Instructions to use AI-Sweden-Models/Llama-3-8B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AI-Sweden-Models/Llama-3-8B-instruct with Transformers:

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

pipe = pipeline("text-generation", model="AI-Sweden-Models/Llama-3-8B-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AI-Sweden-Models/Llama-3-8B-instruct")
model = AutoModelForCausalLM.from_pretrained("AI-Sweden-Models/Llama-3-8B-instruct")
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 AI-Sweden-Models/Llama-3-8B-instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AI-Sweden-Models/Llama-3-8B-instruct

SGLang

How to use AI-Sweden-Models/Llama-3-8B-instruct 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 "AI-Sweden-Models/Llama-3-8B-instruct" \
    --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": "AI-Sweden-Models/Llama-3-8B-instruct",
		"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 "AI-Sweden-Models/Llama-3-8B-instruct" \
        --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": "AI-Sweden-Models/Llama-3-8B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AI-Sweden-Models/Llama-3-8B-instruct with Docker Model Runner:
```
docker model run hf.co/AI-Sweden-Models/Llama-3-8B-instruct
```

Finetune this model, how to handle terminators?

by BoccheseGiacomo - opened Aug 12, 2024

Discussion

BoccheseGiacomo

Aug 12, 2024

Hi everyone and thank you. I need to train this model on a custom task using lora finetuning.
Since i noted that there are special termination tokens , how should i setup my training data and tokenizer in order to get this handled correctly?

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

Since normally, on training, i use: tokenizer.pad_token = tokenizer.eos_token

timpal0l

AI Sweden Model Hub org Aug 13, 2024

Hi everyone and thank you. I need to train this model on a custom task using lora finetuning.
Since i noted that there are special termination tokens , how should i setup my training data and tokenizer in order to get this handled correctly?

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

Since normally, on training, i use: tokenizer.pad_token = tokenizer.eos_token

Hi @BoccheseGiacomo - you should follow the the Llama3 instruct format: https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/#llama-3-instruct

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