Instructions to use LLM-course/simple_tokenizer_retrained2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM-course/simple_tokenizer_retrained2 with Transformers:

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

pipe = pipeline("text-generation", model="LLM-course/simple_tokenizer_retrained2", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("LLM-course/simple_tokenizer_retrained2", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LLM-course/simple_tokenizer_retrained2 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_retrained2"
# 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_retrained2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LLM-course/simple_tokenizer_retrained2

SGLang

How to use LLM-course/simple_tokenizer_retrained2 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_retrained2" \
    --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_retrained2",
		"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_retrained2" \
        --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_retrained2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LLM-course/simple_tokenizer_retrained2 with Docker Model Runner:
```
docker model run hf.co/LLM-course/simple_tokenizer_retrained2
```

simple_tokenizer_retrained2 / config.json

pultch

Chess Challenge submission by pultch

e6bae14 verified 4 months ago

raw

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498 Bytes

	{
	"architectures": [
	"ChessForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "model.ChessConfig",
	"AutoModelForCausalLM": "model.ChessForCausalLM"
	},
	"bos_token_id": 1,
	"dropout": 0.1,
	"dtype": "float32",
	"eos_token_id": 2,
	"layer_norm_epsilon": 1e-05,
	"model_type": "chess_transformer",
	"n_ctx": 128,
	"n_embd": 144,
	"n_head": 16,
	"n_inner": 432,
	"n_layer": 4,
	"pad_token_id": 0,
	"tie_weights": true,
	"transformers_version": "4.57.6",
	"vocab_size": 74
	}