Instructions to use MDaytek/chess-v2-head with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MDaytek/chess-v2-head with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MDaytek/chess-v2-head", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MDaytek/chess-v2-head", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MDaytek/chess-v2-head with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MDaytek/chess-v2-head" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MDaytek/chess-v2-head", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MDaytek/chess-v2-head
- SGLang
How to use MDaytek/chess-v2-head 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 "MDaytek/chess-v2-head" \ --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": "MDaytek/chess-v2-head", "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 "MDaytek/chess-v2-head" \ --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": "MDaytek/chess-v2-head", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MDaytek/chess-v2-head with Docker Model Runner:
docker model run hf.co/MDaytek/chess-v2-head
Full submission with code by MDaytek
Browse files- config.json +16 -20
- tokenizer_config.json +1 -10
config.json
CHANGED
|
@@ -1,21 +1,17 @@
|
|
| 1 |
{
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
"AutoConfig": "model.ChessConfig",
|
| 19 |
-
"AutoModelForCausalLM": "model.ChessForCausalLM"
|
| 20 |
-
}
|
| 21 |
-
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"ChessForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"dtype": "float32",
|
| 6 |
+
"hidden_size": 128,
|
| 7 |
+
"model_type": "chess_transformer",
|
| 8 |
+
"n_ctx": 256,
|
| 9 |
+
"n_embd": 128,
|
| 10 |
+
"n_head": 8,
|
| 11 |
+
"n_inner": 256,
|
| 12 |
+
"n_layer": 6,
|
| 13 |
+
"num_attention_heads": 8,
|
| 14 |
+
"num_hidden_layers": 6,
|
| 15 |
+
"transformers_version": "4.57.3",
|
| 16 |
+
"vocab_size": 1344
|
| 17 |
+
}
|
|
|
|
|
|
|
|
|
|
|
|
tokenizer_config.json
CHANGED
|
@@ -1,10 +1 @@
|
|
| 1 |
-
{
|
| 2 |
-
"model_type": "chess_transformer",
|
| 3 |
-
"auto_map": {
|
| 4 |
-
"AutoTokenizer": [
|
| 5 |
-
"tokenizer.ChessTokenizer",
|
| 6 |
-
null
|
| 7 |
-
]
|
| 8 |
-
},
|
| 9 |
-
"tokenizer_class": "ChessTokenizer"
|
| 10 |
-
}
|
|
|
|
| 1 |
+
{"model_type": "chess_transformer"}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|