Instructions to use fxmarty/tiny-llama-fast-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fxmarty/tiny-llama-fast-tokenizer with Transformers:

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

pipe = pipeline("text-generation", model="fxmarty/tiny-llama-fast-tokenizer")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fxmarty/tiny-llama-fast-tokenizer")
model = AutoModelForCausalLM.from_pretrained("fxmarty/tiny-llama-fast-tokenizer", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fxmarty/tiny-llama-fast-tokenizer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/fxmarty/tiny-llama-fast-tokenizer

SGLang

How to use fxmarty/tiny-llama-fast-tokenizer 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 "fxmarty/tiny-llama-fast-tokenizer" \
    --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": "fxmarty/tiny-llama-fast-tokenizer",
		"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 "fxmarty/tiny-llama-fast-tokenizer" \
        --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": "fxmarty/tiny-llama-fast-tokenizer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use fxmarty/tiny-llama-fast-tokenizer with Docker Model Runner:
```
docker model run hf.co/fxmarty/tiny-llama-fast-tokenizer
```

tiny-llama-fast-tokenizer

Commit History

Nov 18, 2023

06:28
Adding `safetensors` variant of this model⁠

SFconvertbot

fce67ff

Oct 16, 2023

09:01
Create README.md

fxmarty

7777c40

Jun 12, 2023

05:48
fix tokenizer?

Félix Marty

c11267e

Apr 17, 2023

07:53
add model

Félix Marty

daae59b
07:52
initial commit

fxmarty

99db7a9

Commit History

Nov 18, 2023

Adding `safetensors` variant of this model⁠

Oct 16, 2023

Create README.md

Jun 12, 2023

fix tokenizer?

Apr 17, 2023

add model

initial commit