Text Generation
Transformers
Safetensors
chess_transformer
chess
llm-course
chess-challenge
custom_code
Instructions to use LLM-course/chess-MDaytek with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LLM-course/chess-MDaytek with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LLM-course/chess-MDaytek", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("LLM-course/chess-MDaytek", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LLM-course/chess-MDaytek with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LLM-course/chess-MDaytek" # 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/chess-MDaytek", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LLM-course/chess-MDaytek
- SGLang
How to use LLM-course/chess-MDaytek 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/chess-MDaytek" \ --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/chess-MDaytek", "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/chess-MDaytek" \ --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/chess-MDaytek", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LLM-course/chess-MDaytek with Docker Model Runner:
docker model run hf.co/LLM-course/chess-MDaytek
| import json | |
| import os | |
| from transformers import PreTrainedTokenizer | |
| class ChessTokenizer(PreTrainedTokenizer): | |
| model_input_names = ["input_ids", "attention_mask"] | |
| vocab_files_names = {"vocab_file": "vocab.json"} | |
| PAD_TOKEN = "[PAD]" | |
| BOS_TOKEN = "[BOS]" | |
| EOS_TOKEN = "[EOS]" | |
| UNK_TOKEN = "[UNK]" | |
| def __init__(self, vocab_file=None, vocab=None, **kwargs): | |
| self._pad_token = self.PAD_TOKEN | |
| self._bos_token = self.BOS_TOKEN | |
| self._eos_token = self.EOS_TOKEN | |
| self._unk_token = self.UNK_TOKEN | |
| kwargs.pop("pad_token", None) | |
| kwargs.pop("bos_token", None) | |
| kwargs.pop("eos_token", None) | |
| kwargs.pop("unk_token", None) | |
| if vocab is not None: | |
| self._vocab = vocab | |
| elif vocab_file is not None and os.path.exists(vocab_file): | |
| with open(vocab_file, "r", encoding="utf-8") as f: | |
| self._vocab = json.load(f) | |
| else: | |
| self._vocab = self._create_default_vocab() | |
| self._ids_to_tokens = {v: k for k, v in self._vocab.items()} | |
| super().__init__(pad_token=self._pad_token, bos_token=self._bos_token, eos_token=self._eos_token, unk_token=self._unk_token, **kwargs) | |
| def _create_default_vocab(self): | |
| special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN] | |
| return {token: idx for idx, token in enumerate(special_tokens)} | |
| def vocab_size(self): | |
| return len(self._vocab) | |
| def get_vocab(self): | |
| return dict(self._vocab) | |
| def _tokenize(self, text): | |
| return text.strip().split() | |
| def _convert_token_to_id(self, token): | |
| return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0)) | |
| def _convert_id_to_token(self, index): | |
| return self._ids_to_tokens.get(index, self.UNK_TOKEN) | |
| def convert_tokens_to_string(self, tokens): | |
| special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN} | |
| return " ".join(t for t in tokens if t not in special) | |
| def save_vocabulary(self, save_directory, filename_prefix=None): | |
| if not os.path.isdir(save_directory): | |
| os.makedirs(save_directory, exist_ok=True) | |
| vocab_file = os.path.join(save_directory, (filename_prefix + "-" if filename_prefix else "") + "vocab.json") | |
| with open(vocab_file, "w", encoding="utf-8") as f: | |
| json.dump(self._vocab, f, ensure_ascii=False, indent=2) | |
| return (vocab_file,) | |