tiny-random-LlamaForCausalLM

Instructions to use HuggingFaceH4/tiny-random-LlamaForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceH4/tiny-random-LlamaForCausalLM with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceH4/tiny-random-LlamaForCausalLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/tiny-random-LlamaForCausalLM")
model = AutoModelForMultimodalLM.from_pretrained("HuggingFaceH4/tiny-random-LlamaForCausalLM")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HuggingFaceH4/tiny-random-LlamaForCausalLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceH4/tiny-random-LlamaForCausalLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceH4/tiny-random-LlamaForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceH4/tiny-random-LlamaForCausalLM

SGLang

How to use HuggingFaceH4/tiny-random-LlamaForCausalLM 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 "HuggingFaceH4/tiny-random-LlamaForCausalLM" \
    --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": "HuggingFaceH4/tiny-random-LlamaForCausalLM",
		"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 "HuggingFaceH4/tiny-random-LlamaForCausalLM" \
        --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": "HuggingFaceH4/tiny-random-LlamaForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceH4/tiny-random-LlamaForCausalLM with Docker Model Runner:
```
docker model run hf.co/HuggingFaceH4/tiny-random-LlamaForCausalLM
```

pad_token_id=-1 now throws errors in HF

by johngiorgi - opened Jul 8, 2024

Discussion

johngiorgi

Jul 8, 2024

Thanks for uploading this! Super helpful for writing unit tests.

On a recent version of HF (I didn't track down exactly which version), this model now causes the follow error to be thrown due to its use of pad_token_id=-1:

Thrown during validation:
[UserWarning('`pad_token_id` should be positive but got -1. This will cause errors when batch generating, if there is padding. Please set `pas_token_id` explicitly by `model.generation_config.pad_token_id=PAD_TOKEN_ID` to avoid errors in generation, and ensure your `input_ids` input does not have negative values.')]

Maybe using the same ID as the UNK/EOS token would be a better default? Happy to PR if you agree

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