Instructions to use MaziyarPanahi/Llama-3-8B-Instruct-v0.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/Llama-3-8B-Instruct-v0.4 with Transformers:

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

pipe = pipeline("text-generation", model="MaziyarPanahi/Llama-3-8B-Instruct-v0.4")
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-v0.4")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/Llama-3-8B-Instruct-v0.4", 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-v0.4 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-v0.4"
# 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-v0.4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.4

SGLang

How to use MaziyarPanahi/Llama-3-8B-Instruct-v0.4 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-v0.4" \
    --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-v0.4",
		"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-v0.4" \
        --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-v0.4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MaziyarPanahi/Llama-3-8B-Instruct-v0.4 with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.4
```

Llama-3-8B-Instruct-v0.4 / README.md

MaziyarPanahi

Create README.md (#3)

e76851b verified over 2 years ago

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	---
	base_model: meta-llama/Meta-Llama-3-8B-Instruct
	library_name: transformers
	tags:
	- axolotl
	- finetune
	- facebook
	- meta
	- pytorch
	- llama
	- llama-3
	language:
	- en
	pipeline_tag: text-generation
	license: other
	license_name: llama3
	license_link: LICENSE
	inference: false
	model_creator: MaziyarPanahi
	model_name: Llama-3-8B-Instruct-v0.4
	quantized_by: MaziyarPanahi
	---

	<img src="./llama-3-merges.webp" alt="Llama-3 DPO Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>


	# Llama-3-8B-Instruct-v0.4

	This model was developed based on `meta-llama/Meta-Llama-3-8B-Instruct` model.

	# Quantized GGUF

	All GGUF models are available here: [MaziyarPanahi/Llama-3-8B-Instruct-v0.4-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.4-GGUF)


	# Prompt Template

	This model uses `ChatML` prompt template:

	```
	<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>

	{system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>

	{prompt}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>
	````

	# How to use

	You can use this model by using `MaziyarPanahi/Llama-3-8B-Instruct-v0.4` as the model name in Hugging Face's
	transformers library.

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
	from transformers import pipeline
	import torch

	model_id = "MaziyarPanahi/Llama-3-8B-Instruct-v0.4"

	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.bfloat16,
	device_map="auto",
	trust_remote_code=True,
	# attn_implementation="flash_attention_2"
	)

	tokenizer = AutoTokenizer.from_pretrained(
	model_id,
	trust_remote_code=True
	)

	streamer = TextStreamer(tokenizer)

	pipeline = pipeline(
	"text-generation",
	model=model,
	tokenizer=tokenizer,
	model_kwargs={"torch_dtype": torch.bfloat16},
	streamer=streamer
	)

	# Then you can use the pipeline to generate text.

	messages = [
	{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
	{"role": "user", "content": "Who are you?"},
	]

	prompt = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True
	)

	terminators = [
	tokenizer.eos_token_id,
	tokenizer.convert_tokens_to_ids("<\|eot_id\|>")
	]

	outputs = pipeline(
	prompt,
	max_new_tokens=512,
	eos_token_id=terminators,
	do_sample=True,
	temperature=0.6,
	top_p=0.95,
	)
	print(outputs[0]["generated_text"][len(prompt):])
	```