|
--- |
|
language: |
|
- en |
|
- fr |
|
license: apache-2.0 |
|
tags: |
|
- text-generation-inference |
|
- transformers |
|
- unsloth |
|
- mistral |
|
- trl |
|
- sft |
|
base_model: unsloth/mistral-7b-v0.3 |
|
datasets: |
|
- jpacifico/French-Alpaca-dataset-Instruct-110K |
|
--- |
|
|
|
# Uploaded model |
|
|
|
- **Developed by:** AdrienB134 |
|
- **License:** apache-2.0 |
|
- **Finetuned from model :** unsloth/mistral-7b-v0.3 |
|
|
|
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) |
|
|
|
# How to use |
|
|
|
```python |
|
from unsloth import FastLanguageModel |
|
import torch |
|
|
|
max_seq_length = 32_768 # Choose any! We auto support RoPE Scaling internally! |
|
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+ |
|
load_in_4bit = False # Use 4bit quantization to reduce memory usage. Can be True. |
|
|
|
|
|
model, tokenizer = FastLanguageModel.from_pretrained( |
|
model_name = "AdrienB134/French-Alpaca-Mistral-7B-v0.3", |
|
max_seq_length = max_seq_length, |
|
dtype = dtype, |
|
load_in_4bit = load_in_4bit, |
|
# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf |
|
) |
|
|
|
alpaca_prompt = """Ci-dessous tu trouveras une instruction qui décrit une tâche, accompagnée d'un contexte qui donne plus d'informations. Ecrit une réponse appropriée à l'instruction. |
|
### Instruction: |
|
{} |
|
|
|
### Contexte: |
|
{} |
|
|
|
### Response: |
|
{}""" |
|
|
|
FastLanguageModel.for_inference(model) # Enable native 2x faster inference |
|
inputs = tokenizer( |
|
[ |
|
alpaca_prompt.format( |
|
"Continue la série de fibonacci.", # instruction |
|
"1, 1, 2, 3, 5, 8", # contexte |
|
"", # output - leave this blank for generation! |
|
) |
|
], return_tensors = "pt").to("cuda") |
|
|
|
from transformers import TextStreamer |
|
text_streamer = TextStreamer(tokenizer) |
|
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128) |
|
``` |