2 tags: - axolotl - generated_from_trainer model-index: - name: 4db533a6-45ba-4b24-8517-f3834b544f68 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: heegyu/WizardVicuna-open-llama-3b-v2 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 575c32387ecb28f7_train_data.json ds_type: json format: custom path: /workspace/input_data/575c32387ecb28f7_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: nhung01/4db533a6-45ba-4b24-8517-f3834b544f68 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/575c32387ecb28f7_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 077294c5-cf74-4889-92d2-814219f70be0 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 077294c5-cf74-4889-92d2-814219f70be0 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 4db533a6-45ba-4b24-8517-f3834b544f68 This model is a fine-tuned version of [heegyu/WizardVicuna-open-llama-3b-v2](https://huggingface.co/heegyu/WizardVicuna-open-llama-3b-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0132 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1263 \| 0.0017 \| 200 \| 1.0132 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
treasure4l/Llama3.2_3B_DPO	treasure4l	2025-01-24T17:14:42Z	10	0	peft	[ "peft", "safetensors", "dataset:gz25/JGTV_Pref_DS", "arxiv:1910.09700", "region:us" ]	null	2025-01-24T03:27:09Z	--- base_model: unsloth/llama-3.2-3b-bnb-4bit library_name: peft datasets: - gz25/JGTV_Pref_DS --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0
lesso10/f6295c50-f971-420e-b187-e9b89f697b60	lesso10	2025-01-24T17:14:29Z	5	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:EleutherAI/pythia-1b", "base_model:adapter:EleutherAI/pythia-1b", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T15:46:50Z	--- library_name: peft license: apache-2.0 base_model: EleutherAI/pythia-1b tags: - axolotl - generated_from_trainer model-index: - name: f6295c50-f971-420e-b187-e9b89f697b60 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-1b bf16: true chat_template: llama3 datasets: - data_files: - 959285475cc53225_train_data.json ds_type: json format: custom path: /workspace/input_data/959285475cc53225_train_data.json type: field_input: post field_instruction: title field_output: summary format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 2 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: lesso10/f6295c50-f971-420e-b187-e9b89f697b60 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 25 micro_batch_size: 2 mlflow_experiment_name: /tmp/959285475cc53225_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 512 special_tokens: pad_token: <\|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: ad0763da-bb5f-41ee-89a4-20beb5a03fb3 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: ad0763da-bb5f-41ee-89a4-20beb5a03fb3 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # f6295c50-f971-420e-b187-e9b89f697b60 This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5797 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 13.1418 \| 0.0001 \| 1 \| 3.0369 \| \| 12.1455 \| 0.0003 \| 5 \| 2.9812 \| \| 11.799 \| 0.0007 \| 10 \| 2.7262 \| \| 9.7705 \| 0.0010 \| 15 \| 2.6347 \| \| 11.0196 \| 0.0013 \| 20 \| 2.5897 \| \| 10.6078 \| 0.0016 \| 25 \| 2.5797 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
cvoffer/26784c63-3b53-402b-8736-e7956fd384d4	cvoffer	2025-01-24T17:13:55Z	5	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-360M-Instruct", "base_model:adapter:unsloth/SmolLM-360M-Instruct", "license:apache-2.0", "region:us" ]	null	2025-01-24T17:05:55Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-360M-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 26784c63-3b53-402b-8736-e7956fd384d4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/SmolLM-360M-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 615cebde847ce8a5_train_data.json ds_type: json format: custom path: /workspace/input_data/615cebde847ce8a5_train_data.json type: field_input: system_prompt field_instruction: question field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: 1 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: cvoffer/26784c63-3b53-402b-8736-e7956fd384d4 hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 78GiB max_steps: 30 micro_batch_size: 2 mlflow_experiment_name: /tmp/615cebde847ce8a5_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3260e223-b692-495b-a4ed-8da11180f21b wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 3260e223-b692-495b-a4ed-8da11180f21b warmup_steps: 5 weight_decay: 0.001 xformers_attention: true ``` </details><br> # 26784c63-3b53-402b-8736-e7956fd384d4 This model is a fine-tuned version of [unsloth/SmolLM-360M-Instruct](https://huggingface.co/unsloth/SmolLM-360M-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| nan \| \| 0.0 \| 0.0004 \| 5 \| nan \| \| 0.0 \| 0.0008 \| 10 \| nan \| \| 0.0 \| 0.0012 \| 15 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
dim-eleftheriou/Mistral-7B-Instruct-v0.3-mitre-v0.1-GGUF-Q4_k_m	dim-eleftheriou	2025-01-24T17:13:16Z	35	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-24T17:12:21Z	--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: dim-eleftheriou - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Colezwhy/dreambooth	Colezwhy	2025-01-24T17:11:49Z	31	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "lora", "base_model:stable-diffusion-v1-5/stable-diffusion-v1-5", "base_model:adapter:stable-diffusion-v1-5/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-01-23T10:49:33Z	--- base_model: stable-diffusion-v1-5/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m inference: true instance_prompt: a photo of sks dog tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers - diffusers - lora - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - Colezwhy/dreambooth This is a dreambooth model derived from stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
