Instructions to use GautierP/MiniLLM-40M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GautierP/MiniLLM-40M with Transformers:

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

pipe = pipeline("text-generation", model="GautierP/MiniLLM-40M", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("GautierP/MiniLLM-40M", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GautierP/MiniLLM-40M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GautierP/MiniLLM-40M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GautierP/MiniLLM-40M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GautierP/MiniLLM-40M

SGLang

How to use GautierP/MiniLLM-40M 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 "GautierP/MiniLLM-40M" \
    --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": "GautierP/MiniLLM-40M",
		"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 "GautierP/MiniLLM-40M" \
        --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": "GautierP/MiniLLM-40M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GautierP/MiniLLM-40M with Docker Model Runner:
```
docker model run hf.co/GautierP/MiniLLM-40M
```

MiniLLM-40M / config.json

GautierP

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{
  "architectures": [
    "MiniLlmForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "configuration_mini_llm.MiniLlmConfig",
    "AutoModelForCausalLM": "modeling_mini_llm.MiniLlmForCausalLM"
  },
  "dtype": "float32",
  "eos_token_id": 0,
  "hidden_size": 512,
  "intermediate_size": 1408,
  "max_position_embeddings": 1024,
  "model_type": "mini_llm",
  "num_attention_heads": 8,
  "num_hidden_layers": 8,
  "pad_token_id": 0,
  "rms_norm_eps": 1e-05,
  "rope_theta": 10000.0,
  "tie_word_embeddings": true,
  "transformers_version": "5.19.0",
  "vocab_size": 32768
}

	{
	"architectures": [
	"MiniLlmForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration_mini_llm.MiniLlmConfig",
	"AutoModelForCausalLM": "modeling_mini_llm.MiniLlmForCausalLM"
	},
	"dtype": "float32",
	"eos_token_id": 0,
	"hidden_size": 512,
	"intermediate_size": 1408,
	"max_position_embeddings": 1024,
	"model_type": "mini_llm",
	"num_attention_heads": 8,
	"num_hidden_layers": 8,
	"pad_token_id": 0,
	"rms_norm_eps": 1e-05,
	"rope_theta": 10000.0,
	"tie_word_embeddings": true,
	"transformers_version": "5.19.0",
	"vocab_size": 32768
	}