Instructions to use MaziyarPanahi/Llama-3-8B-Instruct-v0.9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/Llama-3-8B-Instruct-v0.9 with Transformers:

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

pipe = pipeline("text-generation", model="MaziyarPanahi/Llama-3-8B-Instruct-v0.9")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/Llama-3-8B-Instruct-v0.9")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/Llama-3-8B-Instruct-v0.9", 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 MaziyarPanahi/Llama-3-8B-Instruct-v0.9 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/Llama-3-8B-Instruct-v0.9"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaziyarPanahi/Llama-3-8B-Instruct-v0.9",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.9

SGLang

How to use MaziyarPanahi/Llama-3-8B-Instruct-v0.9 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 "MaziyarPanahi/Llama-3-8B-Instruct-v0.9" \
    --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": "MaziyarPanahi/Llama-3-8B-Instruct-v0.9",
		"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 "MaziyarPanahi/Llama-3-8B-Instruct-v0.9" \
        --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": "MaziyarPanahi/Llama-3-8B-Instruct-v0.9",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MaziyarPanahi/Llama-3-8B-Instruct-v0.9 with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.9
```

MaziyarPanahi commited on Jun 3, 2024

Commit

400cd21

verified ·

1 Parent(s): 117cb6c

Update README.md

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README.md +11 -15

README.md CHANGED Viewed

@@ -135,8 +135,18 @@ This model was developed based on `MaziyarPanahi/Llama-3-8B-Instruct-v0.8` model
 All GGUF models are available here: [MaziyarPanahi/Llama-3-8B-Instruct-v0.9-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.9-GGUF)
-# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
 `MaziyarPanahi/Llama-3-8B-Instruct-v0.9` is the 4th best-performing 8B model on the Open LLM Leaderboard. (03/06/2024).
@@ -217,17 +227,3 @@ outputs = pipeline(
 )
 print(outputs[0]["generated_text"][len(prompt):])
 ```
-# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
-Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__Llama-3-8B-Instruct-v0.9)
-|             Metric              |Value|
-|---------------------------------|----:|
-|Avg.                             |73.29|
-|AI2 Reasoning Challenge (25-Shot)|72.35|
-|HellaSwag (10-Shot)              |88.17|
-|MMLU (5-Shot)                    |68.10|
-|TruthfulQA (0-shot)              |64.67|
-|Winogrande (5-shot)              |79.95|
-|GSM8k (5-shot)                   |66.49|

 All GGUF models are available here: [MaziyarPanahi/Llama-3-8B-Instruct-v0.9-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.9-GGUF)
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__Llama-3-8B-Instruct-v0.9)
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |73.29|
+|AI2 Reasoning Challenge (25-Shot)|72.35|
+|HellaSwag (10-Shot)              |88.17|
+|MMLU (5-Shot)                    |68.10|
+|TruthfulQA (0-shot)              |64.67|
+|Winogrande (5-shot)              |79.95|
+|GSM8k (5-shot)                   |66.49|
 `MaziyarPanahi/Llama-3-8B-Instruct-v0.9` is the 4th best-performing 8B model on the Open LLM Leaderboard. (03/06/2024).
 )
 print(outputs[0]["generated_text"][len(prompt):])
 ```