Switch to Gradio SDK + ZeroGPU for free GPU inference
Browse files- .gitignore +0 -0
- Dockerfile +0 -12
- README.md +24 -39
- app.py +20 -125
- requirements.txt +0 -4
- temp_page.html +108 -0
.gitignore
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Dockerfile
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FROM pytorch/pytorch:2.2.0-cuda12.1-cudnn8-runtime
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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COPY . .
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EXPOSE 7860
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CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
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README.md
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@@ -3,56 +3,41 @@ title: MiniCPM5-1B
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emoji: 🧠
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colorFrom: blue
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colorTo: purple
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sdk:
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pinned: false
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license: mit
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short_description: MiniCPM5-1B inference with
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---
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# MiniCPM5-1B Chat
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MiniCPM5-1B inference service on Hugging Face Spaces.
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##
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| `MODEL_ID` | `GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking` | Hugging Face model ID |
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| `API_KEY` | `wsh101007` | API key for OpenAI-compatible endpoints |
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| `MAX_TOKENS` | `2048` | Maximum generation tokens |
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## API Usage
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###
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curl -H "Authorization: Bearer wsh101007" https://{your-space}.hf.space/v1/models
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```
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### Chat Completion
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```bash
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curl -X POST https://{your-space}.hf.space/v1/chat/completions \
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-H "Authorization: Bearer wsh101007" \
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-H "Content-Type: application/json" \
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-d '{
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"model": "minicpm5-1b",
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"messages": [{"role": "user", "content": "Hello!"}],
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"temperature": 0.7,
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"max_tokens": 512
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}'
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```
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### Python (OpenAI SDK)
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```python
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client =
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)
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response = client.chat.completions.create(
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model="minicpm5-1b",
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messages=[{"role": "user", "content": "Hello!"}]
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)
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print(response.choices[0].message.content)
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```
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emoji: 🧠
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colorFrom: blue
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colorTo: purple
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sdk: gradio
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sdk_version: "6.20.0"
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python_version: "3.12"
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app_file: app.py
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pinned: false
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license: mit
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short_description: MiniCPM5-1B inference with ZeroGPU free GPU
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---
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# MiniCPM5-1B Chat
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MiniCPM5-1B inference service on Hugging Face Spaces using ZeroGPU (free GPU).
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## Usage
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### Web UI
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Visit `https://arooxy-qwe.hf.space/` to chat.
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### API (Gradio native)
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Gradio auto-generates API endpoints for the chat function:
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```python
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import gradio as gr
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client = gr.Client("Arooxy/qwe")
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result = client.predict(
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message="Hello!",
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history=[],
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api_name="/chat"
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)
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print(result)
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```
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### Environment Variables (Space Secrets)
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| Variable | Default | Description |
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|----------|---------|-------------|
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| `MODEL_ID` | `GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking` | Model to load |
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app.py
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@@ -1,158 +1,53 @@
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import os
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import time
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import json
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import torch
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from fastapi import FastAPI, Request, HTTPException
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from fastapi.responses import JSONResponse, StreamingResponse
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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from contextlib import asynccontextmanager
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import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer
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MODEL_ID = os.getenv("MODEL_ID", "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking")
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API_KEY = os.getenv("API_KEY", "wsh101007")
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MAX_TOKENS = int(os.getenv("MAX_TOKENS", "2048"))
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model = None
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tokenizer = None
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global model, tokenizer
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if model is
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device_map="auto",
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trust_remote_code=True
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)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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print("Model loaded successfully")
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return model, tokenizer
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def verify_auth(request: Request):
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auth = request.headers.get("Authorization", "")
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if not auth.startswith("Bearer ") or auth[7:] != API_KEY:
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raise HTTPException(status_code=401, detail="Invalid API key")
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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load_model()
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yield
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app = FastAPI(lifespan=lifespan, docs_url=None, redoc_url=None)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_methods=["*"],
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allow_headers=["*"],
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)
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class ChatMessage(BaseModel):
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role: str
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content: str
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class ChatCompletionRequest(BaseModel):
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model: str = "minicpm5-1b"
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messages: List[ChatMessage]
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temperature: Optional[float] = 0.7
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top_p: Optional[float] = 0.9
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max_tokens: Optional[int] = None
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stream: Optional[bool] = False
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@app.get("/")
