---
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
  - muse
  - chat
  - multilingual
  - text-generation
language:
  - en
  - de
  - fr
  - it
  - es
  - pt
---

# Muse-1B

Muse-1B is a compact chat language model from **Muse Research Lab**. It is built for helpful everyday conversation, writing, simple coding help, multilingual assistance, and safe general-purpose responses.

## Model Details

**Model Developer:** Muse Research Lab

**Model Architecture:** Muse-1B is an auto-regressive, Llama-style decoder-only transformer optimized for compact chat and general assistance.

| Model | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Knowledge cutoff |
| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
| Muse-1B | ~1B | Multilingual text | Multilingual text and code | 8,192 tokens | Yes | Yes | Not specified |

**Supported Languages:** English, German, French, Italian, Spanish, and Portuguese.

**Status:** This is a compact chat model intended for lightweight assistant-style use.


## Capabilities

- General chat and question answering
- Writing, brainstorming, and rewriting
- Simple coding help and explanations
- Multilingual responses in English, German, French, Italian, Spanish, and Portuguese
- Safe refusal behavior for harmful requests

## Quickstart

```bash
pip install "transformers>=4.43.0" accelerate torch
```

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "muse/Muse-1B"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are Muse-1B, a helpful chat assistant from Muse Research Lab."},
    {"role": "user", "content": "Hi, who are you?"},
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.inference_mode():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
    )

response = tokenizer.decode(output_ids[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
print(response)
```

## Intended Use

Muse-1B is intended for lightweight assistant-style use, including chat, drafting, summarization, simple programming support, and multilingual everyday help.

## Limitations

- May produce incorrect or incomplete answers.
- May struggle with advanced reasoning, long coding tasks, or highly specialized domains.
- Should not be used as the only source for medical, legal, financial, or safety-critical decisions.
- Applications should add their own safeguards when deployed to users.

## Safety

Muse-1B is designed to be helpful while refusing clearly harmful requests. For production use, pair the model with application-level safety checks, monitoring, and domain-specific policies.

---

<div align="center">
  <sub>Built by Muse Research Lab</sub>
</div>