taopanda-4/57c3a30c-e883-4216-a074-89f10c859369	taopanda-4	2025-01-24T17:09:57Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-135M", "base_model:adapter:unsloth/SmolLM-135M", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T16:39:59Z	--- license: apache-2.0 library_name: peft tags: - axolotl - generated_from_trainer base_model: unsloth/SmolLM-135M model-index: - name: 57c3a30c-e883-4216-a074-89f10c859369 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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/SmolLM-135M bf16: auto datasets: - data_files: - 12918daafc7e29ad_train_data.json ds_type: json format: custom path: 12918daafc7e29ad_train_data.json type: field: null field_input: null field_instruction: premise field_output: entailment field_system: null format: null no_input_format: null system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_sample_packing: false eval_table_size: null evals_per_epoch: 0 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: taopanda-4/57c3a30c-e883-4216-a074-89f10c859369 learning_rate: 0.0002 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_r: 32 lora_target_linear: true lr_scheduler: cosine micro_batch_size: 2 model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: ./outputs/out/taopanda-4_f45cb749-0cc3-4d0c-ab1a-6f0cbfef80b5 pad_to_sequence_len: true resume_from_checkpoint: null sample_packing: true saves_per_epoch: 1 seed: 99684 sequence_len: 4096 special_tokens: null strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.1 wandb_entity: fatcat87-taopanda wandb_log_model: null wandb_mode: online wandb_name: taopanda-4_f45cb749-0cc3-4d0c-ab1a-6f0cbfef80b5 wandb_project: subnet56 wandb_runid: taopanda-4_f45cb749-0cc3-4d0c-ab1a-6f0cbfef80b5 wandb_watch: null warmup_ratio: 0.05 weight_decay: 0.0 xformers_attention: null ``` </details><br> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/fatcat87-taopanda/subnet56/runs/wiw4ie03) # 57c3a30c-e883-4216-a074-89f10c859369 This model is a fine-tuned version of [unsloth/SmolLM-135M](https://huggingface.co/unsloth/SmolLM-135M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0362 ## 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: 99684 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 15 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.015 \| 1.0 \| 318 \| 1.0362 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.42.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Romain-XV/c61e0709-7239-4e4e-b7c6-559f047b8349	Romain-XV	2025-01-24T17:08:49Z	11	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0", "base_model:adapter:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0", "license:llama3", "region:us" ]	null	2025-01-24T16:06:24Z	--- library_name: peft license: llama3 base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 tags: - axolotl - generated_from_trainer model-index: - name: c61e0709-7239-4e4e-b7c6-559f047b8349 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: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 26bc9bf8a02efd49_train_data.json ds_type: json format: custom path: /workspace/input_data/26bc9bf8a02efd49_train_data.json type: field_input: candidates field_instruction: article field_output: question format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 5 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 16 gradient_checkpointing: true group_by_length: false hub_model_id: Romain-XV/c61e0709-7239-4e4e-b7c6-559f047b8349 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_best_model_at_end: true load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: true lora_model_dir: null lora_r: 16 lora_target_linear: true lora_target_modules: - q_proj - k_proj - v_proj lr_scheduler: cosine max_steps: 518 micro_batch_size: 4 mlflow_experiment_name: /tmp/26bc9bf8a02efd49_train_data.json model_type: AutoModelForCausalLM optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 100 sequence_len: 2048 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: 8a6d5fac-bf65-456e-9711-c9932c0698ec wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 8a6d5fac-bf65-456e-9711-c9932c0698ec warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # c61e0709-7239-4e4e-b7c6-559f047b8349 This model is a fine-tuned version of [WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0](https://huggingface.co/WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2308 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - 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: 115 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.8074 \| 0.0087 \| 1 \| 2.7402 \| \| 1.2987 \| 0.4372 \| 50 \| 1.2585 \| \| 1.2285 \| 0.8743 \| 100 \| 1.2308 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
thalllsssss/df433dd8-2194-48eb-bfc1-26d525d435da	thalllsssss	2025-01-24T17:08:36Z	8	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T16:38:43Z	--- library_name: peft license: apache-2.0 base_model: teknium/OpenHermes-2.5-Mistral-7B tags: - axolotl - generated_from_trainer model-index: - name: df433dd8-2194-48eb-bfc1-26d525d435da results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: teknium/OpenHermes-2.5-Mistral-7B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - defb6f4b19d9c212_train_data.json ds_type: json format: custom path: /workspace/input_data/defb6f4b19d9c212_train_data.json type: field_input: schema field_instruction: query field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: 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: thalllsssss/df433dd8-2194-48eb-bfc1-26d525d435da 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/defb6f4b19d9c212_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: <\|im_end\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 80d8fb58-c71c-4396-8734-552dcb079eba wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 80d8fb58-c71c-4396-8734-552dcb079eba warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # df433dd8-2194-48eb-bfc1-26d525d435da This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2418 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.17 \| 0.0824 \| 200 \| 0.2418 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
minhnguyennnnnn/b733d243-a79a-4af4-8c87-1f693595ce68	minhnguyennnnnn	2025-01-24T17:07:22Z	8	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T16:38:36Z	--- library_name: peft license: apache-2.0 base_model: teknium/OpenHermes-2.5-Mistral-7B tags: - axolotl - generated_from_trainer model-index: - name: b733d243-a79a-4af4-8c87-1f693595ce68 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: teknium/OpenHermes-2.5-Mistral-7B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - defb6f4b19d9c212_train_data.json ds_type: json format: custom path: /workspace/input_data/defb6f4b19d9c212_train_data.json type: field_input: schema field_instruction: query field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: 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: minhnguyennnnnn/b733d243-a79a-4af4-8c87-1f693595ce68 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/defb6f4b19d9c212_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: <\|im_end\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 80d8fb58-c71c-4396-8734-552dcb079eba wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 80d8fb58-c71c-4396-8734-552dcb079eba warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # b733d243-a79a-4af4-8c87-1f693595ce68 This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2418 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1719 \| 0.0824 \| 200 \| 0.2418 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
