metadata
language:
- it
license: cc-by-nc-4.0
tags:
- sft
- it
- mistral
- chatml
- axolotl
- kpo
prompt_template: >-
<|im_start|>system {system_message}<|im_end|> <|im_start|>user
{prompt}<|im_end|> <|im_start|>assistant
model-index:
- name: maestrale-chat-v0.3-beta
results: []
Maestrale chat beta ༄
By @efederici and @mferraretto
Model description
- Language Model: Mistral-7b for the Italian language, continued pre-training for Italian on a curated large-scale high-quality corpus.
- Fine-Tuning: SFT performed on convs/instructions for three epochs.
- KTO: Aligned with KTO.
v0.3
- Function calling
- Reduced default system prompt to avoid wasting tokens (pre-alignment)
This model uses ChatML prompt format:
<|im_start|>system
Sei un assistente utile.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
Usage:
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
GenerationConfig,
TextStreamer
)
import torch
tokenizer = AutoTokenizer.from_pretrained("mii-llm/maestrale-chat-v0.3-beta")
model = AutoModelForCausalLM.from_pretrained("mii-llm/maestrale-chat-v0.3-beta", load_in_8bit=True, device_map="auto")
gen = GenerationConfig(
do_sample=True,
temperature=0.7,
repetition_penalty=1.2,
top_k=50,
top_p=0.95,
max_new_tokens=500,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.convert_tokens_to_ids("<|im_end|>")
)
streamer = TextStreamer(tokenizer, skip_prompt=True)
messages = [
{"role": "system", "content": "Sei un assistente utile."},
{"role": "user", "content": "{prompt}"}
]
with torch.no_grad():
temp = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(temp, return_tensors="pt").to("cuda")
_ = model.generate(
**inputs,
streamer=streamer,
generation_config=gen
)
Intended uses & limitations
It's a beta version, but it's not safe.
