Instructions to use mimicheng/mistral-7b-sft-qlora-2ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mimicheng/mistral-7b-sft-qlora-2ep with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "mimicheng/mistral-7b-sft-qlora-2ep") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from mimicheng/mistral-7b-sft-qlora-2ep: direct link, hf CLI and curl.
- Browser
- Download file 363 Bytes
-
https://huggingface.co/mimicheng/mistral-7b-sft-qlora-2ep/resolve/73e233a74ce48df7648951a2ce89360ef8c4bacf/all_results.json
- Command line
-
hf download hf://mimicheng/mistral-7b-sft-qlora-2ep@73e233a74ce48df7648951a2ce89360ef8c4bacf/all_results.json
-
curl -L -o all_results.json https://huggingface.co/mimicheng/mistral-7b-sft-qlora-2ep/resolve/73e233a74ce48df7648951a2ce89360ef8c4bacf/all_results.json
363 Bytes
| { | |
| "epoch": 2.0, | |
| "eval_loss": 4.468899726867676, | |
| "eval_runtime": 1139.7383, | |
| "eval_samples": 23110, | |
| "eval_samples_per_second": 13.539, | |
| "eval_steps_per_second": 0.424, | |
| "train_loss": 4.463997247680382, | |
| "train_runtime": 72530.8249, | |
| "train_samples": 207865, | |
| "train_samples_per_second": 3.845, | |
| "train_steps_per_second": 0.12 | |
| } |