duyphu/4aed99e3-5b57-4283-ab6d-b673b38a5eb9	duyphu	2025-01-24T17:04:46Z	9	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:TinyLlama/TinyLlama_v1.1", "base_model:adapter:TinyLlama/TinyLlama_v1.1", "license:apache-2.0", "region:us" ]	null	2025-01-24T16:57:46Z	--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama_v1.1 tags: - axolotl - generated_from_trainer model-index: - name: 4aed99e3-5b57-4283-ab6d-b673b38a5eb9 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: TinyLlama/TinyLlama_v1.1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 8412839835a5e4c8_train_data.json ds_type: json format: custom path: /workspace/input_data/8412839835a5e4c8_train_data.json type: field_instruction: prompt field_output: completion format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 5 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: duyphu/4aed99e3-5b57-4283-ab6d-b673b38a5eb9 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 5 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 50 micro_batch_size: 2 mlflow_experiment_name: /tmp/8412839835a5e4c8_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: 11fc7f5c-dcf3-4f4f-8023-8f38c8ae1ae9 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 11fc7f5c-dcf3-4f4f-8023-8f38c8ae1ae9 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 4aed99e3-5b57-4283-ab6d-b673b38a5eb9 This model is a fine-tuned version of [TinyLlama/TinyLlama_v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8983 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0039 \| 1 \| 1.2903 \| \| 1.2628 \| 0.0386 \| 10 \| 1.1766 \| \| 1.0907 \| 0.0773 \| 20 \| 1.0021 \| \| 0.9467 \| 0.1159 \| 30 \| 0.9219 \| \| 0.8976 \| 0.1546 \| 40 \| 0.9010 \| \| 0.8781 \| 0.1932 \| 50 \| 0.8983 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
adammandic87/dc6ce67f-edea-4eec-9121-398d6416781d	adammandic87	2025-01-24T17:02:57Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-135M", "base_model:adapter:unsloth/SmolLM-135M", "license:apache-2.0", "region:us" ]	null	2025-01-24T16:44:38Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-135M tags: - axolotl - generated_from_trainer model-index: - name: dc6ce67f-edea-4eec-9121-398d6416781d results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/SmolLM-135M bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 12918daafc7e29ad_train_data.json ds_type: json format: custom path: /workspace/input_data/12918daafc7e29ad_train_data.json type: field_instruction: premise field_output: entailment 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/dc6ce67f-edea-4eec-9121-398d6416781d hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/12918daafc7e29ad_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: f45cb749-0cc3-4d0c-ab1a-6f0cbfef80b5 wandb_project: Birthday-SN56-13-Gradients-On-Demand wandb_run: your_name wandb_runid: f45cb749-0cc3-4d0c-ab1a-6f0cbfef80b5 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # dc6ce67f-edea-4eec-9121-398d6416781d This model is a fine-tuned version of [unsloth/SmolLM-135M](https://huggingface.co/unsloth/SmolLM-135M) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0000 \| 1 \| nan \| \| 0.0 \| 0.0001 \| 3 \| nan \| \| 0.0 \| 0.0002 \| 6 \| nan \| \| 0.0 \| 0.0003 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/latin_english_translation_model-GGUF	mradermacher	2025-01-24T17:02:34Z	197	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "base_model:Kankanaghosh/latin_english_translation_model", "base_model:quantized:Kankanaghosh/latin_english_translation_model", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-01-24T17:01:59Z	--- base_model: Kankanaghosh/latin_english_translation_model language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Kankanaghosh/latin_english_translation_model <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/latin_english_translation_model-GGUF/resolve/main/latin_english_translation_model.Q2_K.gguf) \| Q2_K \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/latin_english_translation_model-GGUF/resolve/main/latin_english_translation_model.Q3_K_S.gguf) \| Q3_K_S \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/latin_english_translation_model-GGUF/resolve/main/latin_english_translation_model.Q3_K_M.gguf) \| Q3_K_M \| 0.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/latin_english_translation_model-GGUF/resolve/main/latin_english_translation_model.Q3_K_L.gguf) \| Q3_K_L \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/latin_english_translation_model-GGUF/resolve/main/latin_english_translation_model.IQ4_XS.gguf) \| IQ4_XS \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/latin_english_translation_model-GGUF/resolve/main/latin_english_translation_model.Q4_K_S.gguf) \| Q4_K_S \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/latin_english_translation_model-GGUF/resolve/main/latin_english_translation_model.Q4_K_M.gguf) \| Q4_K_M \| 0.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/latin_english_translation_model-GGUF/resolve/main/latin_english_translation_model.Q5_K_S.gguf) \| Q5_K_S \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/latin_english_translation_model-GGUF/resolve/main/latin_english_translation_model.Q5_K_M.gguf) \| Q5_K_M \| 0.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/latin_english_translation_model-GGUF/resolve/main/latin_english_translation_model.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/latin_english_translation_model-GGUF/resolve/main/latin_english_translation_model.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/latin_english_translation_model-GGUF/resolve/main/latin_english_translation_model.f16.gguf) \| f16 \| 0.2 \| 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 -->
