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Usage
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
model_id = "DeepMount00/Murai-350M-v0.1-beta"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
prompt = [{'role': 'user', 'content': """Scrivi una funzione python che somma due numeri"""}]
inputs = tokenizer.apply_chat_template(
prompt,
add_generation_prompt=True,
return_tensors='pt'
)
tokens = model.generate(
inputs.to(model.device),
max_new_tokens=256,
temperature=0.1,
repetition_penalty=1.2,
do_sample=True
)
print(tokenizer.decode(tokens[0], skip_special_tokens=False))
Citation
@misc{deepmount_llm_2024,
title={Deep LLM: A 350M Parameter Language Model with 42 Layers},
author={MicheleMontebovi},
year={2025},
url={https://huggingface.co/DeepMount00/Murai-350M-v0.1-beta}
}
License
Apache 2.0