Downloads · 30 days
1.5K
3% of all-time downloads
mii-llm/maestrale-chat-v0.4-alpha-sft
maestrale-chat-v0.4-alpha-sft is a text generation model from mii-llm. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as cc-by-nc-4.0.
<div style="width: auto; margin-left: auto; margin-right: auto" <img src="https://i.imgur.com/CQc6d7W.jpeg" alt="Mii-LLM" style="width: 100%; min-width: 400px; display: block; margin: auto;" </div <div style="display:…
Downloads · 30 days
1.5K
3% of all-time downloads
All-time downloads
53.4K
Public
Parameters
7.2B
14.5 GB on disk
Likes
4
Public
Click a slice to open those files.
.safetensors14.5 GB · 100%
From the Hugging Face model README
By @efederici and @mferraretto
v0.4
This model uses ChatML prompt format:
<|im_start|>system
Sei un assistente utile.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| hellaswag_it | 1 | none | 0 | acc | 0.5220 | ± | 0.0052 |
| none | 0 | acc_norm | 0.6887 | ± | 0.0048 | ||
| arc_it | 1 | none | 0 | acc | 0.1762 | ± | 0.0111 |
| none | 0 | acc_norm | 0.5090 | ± | 0.0146 | ||
| m_mmlu_it | 0 | none | 5 | acc | 0.569 | ± | 0.0043 |
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
GenerationConfig,
TextStreamer
)
import torch
tokenizer = AutoTokenizer.from_pretrained("mii-llm/maestrale-chat-v0.4-alpha-sft")
model = AutoModelForCausalLM.from_pretrained("mii-llm/maestrale-chat-v0.4-alpha-sft", 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
)
It's an alpha version; it's not safe, but it can refuse to answer.