robiual-awal/8a3835ec-e768-41ce-ae75-68847c981f37	robiual-awal	2025-01-24T17:02:19Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer", "base_model:adapter:NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer", "license:other", "region:us" ]	null	2025-01-24T17:00:32Z	--- library_name: peft license: other base_model: NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer tags: - axolotl - generated_from_trainer model-index: - name: 8a3835ec-e768-41ce-ae75-68847c981f37 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/Meta-Llama-3-8B-Alternate-Tokenizer bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 7349255a0722d332_train_data.json ds_type: json format: custom path: /workspace/input_data/7349255a0722d332_train_data.json type: field_input: author field_instruction: title field_output: text format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: robiual-awal/8a3835ec-e768-41ce-ae75-68847c981f37 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/7349255a0722d332_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: 13fce366-6808-46f8-8029-0af5c3620a55 wandb_project: Birthday-SN56-29-Gradients-On-Demand wandb_run: your_name wandb_runid: 13fce366-6808-46f8-8029-0af5c3620a55 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 8a3835ec-e768-41ce-ae75-68847c981f37 This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4155 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.511 \| 0.0009 \| 1 \| 2.4499 \| \| 2.3078 \| 0.0026 \| 3 \| 2.4492 \| \| 2.3958 \| 0.0051 \| 6 \| 2.4416 \| \| 2.2283 \| 0.0077 \| 9 \| 2.4155 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
choco58/MistraMystic	choco58	2025-01-24T17:00:14Z	6	3	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "MistraMystic", "Conversational AI", "Personality", "Persona-dialogue", "Dialogue-systems", "Human-like assistant", "Mistral-7B", "Mistral", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-09-17T04:07:16Z	--- library_name: transformers tags: [MistraMystic, Conversational AI, Personality, Persona-dialogue, Dialogue-systems, Human-like assistant, Mistral-7B, Mistral] --- # MistraMystic: Conversational Personality Model 🌊✨ Welcome to MistraMystic—a conversational model fine-tuned from Mistral-7B v0.3, capturing nuanced personality traits that make AI interactions feel more authentic and relatable. Whether it’s about balancing conscientious responses or tapping into empathetic reflections, MistraMystic is here to explore the depths of the human-like personality spectrum. --- ## Model Name: MistraMystic - Architecture: Mistral-7B v0.3 - Training Objective: Personality-Enhanced Conversational AI - Training Dataset: Fine-tuned on conversational data to reflect Big 5 personality traits. - JIC: [Journal Intensive Conversations](https://huggingface.co/datasets/chocokiddo/jic) dataset - Training Duration: 4-5 days on A100 GPU (training parameters can be found in appendix of the paper) --- ## Why "MistraMystic"? The name "MistraMystic" combines the mystique of deep conversation with Mistral's adaptability. Designed to capture the essence of personality through the Big 5 OCEAN traits, MistraMystic works to reflect the nuances of human interactions within its AI responses. The result? A model that speaks with more than just words—it reflects aspects of personality, adding richness and realism to every interaction. --- ## Scope of Applications MistraMystic is crafted for a range of applications where understanding personality-driven conversation is essential. Here’s what it’s especially good for: - Conversational Agents: Engage users with relatable and personality-driven conversations. - Text Generation: Generate human-like text for articles, chats, and creative writing with a personal touch. - Question-Answering: Answer questions with a flair of personality, making responses more relatable. - Educational and Therapy Bots: Assist in applications where personality-sensitive responses can improve user engagement and retention. --- ## Intended Use MistraMystic is built for those aiming to inject personality into conversational systems, whether it’s for customer service bots, therapy support, or just plain fun AI companions. It’s particularly suited to applications where capturing nuances like openness, agreeableness, and neuroticism (yes, even those angsty replies!) can enhance user experience. ### Data and Training The model has been trained on an extensive conversational dataset. Our goal was to align model responses with intrinsic personality traits, enabling MistraMystic to tailor its tone and style depending on conversational context. More information on the dataset will be shared soon. ### Results Personality Evaluation on [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) (OCEAN Personality Benchmark) \| Model \| Description \| Openness \| Conscientiousness \| Extraversion \| Agreeableness \| Neuroticism \| Average \| \|-------------------------\|--------------------------------\|----------\|-------------------\|--------------\|---------------\|-------------\|---------\| \| Mistral 7B v0.3 \| Zero-shot \| 0.8360 \| 0.6390 \| 0.5140 \| 0.8160 \| 0.5350 \| 0.6680 \| \| MistraMystic \| Fine-tuned on Conversational Data \| 0.9340 \| 0.8260 \| 0.6250 \| 0.9530 \| 0.5700 \| 0.7816 \| MistraMystic demonstrates notable improvements across all Big 5 traits. --- ## Performance and Limitations While MistraMystic brings vibrant and personality-driven conversations to the table, it does have limitations: - Personality Representation: MistraMystic is trained for personality alignment, so it may sacrifice some general knowledge capabilities in favor of personality-specific responses. A detailed evaluation will be updated soon. - Sensitive Topics: Despite strong filtering, caution is advised when deploying in high-stakes environments. - Computational Load: The Mistral 7B backbone requires substantial resources, which may limit deployment in real-time settings without sufficient hardware. --- ## Ethical Considerations We made sure to avoid toxic or inappropriate dialogues by tagging any dialogue with over 25% toxic utterances for separate review. Ethical considerations are a priority, and MistraMystic was designed with responsible AI practices in mind. For details on ethical data practices, see the Appendix (coming soon!). --- ## Future Updates Stay tuned for more information on MistraMystic! --- ## Citation ```bibtex @inproceedings{pal-etal-2025-beyond, title = "Beyond Discrete Personas: Personality Modeling Through Journal Intensive Conversations", author = "Pal, Sayantan and Das, Souvik and Srihari, Rohini K.", editor = "Rambow, Owen and Wanner, Leo and