Instructions to use meta-llama/Meta-Llama-3-70B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meta-llama/Meta-Llama-3-70B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="meta-llama/Meta-Llama-3-70B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-70B-Instruct")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-70B-Instruct", 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 meta-llama/Meta-Llama-3-70B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meta-llama/Meta-Llama-3-70B-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": "meta-llama/Meta-Llama-3-70B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meta-llama/Meta-Llama-3-70B-Instruct

SGLang

How to use meta-llama/Meta-Llama-3-70B-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 "meta-llama/Meta-Llama-3-70B-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": "meta-llama/Meta-Llama-3-70B-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 "meta-llama/Meta-Llama-3-70B-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": "meta-llama/Meta-Llama-3-70B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use meta-llama/Meta-Llama-3-70B-Instruct with Docker Model Runner:
```
docker model run hf.co/meta-llama/Meta-Llama-3-70B-Instruct
```

clean_up_tokenization_spaces=True causes formatting issues, why is it set?

#44

by dzhulgakov - opened May 5, 2024

Discussion

dzhulgakov

May 5, 2024

Setting clean_up_tokenization_spaces=True in tokenizer_config.json causes weird output space formatting issues and makes tokenizer encode+decode lossy. This is especially pronounced for code. Minimal repro:

from transformers import AutoTokenizer
t = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-70B-Instruct")
s = "foo ?? bar"
ids = t.encode(s)
s_cleanup = t.decode(ids)
s_no_cleanup = t.decode(ids, clean_up_tokenization_spaces=False)
print(ids)
print(s)
print(s_cleanup)
print(s_no_cleanup)

outputs

[128000, 8134, 9602, 3703]
foo ?? bar
<|begin_of_text|>foo?? bar
<|begin_of_text|>foo ?? bar

Notice the missing space in the first output.

FWIW, Llama2 had it as False.

Official Meta's repo (https://github.com/meta-llama/llama3/blob/main/llama/tokenizer.py) doesn't have any special sauce around TikToken either, and for the text above it preserves the space. Why was this setting turned on for Llama3?

rakmik

Dec 21, 2024

"bos_token": "<|begin_of_text|>",
"clean_up_tokenization_spaces": true,
"eos_token": "<|end_of_text|>",
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 1000000000000000019884624838656,
"tokenizer_class": "PreTrainedTokenizerFast"
}

rakmik

Dec 21, 2024

ISTA-DASLab/Meta-Llama-3.1-8B-AQLM-PV-1Bit-1x16-hf

When running the output is not clear.

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ISTA-DASLab/Meta-Llama-3-70B-AQLM-PV-1Bit-1x16")
model = AutoModelForCausalLM.from_pretrained(
"ISTA-DASLab/Meta-Llama-3-70B-AQLM-PV-1Bit-1x16", device_map="auto"
)

prompt = """How many helicopters can a human eat in one sitting? Reply as a thug."""
model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
input_length = model_inputs.input_ids.shape[1]
generated_ids = model.generate(**model_inputs, max_new_tokens=20)
print(tokenizer.batch_decode(generated_ids[:, input_length:], skip_special_tokens=True)[0])

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