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async def root():
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return {"message": "MiniCPM5-1B API is running", "model": MODEL_ID}
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@app.get("/v1/models")
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async def list_models(request: Request):
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verify_auth(request)
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return {
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"object": "list",
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"data": [{
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"id": "minicpm5-1b",
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"object": "model",
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"created": int(time.time()),
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"owned_by": "user"
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}]
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}
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@app.post("/v1/chat/completions")
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async def chat_completions(request: Request, body: ChatCompletionRequest):
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verify_auth(request)
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m, tok = load_model()
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messages = [{"role": msg.role, "content": msg.content} for msg in body.messages]
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prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tok(prompt, return_tensors="pt").to(m.device)
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prompt_len = inputs.input_ids.shape[1]
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max_new_tokens = min(body.max_tokens or MAX_TOKENS, MAX_TOKENS)
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with torch.no_grad():
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outputs = m.generate(
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**inputs,
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max_new_tokens=max_new_tokens,
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temperature=body.temperature,
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top_p=body.top_p,
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do_sample=body.temperature > 0,
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pad_token_id=tok.pad_token_id,
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)
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response = tok.decode(outputs[0][prompt_len:], skip_special_tokens=True)
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return {
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"id": f"chatcmpl-{int(time.time())}",
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"object": "chat.completion",
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"created": int(time.time()),
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"model": body.model,
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"choices": [{
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"index": 0,
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"message": {"role": "assistant", "content": response.strip()},
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"finish_reason": "stop"
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}],
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"usage": {
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"prompt_tokens": prompt_len,
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"completion_tokens": outputs.shape[1] - prompt_len,
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"total_tokens": outputs.shape[1]
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}
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}
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def chat_fn(message, history):
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m, tok = load_model()
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messages = []
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for h in history:
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messages.append({"role": "user", "content": h[0]})
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messages.append({"role": "assistant", "content": h[1]})
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messages.append({"role": "user", "content": message})
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prompt =
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inputs =
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with torch.no_grad():
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outputs =
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**inputs,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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pad_token_id=
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)
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return
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with gr.Blocks(title="MiniCPM5-1B Chat", theme=gr.themes.Soft()) as
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gr.Markdown(f"# MiniCPM5-1B Chat\n**Model:** `{MODEL_ID}`")
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gr.ChatInterface(
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fn=chat_fn,
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title=None,
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description="
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)
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app = gr.mount_gradio_app(app, demo_ui, path="/")
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import os
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import torch
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import spaces
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import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer
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MODEL_ID = os.getenv("MODEL_ID", "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking")
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model = None
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tokenizer = None
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@spaces.GPU
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def chat_fn(message, history):
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global model, tokenizer
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if model is None:
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True
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)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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messages = []
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for h in history:
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messages.append({"role": "user", "content": h[0]})
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messages.append({"role": "assistant", "content": h[1]})
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messages.append({"role": "user", "content": message})
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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pad_token_id=tokenizer.pad_token_id
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)
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return tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip()
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with gr.Blocks(title="MiniCPM5-1B Chat", theme=gr.themes.Soft()) as demo:
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gr.Markdown(f"# MiniCPM5-1B Chat\n**Model:** `{MODEL_ID}`\n\nPowered by ZeroGPU free GPU")
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gr.ChatInterface(
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fn=chat_fn,
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title=None,
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description="First request loads the model (~30s), subsequent calls are faster."
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)
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requirements.txt
CHANGED
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gradio>=4.21.0,<5.0
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fastapi>=0.100.0
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uvicorn>=0.23.0
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transformers>=4.36.0
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accelerate>=0.25.0
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torch>=2.1.0
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sentencepiece>=0.1.99
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transformers>=4.36.0
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accelerate>=0.25.0
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sentencepiece>=0.1.99
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temp_page.html
ADDED
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0 0 0 18 2H8a2.006 2.006 0 0 0-2 2v24a2.006 2.006 0 0 0 2 2h16a2.006 2.006 0 0 0 2-2V10a.908.908 0 0 0-.3-.7zM18 4.4l5.6 5.6H18zM24 28H8V4h8v6a2.006 2.006 0 0 0 2 2h6z" fill="currentColor"></path></svg><!----> <span class="truncate group-hover:underline">README.md</span></a> <!--[-1--><!--]--> <!--[-1--><!--]--></div> <a class="group col-span-9 flex items-center justify-self-end truncate text-right font-mono text-[0.8rem] leading-6 text-gray-400 md:col-span-6 lg:col-span-4 xl:pr-10" title="Download file" download="" href="/spaces/Arooxy/qwe/resolve/main/README.md?download=true"><span class="truncate max-sm:text-xs">283 Bytes</span> <!--[-1--><!--]--> <div class="group-hover:shadow-xs ml-2 flex h-5 w-5 items-center justify-center rounded-sm border text-gray-500 group-hover:bg-gray-50 group-hover:text-gray-800 dark:border-gray-800 dark:group-hover:bg-gray-800 dark:group-hover:text-gray-300"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" 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