Apidianaki, Marianna and Al-Khalifa, Hend and Eugenio, Barbara Di and Schockaert, Steven", booktitle = "Proceedings of the 31st International Conference on Computational Linguistics", month = jan, year = "2025", address = "Abu Dhabi, UAE", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2025.coling-main.470/", pages = "7055--7074", abstract = "Large Language Models (LLMs) have significantly improved personalized conversational capabilities. However, existing datasets like Persona Chat, Synthetic Persona Chat, and Blended Skill Talk rely on static, predefined personas. This approach often results in dialogues that fail to capture human personalities' fluid and evolving nature. To overcome these limitations, we introduce a novel dataset with around 400,000 dialogues and a framework for generating personalized conversations using long-form journal entries from Reddit. Our approach clusters journal entries for each author and filters them by selecting the most representative cluster, ensuring that the retained entries best reflect the author`s personality. We further refine the data by capturing the Big Five personality traits{---}openness, conscientiousness, extraversion, agreeableness, and neuroticism{---}ensuring that dialogues authentically reflect an individual`s personality. Using Llama 3 70B, we generate high-quality, personality-rich dialogues grounded in these journal entries. Fine-tuning models on this dataset leads to an 11{\%} improvement in capturing personality traits on average, outperforming existing approaches in generating more coherent and personality-driven dialogues." } ```
MaziyarPanahi/DeepSeek-R1-Distill-Llama-8B-GGUF	MaziyarPanahi	2025-01-24T17:00:07Z	413	0	null	[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:unsloth/DeepSeek-R1-Distill-Llama-8B", "base_model:quantized:unsloth/DeepSeek-R1-Distill-Llama-8B", "region:us", "conversational" ]	text-generation	2025-01-24T16:39:48Z	--- base_model: unsloth/DeepSeek-R1-Distill-Llama-8B inference: false model_creator: unsloth model_name: DeepSeek-R1-Distill-Llama-8B-GGUF pipeline_tag: text-generation quantized_by: MaziyarPanahi tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation --- # [MaziyarPanahi/DeepSeek-R1-Distill-Llama-8B-GGUF](https://huggingface.co/MaziyarPanahi/DeepSeek-R1-Distill-Llama-8B-GGUF) - Model creator: [unsloth](https://huggingface.co/unsloth) - Original model: [unsloth/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B) ## Description [MaziyarPanahi/DeepSeek-R1-Distill-Llama-8B-GGUF](https://huggingface.co/MaziyarPanahi/DeepSeek-R1-Distill-Llama-8B-GGUF) contains GGUF format model files for [unsloth/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
vermoney/2ca47c1e-0163-4529-95f9-e4e8d71f7c1f	vermoney	2025-01-24T16:59:55Z	8	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	2025-01-24T08:25:45Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-14B tags: - axolotl - generated_from_trainer model-index: - name: 2ca47c1e-0163-4529-95f9-e4e8d71f7c1f 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: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 05a8f2f79a604342_train_data.json ds_type: json format: custom path: /workspace/input_data/05a8f2f79a604342_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: 1 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: vermoney/2ca47c1e-0163-4529-95f9-e4e8d71f7c1f hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 78GiB max_steps: 30 micro_batch_size: 2 mlflow_experiment_name: /tmp/05a8f2f79a604342_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3c0c5817-87f8-4599-ba7a-4d46ec410da6 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 3c0c5817-87f8-4599-ba7a-4d46ec410da6 warmup_steps: 5 weight_decay: 0.001 xformers_attention: true ``` </details><br> # 2ca47c1e-0163-4529-95f9-e4e8d71f7c1f 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: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0000 \| 1 \| nan \| \| 0.0 \| 0.0001 \| 5 \| nan \| \| 0.0 \| 0.0002 \| 10 \| nan \| \| 0.0 \| 0.0003 \| 15 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
hongngo/a72f98c2-87d5-4db6-a416-a77fcf3932f8	hongngo	2025-01-24T16:54:26Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3.5-mini-instruct", "base_model:adapter:unsloth/Phi-3.5-mini-instruct", "license:mit", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T16:33:33Z	--- library_name: peft license: mit base_model: unsloth/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: a72f98c2-87d5-4db6-a416-a77fcf3932f8 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/Phi-3.5-mini-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 14e7f20aa0ab15bf_train_data.json ds_type: json format: custom path: /workspace/input_data/14e7f20aa0ab15bf_train_data.json type: field_input: input field_instruction: system field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: 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/a72f98c2-87d5-4db6-a416-a77fcf3932f8 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/14e7f20aa0ab15bf_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: 5c66e9d0-7fa2-4ea5-83d0-56a779006d22 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 5c66e9d0-7fa2-4ea5-83d0-56a779006d22 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # a72f98c2-87d5-4db6-a416-a77fcf3932f8 This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.7557 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 7.9056 \| 0.0257 \| 200 \| 8.7557 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
tarabukinivan/56a53b9f-ea9f-4381-ba90-8bcc18074866	tarabukinivan	2025-01-24T16:54:20Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3.5-mini-instruct", "base_model:adapter:unsloth/Phi-3.5-mini-instruct", "license:mit", "region:us" ]	null	2025-01-24T16:33:40Z	--- library_name: peft license: mit base_model: unsloth/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: 56a53b9f-ea9f-4381-ba90-8bcc18074866 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/Phi-3.5-mini-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 14e7f20aa0ab15bf_train_data.json ds_type: json format: custom path: /workspace/input_data/14e7f20aa0ab15bf_train_data.json type: field_input: input field_instruction: system field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: null eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: tarabukinivan/56a53b9f-ea9f-4381-ba90-8bcc18074866 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 75GiB max_steps: 30 micro_batch_size: 2 mlflow_experiment_name: /tmp/14e7f20aa0ab15bf_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 15 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 5c66e9d0-7fa2-4ea5-83d0-56a779006d22 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 5c66e9d0-7fa2-4ea5-83d0-56a779006d22 warmup_steps: 15 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 56a53b9f-ea9f-4381-ba90-8bcc18074866 This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 15 - training_steps: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| nan \| \| 0.0 \| 0.0006 \| 5 \| nan \| \| 0.0 \| 0.0013 \| 10 \| nan \| \| 0.0 \| 0.0019 \| 15 \| nan \| \| 0.0 \| 0.0026 \| 20 \| nan \| \| 0.0 \| 0.0032 \| 25 \| nan \| \| 0.0 \| 0.0039 \| 30 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
