Spaces:
Sleeping
Sleeping
File size: 10,433 Bytes
f0c9bc7 5fa25d7 f0c9bc7 5fedbc6 f0c9bc7 5fa25d7 f0c9bc7 5fa25d7 f0c9bc7 5fa25d7 f0c9bc7 346da48 f0c9bc7 346da48 f0c9bc7 346da48 5fa25d7 f0c9bc7 5fa25d7 f0c9bc7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 |
"""
FastAPI server providing OpenAI-compatible endpoints for code generation.
Designed to work with MCP servers and provide unlimited tokens with minimal rate limiting.
"""
import os
import time
import uuid
from typing import Optional, List, Dict, Any
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
from huggingface_hub import hf_hub_download
from llama_cpp import Llama
import uvicorn
# ============================================================================
# CONFIGURATION
# ============================================================================
MODEL_REPO = "TheBloke/deepseek-coder-1.3b-instruct-GGUF"
MODEL_FILE = "deepseek-coder-1.3b-instruct.Q4_K_M.gguf"
MODEL_NAME = "deepseek-coder-1.3b-instruct"
# Context and generation settings for "unlimited" tokens
MAX_CONTEXT = 4096 # Larger context window
MAX_TOKENS = 4096 # Allow very long responses
DEFAULT_TEMP = 0.7
DEFAULT_TOP_P = 0.95
# ============================================================================
# PYDANTIC MODELS (OpenAI-compatible)
# ============================================================================
class Message(BaseModel):
role: str
content: str
class ChatCompletionRequest(BaseModel):
model: str = MODEL_NAME
messages: List[Message]
temperature: Optional[float] = DEFAULT_TEMP
top_p: Optional[float] = DEFAULT_TOP_P
max_tokens: Optional[int] = MAX_TOKENS
stream: Optional[bool] = False
stop: Optional[List[str]] = None
class CompletionRequest(BaseModel):
model: str = MODEL_NAME
prompt: str
temperature: Optional[float] = DEFAULT_TEMP
top_p: Optional[float] = DEFAULT_TOP_P
max_tokens: Optional[int] = MAX_TOKENS
stop: Optional[List[str]] = None
class Usage(BaseModel):
prompt_tokens: int
completion_tokens: int
total_tokens: int
class ChatCompletionChoice(BaseModel):
index: int
message: Message
finish_reason: str
class ChatCompletionResponse(BaseModel):
id: str
object: str = "chat.completion"
created: int
model: str
choices: List[ChatCompletionChoice]
usage: Usage
class CompletionChoice(BaseModel):
index: int
text: str
finish_reason: str
class CompletionResponse(BaseModel):
id: str
object: str = "text_completion"
created: int
model: str
choices: List[CompletionChoice]
usage: Usage
# ============================================================================
# FASTAPI APP
# ============================================================================
app = FastAPI(
title="Code LLM API",
description="OpenAI-compatible API for code generation with minimal rate limiting",
version="1.0.0"
)
# Enable CORS for MCP server access
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Global model instance
llm: Optional[Llama] = None
# ============================================================================
# MODEL LOADING
# ============================================================================
@app.on_event("startup")
async def load_model():
"""Load the LLM model on startup."""
global llm
print(f"Downloading model {MODEL_REPO}/{MODEL_FILE}...")
model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE)
print(f"Model downloaded to: {model_path}")
print("Loading model into memory...")
llm = Llama(
model_path=model_path,
n_ctx=MAX_CONTEXT,
n_threads=8, # Changed from 4 to 8
n_batch=1024, # Changed from 512 to 1024
verbose=False,
n_gpu_layers=0
)
print("Model loaded successfully!")
