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Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from huggingface_hub import InferenceClient
+import uvicorn
+
+
+app = FastAPI()
+
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
+
+class Item(BaseModel):
+    prompt: str
+    history: list
+    system_prompt: str
+    temperature: float = 0.0
+    max_new_tokens: int = 1048
+    top_p: float = 0.15
+    repetition_penalty: float = 1.0
+
+def format_prompt(message, history):
+    prompt = "<s>"
+    for user_prompt, bot_response in history:
+        prompt += f"[INST] {user_prompt} [/INST]"
+        prompt += f" {bot_response}</s> "
+    prompt += f"[INST] {message} [/INST]"
+    return prompt
+
+def generate(item: Item):
+    temperature = float(item.temperature)
+    if temperature < 1e-2:
+        temperature = 1e-2
+    top_p = float(item.top_p)
+
+    generate_kwargs = dict(
+        temperature=temperature,
+        max_new_tokens=item.max_new_tokens,
+        top_p=top_p,
+        repetition_penalty=item.repetition_penalty,
+        do_sample=True,
+        seed=42,
+    )
+
+    formatted_prompt = format_prompt(f"{item.system_prompt}, {item.prompt}", item.history)
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    output = ""
+
+    for response in stream:
+        output += response.token.text
+    return output
+
+@app.post("/generate/")
+async def generate_text(item: Item):
+    return {"response": generate(item)}
+

requirements.txt

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+fastapi
+uvicorn
+huggingface_hub
+pydantic