Instructions to use NightBaRron1412/deepparse-r1-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use NightBaRron1412/deepparse-r1-8b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Llama-8B") model = PeftModel.from_pretrained(base_model, "NightBaRron1412/deepparse-r1-8b") - Notebooks
- Google Colab
- Kaggle
Download deepparse_finetune_config.json from NightBaRron1412/deepparse-r1-8b: direct link, hf CLI and curl.
- Browser
- Download file 598 Bytes
-
https://huggingface.co/NightBaRron1412/deepparse-r1-8b/resolve/main/deepparse_finetune_config.json
- Command line
-
hf download hf://NightBaRron1412/deepparse-r1-8b/deepparse_finetune_config.json
-
curl -L -o deepparse_finetune_config.json https://huggingface.co/NightBaRron1412/deepparse-r1-8b/resolve/main/deepparse_finetune_config.json
598 Bytes
| { | |
| "train_file": "artifacts/training/train_paper.jsonl", | |
| "output_dir": "artifacts/checkpoints/deepparse-r1-8b", | |
| "model_name": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", | |
| "epochs": 25, | |
| "learning_rate": 0.0002, | |
| "batch_size": 8, | |
| "grad_accum": 4, | |
| "lora_rank": 8, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.01, | |
| "max_length": 512, | |
| "bf16": false, | |
| "attn_implementation": "eager", | |
| "seed": 1337, | |
| "eval_split": 0.0, | |
| "save_steps": 200, | |
| "log_steps": 25, | |
| "target_modules": [ | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| "o_proj", | |
| "gate_proj", | |
| "up_proj", | |
| "down_proj" | |
| ] | |
| } |