Instructions to use DeepMount00/Murai-350M-v0.1-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeepMount00/Murai-350M-v0.1-beta with Transformers:

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

pipe = pipeline("text-generation", model="DeepMount00/Murai-350M-v0.1-beta", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("DeepMount00/Murai-350M-v0.1-beta", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DeepMount00/Murai-350M-v0.1-beta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DeepMount00/Murai-350M-v0.1-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": "DeepMount00/Murai-350M-v0.1-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DeepMount00/Murai-350M-v0.1-beta

SGLang

How to use DeepMount00/Murai-350M-v0.1-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 "DeepMount00/Murai-350M-v0.1-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": "DeepMount00/Murai-350M-v0.1-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 "DeepMount00/Murai-350M-v0.1-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": "DeepMount00/Murai-350M-v0.1-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DeepMount00/Murai-350M-v0.1-beta with Docker Model Runner:
```
docker model run hf.co/DeepMount00/Murai-350M-v0.1-beta
```

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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

Downloads last month: -

Safetensors

Model size

0.4B params

Tensor type

BF16

DeepMount00
/

Murai-350M-v0.1-beta

You need to agree to share your contact information to access this model

Usage

Citation

License

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