Instructions to use mii-llm/maestrale-chat-v0.3-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mii-llm/maestrale-chat-v0.3-beta with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mii-llm/maestrale-chat-v0.3-beta")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mii-llm/maestrale-chat-v0.3-beta")
model = AutoModelForCausalLM.from_pretrained("mii-llm/maestrale-chat-v0.3-beta")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mii-llm/maestrale-chat-v0.3-beta 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-v0.3-beta"
# 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-v0.3-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mii-llm/maestrale-chat-v0.3-beta

SGLang

How to use mii-llm/maestrale-chat-v0.3-beta 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-v0.3-beta" \
    --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-v0.3-beta",
		"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-v0.3-beta" \
        --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-v0.3-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mii-llm/maestrale-chat-v0.3-beta with Docker Model Runner:
```
docker model run hf.co/mii-llm/maestrale-chat-v0.3-beta
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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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.

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for mii-llm/maestrale-chat-v0.3-beta

Quantizations

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