andreichis/moni-lora	andreichis	2025-01-24T16:52:55Z	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	2025-01-24T16:39:49Z	--- 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: moni --- # Moni Lora <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `moni` 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('andreichis/moni-lora', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
minhtrannnn/c17939c8-0261-4e1e-9629-c501461a4d8f	minhtrannnn	2025-01-24T16:52:40Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3.5-mini-instruct", "base_model:adapter:unsloth/Phi-3.5-mini-instruct", "license:mit", "8-bit", "bitsandbytes", "region:us" ]	null	2025-01-24T16:33:24Z	--- library_name: peft license: mit base_model: unsloth/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: c17939c8-0261-4e1e-9629-c501461a4d8f 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/Phi-3.5-mini-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 14e7f20aa0ab15bf_train_data.json ds_type: json format: custom path: /workspace/input_data/14e7f20aa0ab15bf_train_data.json type: field_input: input field_instruction: system field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: 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: minhtrannnn/c17939c8-0261-4e1e-9629-c501461a4d8f 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/14e7f20aa0ab15bf_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: 5c66e9d0-7fa2-4ea5-83d0-56a779006d22 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 5c66e9d0-7fa2-4ea5-83d0-56a779006d22 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # c17939c8-0261-4e1e-9629-c501461a4d8f This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.7751 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 8.0168 \| 0.0257 \| 200 \| 8.7751 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
vital-ai/watt-tool-70B-awq	vital-ai	2025-01-24T16:52:30Z	2,385	0	null	[ "safetensors", "llama", "quantized", "4bit", "AWQ", "base_model:watt-ai/watt-tool-70B", "base_model:quantized:watt-ai/watt-tool-70B", "license:apache-2.0", "4-bit", "awq", "region:us" ]	null	2025-01-24T02:21:48Z	--- license: apache-2.0 base_model: watt-ai/watt-tool-70B tags: - quantized - 4bit - AWQ --- # Model Card: `vital-ai/watt-tool-70B-awq` ## Model Description This model, `vital-ai/watt-tool-70B-awq`, is a quantized version of the base model [`watt-ai/watt-tool-70B`](https://huggingface.co/watt-ai/watt-tool-70B). The quantization process was performed to reduce the model size and improve inference speed while maintaining high performance. Base Model: [`watt-ai/watt-tool-70B`](https://huggingface.co/watt-ai/watt-tool-70B) Quantization Method: 4-bit AWQ
aleegis11/52884f00-e251-4d0c-897d-b76e09c9846a	aleegis11	2025-01-24T16:51:51Z	8	0	peft	[ "peft", "safetensors", "phi3", "axolotl", "generated_from_trainer", "custom_code", "base_model:numind/NuExtract-1.5", "base_model:adapter:numind/NuExtract-1.5", "license:mit", "region:us" ]	null	2025-01-24T16:24:43Z	--- library_name: peft license: mit base_model: numind/NuExtract-v1.5 tags: - axolotl - generated_from_trainer model-index: - name: 52884f00-e251-4d0c-897d-b76e09c9846a results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: numind/NuExtract-v1.5 bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - dc2351e325261d28_train_data.json ds_type: json format: custom path: /workspace/input_data/dc2351e325261d28_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: 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: aleegis11/52884f00-e251-4d0c-897d-b76e09c9846a 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/dc2351e325261d28_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: 0f586585-730a-40cb-960a-9745f62e3dd1 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 0f586585-730a-40cb-960a-9745f62e3dd1 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 52884f00-e251-4d0c-897d-b76e09c9846a This model is a fine-tuned version of [numind/NuExtract-v1.5](https://huggingface.co/numind/NuExtract-v1.5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9382 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.8266 \| 0.0007 \| 1 \| 1.2865 \| \| 4.4222 \| 0.0338 \| 50 \| 1.0381 \| \| 5.2868 \| 0.0677 \| 100 \| 0.9709 \| \| 4.2989 \| 0.1015 \| 150 \| 0.9399 \| \| 4.8094 \| 0.1354 \| 200 \| 0.9382 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
prxy5606/98312ef8-ef70-4d8c-8efd-b3273962b4d9	prxy5606	2025-01-24T16:51:22Z	16	0	peft	[ "peft", "safetensors", "phi", "axolotl", "generated_from_trainer", "base_model:echarlaix/tiny-random-PhiForCausalLM", "base_model:adapter:echarlaix/tiny-random-PhiForCausalLM", "license:apache-2.0", "region:us" ]	null	2025-01-24T16:49:25Z	--- library_name: peft license: apache-2.0 base_model: echarlaix/tiny-random-PhiForCausalLM tags: - axolotl - generated_from_trainer model-index: - name: 98312ef8-ef70-4d8c-8efd-b3273962b4d9 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: echarlaix/tiny-random-PhiForCausalLM bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 764a1f78f3b0faa6_train_data.json ds_type: json format: custom path: /workspace/input_data/764a1f78f3b0faa6_train_data.json type: field_input: rational_answer field_instruction: question field_output: answer format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: prxy5606/98312ef8-ef70-4d8c-8efd-b3273962b4d9 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/764a1f78f3b0faa6_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: 253a846d-0a75-4cca-b4ab-eb033fd80f91 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 253a846d-0a75-4cca-b4ab-eb033fd80f91 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 98312ef8-ef70-4d8c-8efd-b3273962b4d9 