Instructions to use LLM-course/simple_tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM-course/simple_tokenizer with Transformers:

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

pipe = pipeline("text-generation", model="LLM-course/simple_tokenizer", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("LLM-course/simple_tokenizer", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LLM-course/simple_tokenizer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/LLM-course/simple_tokenizer

SGLang

How to use LLM-course/simple_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 "LLM-course/simple_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": "LLM-course/simple_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 "LLM-course/simple_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": "LLM-course/simple_tokenizer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LLM-course/simple_tokenizer with Docker Model Runner:
```
docker model run hf.co/LLM-course/simple_tokenizer
```

🏆 Evaluation Results

by nathanael-fijalkow - opened Jan 26

Discussion

nathanael-fijalkow

MVA+IASD LLM for code and proof org Jan 26

Evaluation Results

Model: LLM-course/simple_tokenizer
Parameters: 861,184 [PASS]
Chess library check: [PASS]

Performance

Metric	Value
Total moves played	500
Games played	26
Legal moves (first try)	76 (15.2%)
Legal moves (with retries)	172 (34.4%)

Interpretation

>90% legal rate: Excellent! Model has learned chess rules well.
70-90% legal rate: Good, but room for improvement.
<70% legal rate: Model struggles with legal move generation.

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment