Instructions to use 0xrushi/chesslfm-experiments with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0xrushi/chesslfm-experiments with Transformers:

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

pipe = pipeline("text-generation", model="0xrushi/chesslfm-experiments")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("0xrushi/chesslfm-experiments", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 0xrushi/chesslfm-experiments with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xrushi/chesslfm-experiments"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0xrushi/chesslfm-experiments",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/0xrushi/chesslfm-experiments

SGLang

How to use 0xrushi/chesslfm-experiments 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 "0xrushi/chesslfm-experiments" \
    --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": "0xrushi/chesslfm-experiments",
		"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 "0xrushi/chesslfm-experiments" \
        --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": "0xrushi/chesslfm-experiments",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use 0xrushi/chesslfm-experiments with Docker Model Runner:
```
docker model run hf.co/0xrushi/chesslfm-experiments
```

chesslfm-experiments / self-play-refresh /tokenizer_config.json

0xrushi

Add files using upload-large-folder tool

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{
  "backend": "tokenizers",
  "bos_token": "<|startoftext|>",
  "clean_up_tokenization_spaces": false,
  "eos_token": "<|im_end|>",
  "is_local": true,
  "legacy": false,
  "local_files_only": true,
  "model_input_names": [
    "input_ids",
    "attention_mask"
  ],
  "model_max_length": 1000000000000000019884624838656,
  "pad_token": "<|pad|>",
  "sp_model_kwargs": {},
  "spaces_between_special_tokens": false,
  "tokenizer_class": "TokenizersBackend",
  "use_default_system_prompt": false,
  "use_fast": true
}

	{
	"backend": "tokenizers",
	"bos_token": "<\|startoftext\|>",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|im_end\|>",
	"is_local": true,
	"legacy": false,
	"local_files_only": true,
	"model_input_names": [
	"input_ids",
	"attention_mask"
	],
	"model_max_length": 1000000000000000019884624838656,
	"pad_token": "<\|pad\|>",
	"sp_model_kwargs": {},
	"spaces_between_special_tokens": false,
	"tokenizer_class": "TokenizersBackend",
	"use_default_system_prompt": false,
	"use_fast": true
	}