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from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import torch.nn.functional as F
app = FastAPI()
# Load the model and tokenizer
model_name = "EleutherAI/gpt-neo-1.3B" # Replace with your desired model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
@app.post("/predict")
async def predict(request: Request):
data = await request.json()
prompt = data.get("prompt", "")
if not prompt:
return {"error": "Prompt is required"}
# Tokenize the input
inputs = tokenizer(prompt, return_tensors="pt").to(device)
input_ids = inputs.input_ids
attention_mask = inputs.attention_mask
def token_generator():
temperature = 0.7
top_p = 0.9
for _ in range(100): # Limit to 100 tokens
with torch.no_grad(): # Disable gradient computation for inference
outputs = model(input_ids=input_ids, attention_mask=attention_mask)
next_token_logits = outputs.logits[:, -1, :]
# Apply temperature and softmax
next_token_logits = next_token_logits / temperature
next_token_probs = F.softmax(next_token_logits, dim=-1)
# Apply nucleus sampling (top-p)
sorted_probs, sorted_indices = torch.sort(next_token_probs, descending=True)
cumulative_probs = torch.cumsum(sorted_probs, dim=-1)
sorted_probs = sorted_probs[cumulative_probs <= top_p]
sorted_indices = sorted_indices[:len(sorted_probs)]
# Sample next token
if len(sorted_probs) > 0:
next_token_id = sorted_indices[torch.multinomial(sorted_probs, 1)]
else:
next_token_id = torch.argmax(next_token_probs)
# Append the new token to the input sequence
input_ids = torch.cat([input_ids, next_token_id.unsqueeze(-1)], dim=-1)
# Decode and yield the token
token = tokenizer.decode(next_token_id.squeeze(), skip_special_tokens=True)
yield token + " "
# Stop if the end-of-sequence token is generated
if next_token_id.squeeze().item() == tokenizer.eos_token_id:
break
return StreamingResponse(token_generator(), media_type="text/plain")
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