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ArchiveStudio
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ArchiveStudio
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Model Developer: Muse Research Lab
Model Architecture: Muse-3B 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-3B | ~3B | Multilingual text | Multilingual text and code | 8,192 tokens | Yes | Yes | Not specified |
Supported Languages: English, German, and French.
Status: This is an early compact chat model intended for lightweight assistant-style use and experimentation.
bash
pip install "transformers>=4.43.0" accelerate torchpython
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer MODEL_ID = "Muse-research/Muse-3B" 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-3B, 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)Muse-3B is intended for lightweight assistant-style use, including chat, drafting, summarization, simple programming support, and English/German/French/Italian/Spanish/Portuguese everyday help.
Muse-3B 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.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer MODEL_ID = "Muse-research/Muse-3B" 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-3B, 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)