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07a91a1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | # Advanced Coding LLM - Complete Instructions
This document provides full setup, run, validation, optimization, and deployment steps for the `coding-llm` project.
## 1) Prerequisites
- Python 3.10+ (recommended 3.11/3.12)
- Git
- Internet access for first model download
- Optional: Docker Desktop
- Optional: Hugging Face account and access token
## 2) Project Setup
From project root:
```bash
cd "C:\Users\GIRISH\OneDrive\Desktop\AI model_14_04_26\coding-llm"
```
Create environment file:
```bash
copy .env.example .env
```
Install dependencies:
```bash
python tasks.py install
```
## 3) Configure `.env`
Open `.env` and set values:
- `MODEL_NAME=Qwen/Qwen2.5-Coder-1.5B-Instruct`
- `FALLBACK_MODEL_NAME=Qwen/Qwen2.5-Coder-0.5B-Instruct`
- `FINAL_FALLBACK_MODEL_NAME=sshleifer/tiny-gpt2` (optional emergency fallback)
- `FORCE_MOCK_MODE=false` (true for instant test mode)
- `API_KEY=<your_secret_key>`
- `RATE_LIMIT_PER_MINUTE=30`
- `USE_RAG=true`
## 4) Run API Locally
```bash
python tasks.py run
```
Server runs at:
- `http://127.0.0.1:8000`
Health endpoint:
- `GET http://127.0.0.1:8000/health`
## 5) Run Smoke Tests
### Full smoke test
```bash
python smoke_test.py
```
### Health-only smoke test
```bash
set SMOKE_SKIP_GENERATE=true
python smoke_test.py
```
### Combined run-and-test command
```bash
python tasks.py serve-smoke
```
This starts server, executes smoke test, and shuts server down automatically.
## 6) If Generation Is Slow on First Run
First `/generate` may take long due to model download/warmup.
Options:
- Increase timeout:
- `set SMOKE_TIMEOUT=900`
- Use mock mode for quick validation:
- set `FORCE_MOCK_MODE=true`
- Run full mode after model cache is ready.
## 7) API Usage
### Endpoint
- `POST /generate`
### Input JSON
```json
{
"instruction": "Fix this code",
"input": "def add(a,b) return a+b"
}
```
### Required Header (if API key enabled)
- `x-api-key: <API_KEY>`
### Output JSON
```json
{
"code": "...",
"explanation": "...",
"confidence": 0.0,
"important_tokens": ["..."],
"relevancy_score": 0.0,
"hallucination": false,
"latency_ms": 0
}
```
## 8) Docker Deployment
```bash
copy .env.example .env
docker compose up --build -d
```
Validate:
```bash
python smoke_test.py
```
Stop:
```bash
docker compose down
```
## 9) Hugging Face Space Deployment
Create HF token (write permission), then:
```bash
python tasks.py hf-upload --repo-id <username/coding-llm-space> --token <HF_TOKEN>
```
After upload, configure Space variables/secrets:
- `MODEL_NAME`
- `FALLBACK_MODEL_NAME`
- `FORCE_MOCK_MODE`
- `API_KEY` (if needed in your architecture)
## 10) Production Hardening Checklist
- Keep `API_KEY` enabled
- Keep rate limiting enabled (`RATE_LIMIT_PER_MINUTE`)
- Put API behind HTTPS reverse proxy
- Add logging and monitoring
- Pin model versions if strict reproducibility required
- Use `FORCE_MOCK_MODE=false` in production
## 11) Common Troubleshooting
- `WinError 10061`:
- API server is not running. Start with `python tasks.py run`.
- `401 Unauthorized`:
- `x-api-key` does not match server `API_KEY`.
- Health works but generate times out:
- model is still downloading/warming up.
- Low-quality gibberish output:
- likely fallback model path used; verify `.env` model names.
## 12) Recommended Daily Commands
- Install/update: `python tasks.py install`
- Run API: `python tasks.py run`
- Smoke: `python tasks.py smoke`
- Run+smoke: `python tasks.py serve-smoke`
- Docker up/down: `python tasks.py docker-up` / `python tasks.py docker-down`
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