# ============================================================================
# HELPER FUNCTIONS
# ============================================================================
def messages_to_prompt(messages: List[Message]) -> str:
"""Convert OpenAI-style messages to a prompt for CodeLlama."""
prompt_parts = []
for msg in messages:
if msg.role == "system":
prompt_parts.append(f"### System: {msg.content}")
elif msg.role == "user":
prompt_parts.append(f"### Instruction: {msg.content}")
elif msg.role == "assistant":
prompt_parts.append(f"### Response: {msg.content}")
prompt_parts.append("### Response:")
return "\n".join(prompt_parts)
def estimate_tokens(text: str) -> int:
"""Rough token estimation (1 token ≈ 4 chars)."""
return len(text) // 4
# ============================================================================
# API ENDPOINTS
# ============================================================================
@app.get("/")
async def root():
"""Health check endpoint."""
return {
"status": "online",
"model": MODEL_NAME,
"max_context": MAX_CONTEXT,
"max_tokens": MAX_TOKENS,
"endpoints": {
"chat": "/v1/chat/completions",
"completion": "/v1/completions",
"models": "/v1/models"
}
}
@app.get("/health")
async def health():
"""Health check for monitoring."""
return {
"status": "healthy" if llm is not None else "loading",
"model_loaded": llm is not None
}
@app.get("/v1/models")
async def list_models():
"""List available models (OpenAI-compatible)."""
return {
"object": "list",
"data": [
{
"id": MODEL_NAME,
"object": "model",
"created": int(time.time()),
"owned_by": "huggingface",
"permission": [],
"root": MODEL_NAME,
"parent": None
}
]
}
@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
async def chat_completions(request: ChatCompletionRequest):
"""
OpenAI-compatible chat completions endpoint.
No rate limiting - designed for unlimited use.
"""
if llm is None:
raise HTTPException(status_code=503, detail="Model still loading")
if request.stream:
raise HTTPException(status_code=501, detail="Streaming not yet implemented")
# Convert messages to prompt
prompt = messages_to_prompt(request.messages)
# Generate response
try:
output = llm(
prompt,
max_tokens=request.max_tokens or MAX_TOKENS,
temperature=request.temperature or DEFAULT_TEMP,
top_p=request.top_p or DEFAULT_TOP_P,
stop=request.stop or ["###", "\n\n\n"],
echo=False
)
generated_text = output['choices'][0]['text'].strip()
# Estimate token usage
prompt_tokens = estimate_tokens(prompt)
completion_tokens = estimate_tokens(generated_text)
return ChatCompletionResponse(
id=f"chatcmpl-{uuid.uuid4().hex[:8]}",
created=int(time.time()),
model=request.model,
choices=[
ChatCompletionChoice(
index=0,
message=Message(role="assistant", content=generated_text),
finish_reason="stop"
)
],
usage=Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens
)
)
except Exception as e:
raise HTTPException(status_code=500, detail=f"Generation failed: {str(e)}")
@app.post("/v1/completions", response_model=CompletionResponse)
async def completions(request: CompletionRequest):
"""
OpenAI-compatible completions endpoint.
No rate limiting - designed for unlimited use.
"""
if llm is None:
raise HTTPException(status_code=503, detail="Model still loading")
try:
output = llm(
request.prompt,
max_tokens=request.max_tokens or MAX_TOKENS,
temperature=request.temperature or DEFAULT_TEMP,
top_p=request.top_p or DEFAULT_TOP_P,
stop=request.stop or [],
echo=False
)
generated_text = output['choices'][0]['text'].strip()
# Estimate token usage
prompt_tokens = estimate_tokens(request.prompt)
completion_tokens = estimate_tokens(generated_text)
return CompletionResponse(
id=f"cmpl-{uuid.uuid4().hex[:8]}",
created=int(time.time()),
model=request.model,
choices=[
CompletionChoice(
index=0,
text=generated_text,
finish_reason="stop"
)
],
usage=Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens
)
)
except Exception as e:
raise HTTPException(status_code=500, detail=f"Generation failed: {str(e)}")
# ============================================================================
# SIMPLE ENDPOINTS (for easier testing)
# ============================================================================
@app.post("/generate")
async def generate(prompt: str, max_tokens: int = 512):
"""Simple generation endpoint for quick testing."""
if llm is None:
raise HTTPException(status_code=503, detail="Model still loading")
try:
output = llm(prompt, max_tokens=max_tokens, temperature=0.7)
return {
"prompt": prompt,
"response": output['choices'][0]['text'].strip(),
"model": MODEL_NAME
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
# ============================================================================
# MAIN
# ============================================================================
if __name__ == "__main__":
uvicorn.run(
app,
host="0.0.0.0",
port=int(os.getenv("PORT", "7860")), # HF Spaces uses port 7860
log_level="info"
)
|