Instructions to use LLM-CLEM/Lam-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM-CLEM/Lam-3 with Transformers:

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

pipe = pipeline("text-generation", model="LLM-CLEM/Lam-3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LLM-CLEM/Lam-3")
model = AutoModelForCausalLM.from_pretrained("LLM-CLEM/Lam-3", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LLM-CLEM/Lam-3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM-CLEM/Lam-3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM-CLEM/Lam-3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLM-CLEM/Lam-3

SGLang

How to use LLM-CLEM/Lam-3 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 "LLM-CLEM/Lam-3" \
    --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": "LLM-CLEM/Lam-3",
		"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 "LLM-CLEM/Lam-3" \
        --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": "LLM-CLEM/Lam-3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLM-CLEM/Lam-3 with Docker Model Runner:
```
docker model run hf.co/LLM-CLEM/Lam-3
```

Lam-3 / tokenizer.json

Clemylia

Publication du modèle Lamina-0.7B fine-tuné sur dataset Nacid.

47aa50a verified 10 months ago

Raw

History Blame Contribute Delete

251 kB

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	],
	[
	"hyperpara",
	"mètre"
	],
	[
	"kilomè",
	"tres"
	],
	[
	"échantil",
	"lons"
	],
	[
	"Graphic",
	"s"
	],
	[
	"nuclé",
	"aire"
	],
	[
	"possè",
	"dent"
	],
	[
	"spéciali",
	"sé"
	],
	[
	"personnali",
	"sés"
	],
	[
	"Certain",
	"es"
	],
	[
	"Rayle",
	"igh"
	],
	[
	"biologique",
	"ment"
	],
	[
	"célè",
	"bre"
	],
	[
	"improbab",
	"les"
	],
	[
	"immuab",
	"les"
	],
	[
	"push_t",
	"o_hub"
	],
	[
	"Capricor",
	"ne"
	],
	[
	"Ludw",
	"ig"
	],
	[
	"Stock",
	"holm"
	],
	[
	"receuil",
	"lit"
	],
	[
	"guacamo",
	"le"
	],
	[
	"Beetho",
	"ven"
	],
	[
	"Kilimandj",
	"aro"
	],
	[
	"Préju",
	"gés"
	],
	[
	"Sydne",
	"y"
	],
	[
	"Salvad",
	"or"
	],
	[
	"Santiag",
	"o"
	],
	[
	"Théodo",
	"re"
	],
	[
	"cryptograph",
	"iquement"
	],
	[
	"effica",
	"ce"
	],
	[
	"emprein",
	"tes"
	],
	[
	"satisfa",
	"ction"
	],
	[
	"thermodyna",
	"mique"
	]
	]
	}
	}