Instructions to use gradientai/Llama-3-8B-Instruct-262k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gradientai/Llama-3-8B-Instruct-262k with Transformers:

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

pipe = pipeline("text-generation", model="gradientai/Llama-3-8B-Instruct-262k")
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-262k")
model = AutoModelForCausalLM.from_pretrained("gradientai/Llama-3-8B-Instruct-262k", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gradientai/Llama-3-8B-Instruct-262k 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-262k"
# 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-262k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gradientai/Llama-3-8B-Instruct-262k

SGLang

How to use gradientai/Llama-3-8B-Instruct-262k 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-262k" \
    --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-262k",
		"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-262k" \
        --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-262k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gradientai/Llama-3-8B-Instruct-262k with Docker Model Runner:
```
docker model run hf.co/gradientai/Llama-3-8B-Instruct-262k
```

Update README to add title and Gradient logo

by tpeng726 - opened Apr 25, 2024

base: refs/heads/main

←

from: refs/pr/7

Discussion Files changed

+11

-7

Files changed (1) hide show

README.md +11 -7

README.md CHANGED Viewed

@@ -6,6 +6,9 @@ tags:
 - meta
 - llama-3
 ---
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585dc9be92bc5f258156bd6/hiHWva3CbsrnPvZTp5-lu.png)
@@ -40,6 +43,14 @@ For training data, we generate long contexts by augmenting [SlimPajama](https://
 | # GPUs                      | 8          | 8          |
 | GPU Type                    | NVIDIA L40S| NVIDIA L40S|
 ## References
 [1] Peng, Bowen, et al. "Yarn: Efficient context window extension of large language models." arXiv preprint arXiv:2309.00071 (2023).
@@ -48,13 +59,6 @@ For training data, we generate long contexts by augmenting [SlimPajama](https://
 [3] https://github.com/jzhang38/EasyContext
-## The Gradient AI Team
-Gradient is accelerating AI transformation across industries. https://gradient.ai/
-## Contact Us
-Drop an email to [contact@gradient.ai](mailto:contact@gradient.ai)
 ----

 - meta
 - llama-3
 ---
+<img src="https://cdn-uploads.huggingface.co/production/uploads/655bb613e8a8971e89944f3e/TSa3V8YpoVagnTYgxiLaO.png" width="200"/>
+# Llama-3 8B Instruct 262k
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585dc9be92bc5f258156bd6/hiHWva3CbsrnPvZTp5-lu.png)
 | # GPUs                      | 8          | 8          |
 | GPU Type                    | NVIDIA L40S| NVIDIA L40S|
+## The Gradient AI Team
+Gradient is accelerating AI transformation across industries. https://gradient.ai/
+## Contact Us
+Drop an email to [contact@gradient.ai](mailto:contact@gradient.ai)
 ## References
 [1] Peng, Bowen, et al. "Yarn: Efficient context window extension of large language models." arXiv preprint arXiv:2309.00071 (2023).
 [3] https://github.com/jzhang38/EasyContext
 ----