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
```

Update Readme link

#30

by michaelfeil - opened Feb 9

base: refs/heads/main

←

from: refs/pr/30

Discussion Files changed

-1

Files changed (1) hide show

README.md +2 -1

README.md CHANGED Viewed

@@ -110,8 +110,9 @@ Drop an email to [contact@gradient.ai](mailto:contact@gradient.ai)
   title={Llama 3 Gradient: A series of long context models},
   author={Leonid Pekelis and Michael Feil and Forrest Moret and Mark Huang and Tiffany Peng},
   year={2024},
-  url = {https://gradient.ai/blog/scaling-rotational-embeddings-for-long-context-language-models},
   doi = { 10.57967/hf/3372 },
 }
 ```

   title={Llama 3 Gradient: A series of long context models},
   author={Leonid Pekelis and Michael Feil and Forrest Moret and Mark Huang and Tiffany Peng},
   year={2024},
+  url = {https://web.archive.org/web/20240928003829/https://gradient.ai/blog/scaling-rotational-embeddings-for-long-context-language-models},
   doi = { 10.57967/hf/3372 },
+  note    = {Pekelis and Feil contributed equally.},
 }
 ```