Instructions to use meta-llama/Llama-3.1-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meta-llama/Llama-3.1-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="meta-llama/Llama-3.1-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-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
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use meta-llama/Llama-3.1-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meta-llama/Llama-3.1-8B-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": "meta-llama/Llama-3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meta-llama/Llama-3.1-8B-Instruct

SGLang

How to use meta-llama/Llama-3.1-8B-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 "meta-llama/Llama-3.1-8B-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": "meta-llama/Llama-3.1-8B-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 "meta-llama/Llama-3.1-8B-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": "meta-llama/Llama-3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use meta-llama/Llama-3.1-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/meta-llama/Llama-3.1-8B-Instruct
```

`config.eos_token_id`'s being a list causes problem

#149

by sadra-barikbin - opened Oct 12, 2024

Discussion

sadra-barikbin

Oct 12, 2024

Hi there!

Model config's eos_token_id is of type list but is supposed to be an int according to transformers's configuration_utils.py::PreTrainedConfig.

https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/blob/0e9e39f249a16976918f6564b8830bc894c89659/config.json#L8-L12

This causes problem during serving the model with TGI:

File "/opt/conda/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 3347, in batch_encode_plus
    padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
  File "/opt/conda/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 2976, in _get_padding_truncation_strategies
    if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.pad_token is None or self.pad_token_id < 0):
TypeError: '<' not supported between instances of 'NoneType' and 'int'
2024-10-10T15:39:46.502649Z ERROR warmup{max_input_length=4095 max_prefill_tokens=4145 max_total_tokens=4096 max_batch_size=None}:warmup: text_generation_router_v3::client: backends/v3/src/client/mod.rs:54: Server error: '<' not supported between instances of 'NoneType' and 'int'
Error: Backend(Warmup(Generation("'<' not supported between instances of 'NoneType' and 'int'")))
2024-10-10T15:39:46.557801Z ERROR text_generation_launcher: Webserver Crashed
2024-10-10T15:39:46.557834Z  INFO text_generation_launcher: Shutting down shards

What causes the problem in TGI is that if model's tokenizer does not have a padding_token, TGI attempts to set it using eos_token:

https://github.com/huggingface/text-generation-inference/blob/0c478846c5002a4053b0349d6557bafb9cedc935/server/text_generation_server/models/causal_lm.py#L547-L551

By setting tokenizer.pad_token_id, tokenizer's _pad_token becomes ['<|end_of_text|>', '<|eom_id|>', '<|eot_id|>'].

https://github.com/huggingface/transformers/blob/617b21273a349bd3a94e2b3bfb83f8089f45749b/src/transformers/tokenization_utils_base.py#L1295-L1297

https://github.com/huggingface/transformers/blob/617b21273a349bd3a94e2b3bfb83f8089f45749b/src/transformers/tokenization_utils_base.py#L1101-L1110

https://github.com/huggingface/transformers/blob/617b21273a349bd3a94e2b3bfb83f8089f45749b/src/transformers/tokenization_utils_base.py#L1233-L1240

This problem also exists for meta-llama/Llama-3.2-3B-Instruct and meta-llama/Llama-3.2-1B-Instruct.

@joaogante

joaogante

Oct 14, 2024

(TL;DR: do you have a recent version of TGI? Have you tried updating? The two stop tokens are part of the design of llama 3.1)

sadra-barikbin

Oct 14, 2024

•

edited Oct 14, 2024

Thank you for your response.

It turned out that I was running TGI over CPU🤦🏻. In that case, TGI goes through the path above and raises error (is it a bug for TGI then?) but running on GPU it loads another variant of the model i.e. the flash one which in its code they've handled this matter:

https://github.com/huggingface/text-generation-inference/blob/3ea82d008c8f26157e8d0b568b885536efb6a7b0/server/text_generation_server/models/flash_causal_lm.py#L970-L972

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