Text Generation
Transformers
Safetensors
Italian
mistral
sft
chatml
conversational
text-generation-inference
Instructions to use mii-llm/maestrale-chat-v0.2-alpha-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mii-llm/maestrale-chat-v0.2-alpha-sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mii-llm/maestrale-chat-v0.2-alpha-sft") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mii-llm/maestrale-chat-v0.2-alpha-sft") model = AutoModelForCausalLM.from_pretrained("mii-llm/maestrale-chat-v0.2-alpha-sft", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mii-llm/maestrale-chat-v0.2-alpha-sft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mii-llm/maestrale-chat-v0.2-alpha-sft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mii-llm/maestrale-chat-v0.2-alpha-sft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mii-llm/maestrale-chat-v0.2-alpha-sft
- SGLang
How to use mii-llm/maestrale-chat-v0.2-alpha-sft with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "mii-llm/maestrale-chat-v0.2-alpha-sft" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mii-llm/maestrale-chat-v0.2-alpha-sft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "mii-llm/maestrale-chat-v0.2-alpha-sft" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mii-llm/maestrale-chat-v0.2-alpha-sft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mii-llm/maestrale-chat-v0.2-alpha-sft with Docker Model Runner:
docker model run hf.co/mii-llm/maestrale-chat-v0.2-alpha-sft
Maestrale chat alpha ༄
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 ~270k Italian convs/instructions for one epoch.
This model uses ChatML prompt format:
<|im_start|>system
Assisti sempre con cura, rispetto e verità. Rispondi con la massima utilità ma in modo sicuro. Evita contenuti dannosi, non etici, pregiudizievoli o negativi. Assicurati che le risposte promuovano equità e positività.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
Usage:
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
GenerationConfig,
TextStreamer
)
import torch
torch.backends.cuda.matmul.allow_tf32 = True
tokenizer = AutoTokenizer.from_pretrained("mii-llm/maestrale-chat-v0.2-alpha")
model = AutoModelForCausalLM.from_pretrained("mii-llm/maestrale-chat-v0.2-alpha", 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|>")
)
messages = [
{"role": "system", "content": "Assisti sempre con cura, rispetto e verità. Rispondi con la massima utilità ma in modo sicuro. Evita contenuti dannosi, non etici, pregiudizievoli o negativi. Assicurati che le risposte promuovano equità e positività."},
{"role": "user", "content": "{prompt}"}
]
with torch.no_grad(), torch.backends.cuda.sdp_kernel(
enable_flash=True,
enable_math=False,
enable_mem_efficient=False
):
temp = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(temp, return_tensors="pt").to("cuda")
streamer = TextStreamer(tokenizer, skip_prompt=True)
_ = model.generate(
**inputs,
streamer=streamer,
generation_config=gen
)
Intended uses & limitations
It's an alpha version, it's not aligned. We are working on alignment data and evals.
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