Instructions to use AXCXEPT/Llama-3-EZO-8b-Common-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AXCXEPT/Llama-3-EZO-8b-Common-it with Transformers:

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

pipe = pipeline("text-generation", model="AXCXEPT/Llama-3-EZO-8b-Common-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("AXCXEPT/Llama-3-EZO-8b-Common-it")
model = AutoModelForMultimodalLM.from_pretrained("AXCXEPT/Llama-3-EZO-8b-Common-it")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AXCXEPT/Llama-3-EZO-8b-Common-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AXCXEPT/Llama-3-EZO-8b-Common-it"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AXCXEPT/Llama-3-EZO-8b-Common-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AXCXEPT/Llama-3-EZO-8b-Common-it

SGLang

How to use AXCXEPT/Llama-3-EZO-8b-Common-it 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 "AXCXEPT/Llama-3-EZO-8b-Common-it" \
    --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": "AXCXEPT/Llama-3-EZO-8b-Common-it",
		"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 "AXCXEPT/Llama-3-EZO-8b-Common-it" \
        --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": "AXCXEPT/Llama-3-EZO-8b-Common-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AXCXEPT/Llama-3-EZO-8b-Common-it with Docker Model Runner:
```
docker model run hf.co/AXCXEPT/Llama-3-EZO-8b-Common-it
```

Llama-3-EZO-8b-Common-it

Commit History

Upload tokenizer

fe2479b
verified

KAZUYA commited on Jul 13, 2024

Upload LlamaForCausalLM

b47db5c
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KAZUYA commited on Jul 13, 2024

initial commit

636f967
verified

KAZUYA commited on Jul 13, 2024

Commit History

Upload tokenizer fe2479b verified

Upload LlamaForCausalLM b47db5c verified

initial commit 636f967 verified

Upload tokenizer

fe2479b
verified

Upload LlamaForCausalLM

b47db5c
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initial commit

636f967
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