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from typing import Optional
from fastapi import FastAPI
from pydantic import BaseModel
from vllm import LLM, SamplingParams, RequestOutput
# Don't forget to set HF_TOKEN in the env during running
app = FastAPI()
# Initialize the LLM engine
# Replace 'your-model-path' with the actual path or name of your model
engine = LLM(
model='meta-llama/Llama-3.2-3B-Instruct',
revision="0cb88a4f764b7a12671c53f0838cd831a0843b95",
max_num_batched_tokens=512, # Reduced for T4
max_num_seqs=16, # Reduced for T4
gpu_memory_utilization=0.85, # Slightly increased, adjust if needed
max_model_len=131072, # Llama-3.2-3B-Instruct context length
enforce_eager=True, # Disable CUDA graph
dtype='half', # Use half precision
)
@app.get("/")
def greet_json():
return {"Hello": "World!"}
class GenerationRequest(BaseModel):
prompt: str
max_tokens: int = 100
temperature: float = 0.7
logit_bias: Optional[dict[int, float]] = None
class GenerationResponse(BaseModel):
text: Optional[str]
error: Optional[str]
@app.post("/generate-llama3-2")
def generate_text(request: GenerationRequest) -> list[RequestOutput] | dict[str, str]:
try:
sampling_params: SamplingParams = SamplingParams(
temperature=request.temperature,
max_tokens=request.max_tokens,
logit_bias=request.logit_bias,
)
# Generate text
return engine.generate(
prompts=request.prompt,
sampling_params=sampling_params
)
except Exception as e:
return {
"error": str(e)
}
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