Instructions to use MDaytek/chess-submission-v13-fix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MDaytek/chess-submission-v13-fix with Transformers:

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

pipe = pipeline("text-generation", model="MDaytek/chess-submission-v13-fix", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MDaytek/chess-submission-v13-fix", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MDaytek/chess-submission-v13-fix with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MDaytek/chess-submission-v13-fix"
# 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-submission-v13-fix",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MDaytek/chess-submission-v13-fix

SGLang

How to use MDaytek/chess-submission-v13-fix 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-submission-v13-fix" \
    --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-submission-v13-fix",
		"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-submission-v13-fix" \
        --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-submission-v13-fix",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MDaytek/chess-submission-v13-fix with Docker Model Runner:
```
docker model run hf.co/MDaytek/chess-submission-v13-fix
```

MDaytek commited on Jan 19

Commit

83869fe

verified ·

1 Parent(s): f3ba272

V13 Fix ctx

Browse files

Files changed (6) hide show

README.md +6 -0
config.json +19 -0
model.py +81 -0
model.safetensors +3 -0
tokenizer.py +26 -0
vocab.json +1006 -0

README.md ADDED Viewed

	@@ -0,0 +1,6 @@

+---
+library_name: transformers
+tags: [chess, llm-course]
+---
+# ChessGPT V13
+Fixed n_ctx attribute.

config.json ADDED Viewed

	@@ -0,0 +1,19 @@

+{
+    "architectures": [
+        "ChessGPT"
+    ],
+    "dropout": 0.1,
+    "dtype": "float32",
+    "model_type": "chess_gpt",
+    "n_ctx": 256,
+    "n_embd": 128,
+    "n_head": 4,
+    "n_layer": 4,
+    "n_positions": 256,
+    "transformers_version": "4.57.6",
+    "vocab_size": 1004,
+    "auto_map": {
+        "AutoConfig": "model.ChessConfig",
+        "AutoModelForCausalLM": "model.ChessGPT"
+    }
+}

model.py ADDED Viewed

	@@ -0,0 +1,81 @@

+import math, torch, torch.nn as nn, torch.nn.functional as F
+from transformers import PreTrainedModel, PretrainedConfig
+from transformers.modeling_outputs import CausalLMOutputWithPast
+class ChessConfig(PretrainedConfig):
+    model_type = "chess_gpt"
+    def __init__(self, vocab_size=1000, n_positions=256, n_embd=128, n_layer=4, n_head=4, dropout=0.1, **kwargs):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.n_positions = n_positions
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.dropout = dropout
+        # 👇 LE FIX EST ICI : On définit n_ctx comme alias de n_positions
+        self.n_ctx = n_positions
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+    def forward(self, x):
+        B, T, C = x.size()
+        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+        mask = torch.triu(torch.ones(T, T, device=x.device), diagonal=1).bool()
+        att = att.masked_fill(mask, float('-inf'))
+        att = F.softmax(att, dim=-1)
+        y = self.resid_dropout(self.c_proj((self.attn_dropout(att) @ v).transpose(1, 2).contiguous().view(B, T, C)))
+        return y
+class GPTBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.n_embd)
+        self.attn = CausalSelfAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd)
+        self.mlp = nn.Sequential(nn.Linear(config.n_embd, 4 * config.n_embd), nn.GELU(), nn.Linear(4 * config.n_embd, config.n_embd), nn.Dropout(config.dropout))
+    def forward(self, x): return x + self.mlp(self.ln_2(x + self.attn(self.ln_1(x))))
+class ChessGPT(PreTrainedModel):
+    config_class = ChessConfig
+    def __init__(self, config):
+        super().__init__(config)
+        self.token_emb = nn.Embedding(config.vocab_size, config.n_embd)
+        self.pos_emb = nn.Embedding(config.n_positions, config.n_embd)
+        self.dropout = nn.Dropout(config.dropout)
+        self.blocks = nn.ModuleList([GPTBlock(config) for _ in range(config.n_layer)])
+        self.ln_f = nn.LayerNorm(config.n_embd)
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.apply(self._init_weights)
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, nn.LayerNorm):
+            torch.nn.init.zeros_(module.bias)
+            torch.nn.init.ones_(module.weight)
+    def forward(self, input_ids, labels=None, **kwargs):
+        B, T = input_ids.shape
+        x = self.dropout(self.token_emb(input_ids) + self.pos_emb(torch.arange(0, T, device=input_ids.device)))
+        for block in self.blocks: x = block(x)
+        logits = self.lm_head(self.ln_f(x))
+        loss = None
+        if labels is not None:
+            # Sécurité anti-crash
+            labels = labels.clamp(0, self.config.vocab_size - 1)
+            loss = nn.CrossEntropyLoss(ignore_index=0)(logits[..., :-1, :].contiguous().view(-1, logits.size(-1)), labels[..., 1:].contiguous().view(-1))
+        return CausalLMOutputWithPast(loss=loss, logits=logits)

model.safetensors ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:cdcb3fc13f3b4fec1d3c8dd0ac82ade20ae9fbc6a03b92a37c0a7e5eac51234b
+size 4337224

tokenizer.py ADDED Viewed

	@@ -0,0 +1,26 @@

+import json, os, torch
+class ChessTokenizer:
+    def __init__(self, vocab):
+        self.vocab = vocab
+        self.id_to_token = {v: k for k, v in vocab.items()}
+        self.pad_token_id = vocab.get("[PAD]", 0)
+        self.bos_token_id = vocab.get("[BOS]", 1)
+        self.eos_token_id = vocab.get("[EOS]", 2)
+        self.unk_token_id = vocab.get("[UNK]", 3)
+    @property
+    def vocab_size(self): return len(self.vocab)
+    def _convert_token_to_id(self, token): return self.vocab.get(token, self.unk_token_id)
+    def pad(self, encoded_inputs, padding=True, max_length=None, pad_to_multiple_of=None, return_tensors=None, **kwargs):
+        max_len = max(len(x["input_ids"]) for x in encoded_inputs)
+        padded = []
+        for x in encoded_inputs:
+            ids = x["input_ids"] + [self.pad_token_id] * (max_len - len(x["input_ids"]))
+            padded.append(ids)
+        if return_tensors == "pt": return {"input_ids": torch.tensor(padded, dtype=torch.long)}
+        return {"input_ids": padded}
+    def save_pretrained(self, path):
+        os.makedirs(path, exist_ok=True)
+        with open(os.path.join(path, "vocab.json"), "w") as f: json.dump(self.vocab, f, indent=2)
+    @classmethod
+    def from_pretrained(cls, path):
+        with open(os.path.join(path, "vocab.json"), "r") as f: return cls(json.load(f))

vocab.json ADDED Viewed

	@@ -0,0 +1,1006 @@

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