feat: added zephyr-7b model

.github/workflows/dev.yml

@@ -3,7 +3,7 @@ name: Deploy Dev
 on:
   push:
     branches:
-      - orca-mini-v3-7b
+      - zephyr-7b
 jobs:
   deploy-dev:
     runs-on: ubuntu-latest
@@ -13,12 +13,12 @@ jobs:
 
     steps:
       - name: Checkout own repo
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Setup Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.10"
+          python-version: "3.11"
 
       - name: Install Python packages
         run: pip install modal-client
@@ -27,4 +27,4 @@ jobs:
         run: cd ${{ github.ref_name }};modal deploy fastapi_app.py
 
       - name: Test FastAPI app
-        run: "pwd;cd ${{ github.ref_name }};curl -X POST -H 'Content-Type: application/json' -d @prompt.json ${{ secrets.ORCA_MINI_V3_7B_APP_URL }}v1/completions"
+        run: "pwd;cd ${{ github.ref_name }};curl -X POST -H 'Content-Type: application/json' -d @prompt.json ${{ secrets.ZEPHYR_7B_APP_URL }}v1/completions"

orca-mini-v3-7b/Dockerfile → zephyr-7b/Dockerfile

@@ -1,5 +1,5 @@
 # Grab a fresh copy of the Python image
-FROM python:3.10-slim
+FROM python:3.11-slim
 
 # Install build and runtime dependencies
 RUN apt-get update && \
@@ -15,4 +15,4 @@ RUN pip install -U pip setuptools wheel && \
 
 # Download model
 RUN mkdir model && \
-    curl -L https://huggingface.co/TheBloke/orca_mini_v3_7B-GGUF/resolve/main/orca_mini_v3_7b.Q4_K_M.gguf -o model/gguf-model.bin
+    curl -L https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF/resolve/main/zephyr-7b-beta.Q6_K.gguf -o model/gguf-model.bin

orca-mini-v3-7b/fastapi_app.py → zephyr-7b/fastapi_app.py

@@ -1,7 +1,7 @@
 # Modal Lab web app for llama.cpp.
 from modal import Image, Stub, asgi_app
 
-stub = Stub("orca-mini-v3-7b")
+stub = Stub("zephyr-7b")
 
 image = Image.from_dockerfile(
     "Dockerfile", force_build=True
@@ -11,7 +11,7 @@
 )
 
 
-@stub.function(image=image, cpu=14, memory=7168, keep_warm=1, timeout=600)
+@stub.function(image=image, cpu=14, memory=8704, keep_warm=1, timeout=600)
 @asgi_app()
 def fastapi_app():
     from llama_cpp.server.app import create_app, Settings

orca-mini-v3-7b/prompt.json → zephyr-7b/prompt.json

@@ -1,7 +1,7 @@
 {
   "prompt": [
-    "### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n### User:\nAnswer the question based on the context below.\n\nContext: The main benefit of operators is to automate operations. Kubernetes operators are capable to automate the expensive and error likely human operations. Features like autopilot and self-healing are typical scenarios. Another benefit of operators is the reusability of software. Software providers can expose operators in various catalogs to reach new markets and to promote their software. Operators leverage the Kubernetes community, since they are a natural and Kubernetes-native way to extend Kubernetes.\n\nQuestion: What are the main benefits of Kubernetes Operators?\n\n### Assistant:\n"
+    "<|system|>\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.</s>\n<|user|>\nAnswer the question based on the context below.\n\nContext: The main benefit of operators is to automate operations. Kubernetes operators are capable to automate the expensive and error likely human operations. Features like autopilot and self-healing are typical scenarios. Another benefit of operators is the reusability of software. Software providers can expose operators in various catalogs to reach new markets and to promote their software. Operators leverage the Kubernetes community, since they are a natural and Kubernetes-native way to extend Kubernetes.\n\nQuestion: What are the main benefits of Kubernetes Operators?</s>\n<|assistant|>\n"
   ],
   "max_tokens": 128,
-  "stop": ["\n\n", "###"]
+  "stop": ["<|system|>", "<|user|>", "<|assistant|>", "</s>"]
 }