This model is a fine-tuned version of [echarlaix/tiny-random-PhiForCausalLM](https://huggingface.co/echarlaix/tiny-random-PhiForCausalLM) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.8340 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 6.9385 \| 0.0003 \| 1 \| 6.9434 \| \| 6.8661 \| 0.0166 \| 50 \| 6.8642 \| \| 6.8282 \| 0.0333 \| 100 \| 6.8437 \| \| 6.8196 \| 0.0499 \| 150 \| 6.8352 \| \| 6.8165 \| 0.0666 \| 200 \| 6.8340 \| ### 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/b12d83a5-28b9-4365-9fbd-5b9e68c2da04	robiual-awal	2025-01-24T16:50:33Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Hermes-3-Llama-3.1-8B", "base_model:adapter:unsloth/Hermes-3-Llama-3.1-8B", "region:us" ]	null	2025-01-24T16:48:30Z	--- library_name: peft base_model: unsloth/Hermes-3-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: b12d83a5-28b9-4365-9fbd-5b9e68c2da04 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/Hermes-3-Llama-3.1-8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 7498d1d10e472fec_train_data.json ds_type: json format: custom path: /workspace/input_data/7498d1d10e472fec_train_data.json type: field_instruction: title field_output: text 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: robiual-awal/b12d83a5-28b9-4365-9fbd-5b9e68c2da04 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/7498d1d10e472fec_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: 145b78ab-4197-45e4-b32d-0f5f5e9a56ab wandb_project: Birthday-SN56-29-Gradients-On-Demand wandb_run: your_name wandb_runid: 145b78ab-4197-45e4-b32d-0f5f5e9a56ab warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # b12d83a5-28b9-4365-9fbd-5b9e68c2da04 This model is a fine-tuned version of [unsloth/Hermes-3-Llama-3.1-8B](https://huggingface.co/unsloth/Hermes-3-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0009 \| 1 \| nan \| \| 0.0 \| 0.0026 \| 3 \| nan \| \| 0.0 \| 0.0052 \| 6 \| nan \| \| 0.0 \| 0.0077 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
daniel40/c36e98e9-29b0-4860-a7bb-079bd6e9b109	daniel40	2025-01-24T16:50:24Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3.5-mini-instruct", "base_model:adapter:unsloth/Phi-3.5-mini-instruct", "license:mit", "region:us" ]	null	2025-01-24T16:45:01Z	--- library_name: peft license: mit base_model: unsloth/Phi-3.5-mini-instruct tags: - axolotl - generated_from_trainer model-index: - name: c36e98e9-29b0-4860-a7bb-079bd6e9b109 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/Phi-3.5-mini-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 14e7f20aa0ab15bf_train_data.json ds_type: json format: custom path: /workspace/input_data/14e7f20aa0ab15bf_train_data.json type: field_input: input field_instruction: system field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 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/c36e98e9-29b0-4860-a7bb-079bd6e9b109 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/14e7f20aa0ab15bf_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: 5c66e9d0-7fa2-4ea5-83d0-56a779006d22 wandb_project: Birthday-SN56-28-Gradients-On-Demand wandb_run: your_name wandb_runid: 5c66e9d0-7fa2-4ea5-83d0-56a779006d22 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # c36e98e9-29b0-4860-a7bb-079bd6e9b109 This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.0 \| 0.0001 \| 1 \| nan \| \| 0.0 \| 0.0004 \| 3 \| nan \| \| 0.0 \| 0.0008 \| 6 \| nan \| \| 0.0 \| 0.0012 \| 9 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/DeepSeek-R1-MFANN-TIES-unretrained-7b-GGUF	mradermacher	2025-01-24T16:43:56Z	776	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:netcat420/DEFUNCT-EXPERIMENT2_2", "base_model:quantized:netcat420/DEFUNCT-EXPERIMENT2_2", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-22T14:49:23Z	--- base_model: netcat420/DEFUNCT-EXPERIMENT2_2 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/netcat420/DEFUNCT-EXPERIMENT2_2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-MFANN-TIES-unretrained-7b-GGUF/resolve/main/DeepSeek-R1-MFANN-TIES-unretrained-7b.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-MFANN-TIES-unretrained-7b-GGUF/resolve/main/DeepSeek-R1-MFANN-TIES-unretrained-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-MFANN-TIES-unretrained-7b-GGUF/resolve/main/DeepSeek-R1-MFANN-TIES-unretrained-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-MFANN-TIES-unretrained-7b-GGUF/resolve/main/DeepSeek-R1-MFANN-TIES-unretrained-7b.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-MFANN-TIES-unretrained-7b-GGUF/resolve/main/DeepSeek-R1-MFANN-TIES-unretrained-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-MFANN-TIES-unretrained-7b-GGUF/resolve/main/DeepSeek-R1-MFANN-TIES-unretrained-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-MFANN-TIES-unretrained-7b-GGUF/resolve/main/DeepSeek-R1-MFANN-TIES-unretrained-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-MFANN-TIES-unretrained-7b-GGUF/resolve/main/DeepSeek-R1-MFANN-TIES-unretrained-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-MFANN-TIES-unretrained-7b-GGUF/resolve/main/DeepSeek-R1-MFANN-TIES-unretrained-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-MFANN-TIES-unretrained-7b-GGUF/resolve/main/DeepSeek-R1-MFANN-TIES-unretrained-7b.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-MFANN-TIES-unretrained-7b-GGUF/resolve/main/DeepSeek-R1-MFANN-TIES-unretrained-7b.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-MFANN-TIES-unretrained-7b-GGUF/resolve/main/DeepSeek-R1-MFANN-TIES-unretrained-7b.f16.gguf) \| f16 \| 15.3 \| 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/Qwen2.5-DeepSeek-R1-MFANN-7b-GGUF	mradermacher	2025-01-24T16:43:23Z	848	0	transformers	[ "transformers", "gguf", "en", "base_model:netcat420/DEFUNCT-EXPERIMENT2", "base_model:quantized:netcat420/DEFUNCT-EXPERIMENT2", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-23T05:35:04Z	--- base_model: netcat420/DEFUNCT-EXPERIMENT2 language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/netcat420/DEFUNCT-EXPERIMENT2 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-DeepSeek-R1-MFANN-7b-GGUF/resolve/main/Qwen2.5-DeepSeek-R1-MFANN-7b.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-DeepSeek-R1-MFANN-7b-GGUF/resolve/main/Qwen2.5-DeepSeek-R1-MFANN-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-DeepSeek-R1-MFANN-7b-GGUF/resolve/main/Qwen2.5-DeepSeek-R1-MFANN-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-DeepSeek-R1-MFANN-7b-GGUF/resolve/main/Qwen2.5-DeepSeek-R1-MFANN-7b.