Instructions to use Likich/open-coding-mistral7b-single_code-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Likich/open-coding-mistral7b-single_code-qlora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") model = PeftModel.from_pretrained(base_model, "Likich/open-coding-mistral7b-single_code-qlora") - Notebooks
- Google Colab
- Kaggle
Download verification_summary.json from Likich/open-coding-mistral7b-single_code-qlora: direct link, hf CLI and curl.
- Browser
- Download file 423 Bytes
-
https://huggingface.co/Likich/open-coding-mistral7b-single_code-qlora/resolve/main/verification_summary.json
- Command line
-
hf download hf://Likich/open-coding-mistral7b-single_code-qlora/verification_summary.json
-
curl -L -o verification_summary.json https://huggingface.co/Likich/open-coding-mistral7b-single_code-qlora/resolve/main/verification_summary.json
423 Bytes
| { | |
| "n_test": 100, | |
| "valid_json_rate": 1.0, | |
| "nonempty_rate": 1.0, | |
| "exact_set_match": 0.2, | |
| "mean_set_f1": 0.2, | |
| "average_codes": 1.0, | |
| "model_name": "mistral7b", | |
| "base_model": "mistralai/Mistral-7B-Instruct-v0.2", | |
| "adapter": "artifacts/mistral7b", | |
| "task_mode": "single_code", | |
| "thresholds": { | |
| "min_valid_json_rate": 0.9, | |
| "min_nonempty_rate": 0.95, | |
| "max_average_codes": 3.0 | |
| }, | |
| "passed": true | |
| } |