Instructions to use NANI-Nithin/MiniCPM5-2B-OpenCodeReasoning2-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use NANI-Nithin/MiniCPM5-2B-OpenCodeReasoning2-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM5-2B") model = PeftModel.from_pretrained(base_model, "NANI-Nithin/MiniCPM5-2B-OpenCodeReasoning2-LoRA") - Notebooks
- Google Colab
- Kaggle
Download train_stats.json from NANI-Nithin/MiniCPM5-2B-OpenCodeReasoning2-LoRA: direct link, hf CLI and curl.
- Browser
- Download file 83 Bytes
-
https://huggingface.co/NANI-Nithin/MiniCPM5-2B-OpenCodeReasoning2-LoRA/resolve/main/train_stats.json
- Command line
-
hf download hf://NANI-Nithin/MiniCPM5-2B-OpenCodeReasoning2-LoRA/train_stats.json
-
curl -L -o train_stats.json https://huggingface.co/NANI-Nithin/MiniCPM5-2B-OpenCodeReasoning2-LoRA/resolve/main/train_stats.json
83 Bytes
| { | |
| "train_rows": 50000, | |
| "base_model": "openbmb/MiniCPM5-2B", | |
| "seq_len": 4096 | |
| } |