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-DeepSeek-R1-MFANN-7b-GGUF/resolve/main/Qwen2.5-DeepSeek-R1-MFANN-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-DeepSeek-R1-MFANN-7b-GGUF/resolve/main/Qwen2.5-DeepSeek-R1-MFANN-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-DeepSeek-R1-MFANN-7b-GGUF/resolve/main/Qwen2.5-DeepSeek-R1-MFANN-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-DeepSeek-R1-MFANN-7b-GGUF/resolve/main/Qwen2.5-DeepSeek-R1-MFANN-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-DeepSeek-R1-MFANN-7b-GGUF/resolve/main/Qwen2.5-DeepSeek-R1-MFANN-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-DeepSeek-R1-MFANN-7b-GGUF/resolve/main/Qwen2.5-DeepSeek-R1-MFANN-7b.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-DeepSeek-R1-MFANN-7b-GGUF/resolve/main/Qwen2.5-DeepSeek-R1-MFANN-7b.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-DeepSeek-R1-MFANN-7b-GGUF/resolve/main/Qwen2.5-DeepSeek-R1-MFANN-7b.f16.gguf) \| f16 \| 15.3 \| 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 -->
safe-models/RMVPE	safe-models	2025-01-24T16:42:58Z	47	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-01-24T16:42:30Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
mradermacher/Quasar-1.5-Pro-GGUF	mradermacher	2025-01-24T16:42:41Z	339	0	transformers	[ "transformers", "gguf", "en", "base_model:silx-ai/Quasar-1.5-Pro", "base_model:quantized:silx-ai/Quasar-1.5-Pro", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-23T15:32:53Z	--- base_model: silx-ai/Quasar-1.5-Pro language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/silx-ai/Quasar-1.5-Pro <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Quasar-1.5-Pro-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/Quasar-1.5-Pro-GGUF/resolve/main/Quasar-1.5-Pro.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Quasar-1.5-Pro-GGUF/resolve/main/Quasar-1.5-Pro.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Quasar-1.5-Pro-GGUF/resolve/main/Quasar-1.5-Pro.Q3_K_M.gguf) \| Q3_K_M \| 16.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Quasar-1.5-Pro-GGUF/resolve/main/Quasar-1.5-Pro.Q3_K_L.gguf) \| Q3_K_L \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Quasar-1.5-Pro-GGUF/resolve/main/Quasar-1.5-Pro.IQ4_XS.gguf) \| IQ4_XS \| 18.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Quasar-1.5-Pro-GGUF/resolve/main/Quasar-1.5-Pro.Q4_K_S.gguf) \| Q4_K_S \| 18.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Quasar-1.5-Pro-GGUF/resolve/main/Quasar-1.5-Pro.Q4_K_M.gguf) \| Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Quasar-1.5-Pro-GGUF/resolve/main/Quasar-1.5-Pro.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Quasar-1.5-Pro-GGUF/resolve/main/Quasar-1.5-Pro.Q5_K_M.gguf) \| Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Quasar-1.5-Pro-GGUF/resolve/main/Quasar-1.5-Pro.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Quasar-1.5-Pro-GGUF/resolve/main/Quasar-1.5-Pro.Q8_0.gguf) \| Q8_0 \| 34.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF	mradermacher	2025-01-24T16:42:36Z	3,821	1	transformers	[ "transformers", "gguf", "en", "base_model:thirdeyeai/DeepSeek-R1-Distill-Qwen-1.5B-uncensored", "base_model:quantized:thirdeyeai/DeepSeek-R1-Distill-Qwen-1.5B-uncensored", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-01-23T16:14:06Z	--- base_model: thirdeyeai/DeepSeek-R1-Distill-Qwen-1.5B-uncensored language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/thirdeyeai/DeepSeek-R1-Distill-Qwen-1.5B-uncensored <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-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/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.6 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.8 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-Q2_K.gguf) \| i1-Q2_K \| 0.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 1.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-IQ3_S.gguf) \| i1-IQ3_S \| 1.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-IQ3_M.gguf) \| i1-IQ3_M \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 1.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 1.1 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 1.2 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-Q4_0.gguf) \| i1-Q4_0 \| 1.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 1.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-Q4_1.gguf) \| i1-Q4_1 \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-uncensored-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-uncensored.i1-Q6_K.gguf) \| i1-Q6_K \| 1.6 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/Calcium-Opus-20B-v1-i1-GGUF	mradermacher	2025-01-24T16:42:23Z	739	0	transformers	[ "transformers", "gguf", "Opus", "text-generation-inference", "en", "base_model:prithivMLmods/Calcium-Opus-20B-v1", "base_model:quantized:prithivMLmods/Calcium-Opus-20B-v1", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-01-23T17:33:30Z	--- base_model: prithivMLmods/Calcium-Opus-20B-v1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - Opus - text-generation-inference --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/prithivMLmods/Calcium-Opus-20B-v1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-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/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 4.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 5.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 7.0 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-Q2_K.gguf) \| i1-Q2_K \| 7.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 7.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 8.7 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 8.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 9.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 10.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-Q4_0.gguf) \| i1-Q4_0 \| 11.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 11.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 11.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-Q4_1.gguf) \| i1-Q4_1 \| 12.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 13.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 13.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Calcium-Opus-20B-v1-i1-GGUF/resolve/main/Calcium-Opus-20B-v1.i1-Q6_K.gguf) \| i1-Q6_K \| 15.8 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

Subsets and Splits