c1

.gitattributes

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-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+# Use Python 3.9 as the base image
+FROM python:3.9
+
+# Set the working directory inside the container
+WORKDIR /code
+
+# Copy requirements file into the container
+COPY ./requirements.txt /code/requirements.txt
+
+# Install dependencies
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Add the model download logic here
+RUN apt-get update && apt-get install -y wget && \
+    wget -O /code/zephyr-7b-beta.Q4_K_S.gguf "https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF/resolve/main/zephyr-7b-beta.Q5_K_S.gguf"
+
+# Copy the main Python application
+COPY ./main.py /code/main.py
+
+# Expose the port for the FastAPI app
+EXPOSE 7860
+
+# Command to run the application
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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----
-title: Llm
-emoji: 😻
-colorFrom: green
-colorTo: green
-sdk: docker
-pinned: false
-license: mit
-short_description: llm deployment
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Llm
+emoji: 😻
+colorFrom: green
+colorTo: green
+sdk: docker
+pinned: false
+license: mit
+short_description: llm deployment
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

main.py

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+import os
+import requests
+from ctransformers import AutoModelForCausalLM
+from fastapi import FastAPI
+from pydantic import BaseModel
+
+# Define the public URL for the model file
+MODEL_URL = "https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF/resolve/main/zephyr-7b-beta.Q5_K_S.gguf"
+MODEL_PATH = "zephyr-7b-beta.Q4_K_S.gguf"
+
+# Download the model file if not already present
+def download_model(model_url, model_path):
+    if not os.path.exists(model_path):
+        print(f"Downloading model from {model_url}...")
+        response = requests.get(model_url, stream=True)
+        with open(model_path, "wb") as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                f.write(chunk)
+        print("Model download complete.")
+    else:
+        print("Model already exists locally.")
+
+# Ensure the model file is downloaded
+download_model(MODEL_URL, MODEL_PATH)
+
+# Load the model
+llm = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    model_type="mistral",
+    max_new_tokens=1096,
+    threads=3,
+)
+
+# Pydantic object for request validation
+class Validation(BaseModel):
+    prompt: str
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Zephyr completion endpoint
+@app.post("/llm_on_cpu")
+async def stream(item: Validation):
+    system_prompt = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.'
+    E_INST = "</s>"
+    user, assistant = "<|user|>", "<|assistant|>"
+    prompt = f"{system_prompt}{E_INST}\n{user}\n{item.prompt.strip()}{E_INST}\n{assistant}\n"
+    return llm(prompt)

requirements.txt

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+python-multipart
+fastapi
+pydantic
+uvicorn
+requests
+ctransformers