Instructions to use Legalaz/25_llambo2_21_13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Legalaz/25_llambo2_21_13 with Transformers:

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

pipe = pipeline("text-generation", model="Legalaz/25_llambo2_21_13")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Legalaz/25_llambo2_21_13")
model = AutoModelForCausalLM.from_pretrained("Legalaz/25_llambo2_21_13")
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

vLLM

How to use Legalaz/25_llambo2_21_13 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Legalaz/25_llambo2_21_13"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Legalaz/25_llambo2_21_13",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Legalaz/25_llambo2_21_13

SGLang

How to use Legalaz/25_llambo2_21_13 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 "Legalaz/25_llambo2_21_13" \
    --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": "Legalaz/25_llambo2_21_13",
		"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 "Legalaz/25_llambo2_21_13" \
        --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": "Legalaz/25_llambo2_21_13",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Legalaz/25_llambo2_21_13 with Docker Model Runner:
```
docker model run hf.co/Legalaz/25_llambo2_21_13
```

25_llambo2_21_13 / tokenizer.json

Legalaz

Upload folder using huggingface_hub

4487e66 verified over 1 year ago

download

history blame contribute delete

17.2 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 91f1bc68d6688337a3faf6275dfc2641ee8164a34eef6027a52e6a1a411484ee
Size of remote file:: 17.2 MB
SHA256:: 510d12ec255f4cb0304aa5428d699c354c1a49696b427a2748a7b03bb7bbb575

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.