Update app.py
Browse files
app.py
CHANGED
@@ -10,17 +10,26 @@ from pydantic import BaseModel
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class GenModel(BaseModel):
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question: str
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system: str = "You are a
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temperature: float = 0.
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seed: int =
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repo_id="Qwen/Qwen1.5-0.5B-Chat-GGUF",
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filename="*q4_0.gguf",
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tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B"),
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verbose=False,
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n_ctx=4096,
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#chat_format="llama-2"
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)
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# Logger setup
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@@ -67,14 +76,14 @@ def health():
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return {"status": "ok"}
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# Chat Completion API
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@app.post("/
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async def complete(gen:GenModel):
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try:
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messages=[
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{"role": "system", "content": gen.system},
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]
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st = time()
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output =
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messages = messages,
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temperature=gen.temperature,
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seed=gen.seed,
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@@ -104,16 +113,16 @@ async def complete(gen:GenModel):
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)
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# Chat Completion API
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@app.get("/
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async def complete(
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question: str,
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system: str = "You are
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temperature: float = 0.7,
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seed: int = 42,
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) -> dict:
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try:
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st = time()
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output =
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messages=[
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{"role": "system", "content": system},
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{"role": "user", "content": question},
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class GenModel(BaseModel):
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question: str
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system: str = "You are a professional medical assistant."
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temperature: float = 0.8
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seed: int = 101
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llm_chat = llama_cpp.Llama.from_pretrained(
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repo_id="Qwen/Qwen1.5-0.5B-Chat-GGUF",
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filename="*q4_0.gguf",
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tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B"),
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verbose=False,
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n_ctx=1024,
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n_gpu_layers=0,
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#chat_format="llama-2"
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)
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llm_generate = llama_cpp.Llama.from_pretrained(
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repo_id="Qwen/Qwen1.5-0.5B-Chat-GGUF",
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filename="*q4_0.gguf",
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tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B"),
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verbose=False,
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n_ctx=4096,
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n_gpu_layers=0,
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#chat_format="llama-2"
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)
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# Logger setup
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return {"status": "ok"}
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# Chat Completion API
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@app.post("/chat/")
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async def complete(gen:GenModel):
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try:
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messages=[
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{"role": "system", "content": gen.system},
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]
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st = time()
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output = llm_chat.create_chat_completion(
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messages = messages,
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temperature=gen.temperature,
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seed=gen.seed,
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)
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# Chat Completion API
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@app.get("/generate")
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async def complete(
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question: str,
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system: str = "You are an AI assistant.",
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temperature: float = 0.7,
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seed: int = 42,
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) -> dict:
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try:
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st = time()
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output = llm_generate.create_chat_completion(
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messages=[
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{"role": "system", "content": system},
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{"role": "user", "content": question},
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