Instructions to use gradientai/Llama-3-8B-Instruct-Gradient-1048k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gradientai/Llama-3-8B-Instruct-Gradient-1048k with Transformers:

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

pipe = pipeline("text-generation", model="gradientai/Llama-3-8B-Instruct-Gradient-1048k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gradientai/Llama-3-8B-Instruct-Gradient-1048k")
model = AutoModelForCausalLM.from_pretrained("gradientai/Llama-3-8B-Instruct-Gradient-1048k")
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

vLLM

How to use gradientai/Llama-3-8B-Instruct-Gradient-1048k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gradientai/Llama-3-8B-Instruct-Gradient-1048k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gradientai/Llama-3-8B-Instruct-Gradient-1048k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k

SGLang

How to use gradientai/Llama-3-8B-Instruct-Gradient-1048k 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 "gradientai/Llama-3-8B-Instruct-Gradient-1048k" \
    --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": "gradientai/Llama-3-8B-Instruct-Gradient-1048k",
		"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 "gradientai/Llama-3-8B-Instruct-Gradient-1048k" \
        --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": "gradientai/Llama-3-8B-Instruct-Gradient-1048k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gradientai/Llama-3-8B-Instruct-Gradient-1048k with Docker Model Runner:
```
docker model run hf.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k
```

GPU requirement for hosting this model?

by csgxy2022 - opened May 1, 2024

Discussion

csgxy2022

May 1, 2024

Two A100 GPUs, trying to host this model but got OOM issue.

docker run --runtime nvidia --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface --env "HUGGING_FACE_HUB_TOKEN=xxx" -p 8000:8000 --ipc=host vllm/vllm-openai:latest --model gradientai/Llama-3-8B-Instruct-Gradient-1048k --tensor-parallel-size 2

I got:

torch.cuda.OutOfMemoryError: CUDA out of memory

csgxy2022

May 1, 2024

No problem hosting the original Llma-3-8B-Instruct model

fhsp93

May 3, 2024

This comment has been hidden

michaelfeil

May 4, 2024

•

edited May 4, 2024

The following should do the job for vllm! (A100.x2)

docker run --runtime nvidia --gpus all \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    -p 8000:8000 \
    --shm-size=8g \
    --ipc=host \
    vllm/vllm-openai:latest \
    --model gradientai/Llama-3-8B-Instruct-1048k \
    --tensor-parallel-size 2 \
    --max-model-len 65536

vLLM is not optimal, it would require around ~1000GB vRAM (IIRC) to serve a model with this hidden dim and ctx length.

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