Instructions to use astronomer/Llama-3-8B-Special-Tokens-Adjusted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use astronomer/Llama-3-8B-Special-Tokens-Adjusted with Transformers:

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

pipe = pipeline("text-generation", model="astronomer/Llama-3-8B-Special-Tokens-Adjusted")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("astronomer/Llama-3-8B-Special-Tokens-Adjusted")
model = AutoModelForCausalLM.from_pretrained("astronomer/Llama-3-8B-Special-Tokens-Adjusted", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use astronomer/Llama-3-8B-Special-Tokens-Adjusted with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "astronomer/Llama-3-8B-Special-Tokens-Adjusted"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "astronomer/Llama-3-8B-Special-Tokens-Adjusted",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/astronomer/Llama-3-8B-Special-Tokens-Adjusted

SGLang

How to use astronomer/Llama-3-8B-Special-Tokens-Adjusted 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 "astronomer/Llama-3-8B-Special-Tokens-Adjusted" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "astronomer/Llama-3-8B-Special-Tokens-Adjusted",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "astronomer/Llama-3-8B-Special-Tokens-Adjusted" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "astronomer/Llama-3-8B-Special-Tokens-Adjusted",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use astronomer/Llama-3-8B-Special-Tokens-Adjusted with Docker Model Runner:
```
docker model run hf.co/astronomer/Llama-3-8B-Special-Tokens-Adjusted
```

Changing Reserve token names

by vburb - opened May 24, 2024

Discussion

vburb

May 24, 2024

Question on the reserve tokens. Is there any impact to changing the value name of the tokens? For instance, if we wanted to use certain reserve tokens for classification purposes and didn't want to create vague token references in our prompts?

For example,

"128010": {
"content": "<|reserved_special_token_5|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},

"128010": {
"content": "<|new_label_5_reference|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},

davidxmle

Astronomer org May 28, 2024

I think in theory this is ok. But just know that the embedding value of that token id was not pretrained on whatever you are working on, so it probably has either no semantic meaning or has incorrect semantic meaning from pretraining.

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