Git Commit Summarization: OSS Models & Tools Research

Research Date: 2025-11-13 Use Case: Git commit diff to human-readable summary generation Requirements: 100% OSS, on-premise, fast batch processing, low resource usage

Executive Summary

Top Recommendation: Qwen2.5-Coder-1.5B (GGUF Q4_K_M) + Ollama

Best Model: Qwen2.5-Coder-1.5B-Instruct
Best Inference Server: Ollama (for simplicity) or vLLM (for production scale)
Expected Performance: <1 second per summary, 50-100+ tokens/sec
Hardware Requirements: 2-4GB RAM/VRAM (quantized 4-bit)
License: Apache 2.0 (commercial use allowed)

1. Lightweight Summarization Models

Top Models for Technical Text (1B-7B params)

Tier 1: Code-Aware Models (BEST for Git Commits)

Qwen2.5-Coder Series (RECOMMENDED)

Sizes: 0.5B, 1.5B, 3B, 7B, 14B, 32B
License: Apache 2.0 (except 3B and 72B variants use Qwen license)
Training: 2T tokens (87% code, 13% natural language)
Context: 128K tokens
Best for commits: 1.5B or 3B (optimal quality/speed balance)
Performance: Qwen2.5-7B achieves 84.8 on HumanEval (beats Gemma2-9B and Llama3.1-8B)
Strengths:
- Purpose-built for code understanding
- Excellent at technical text summarization
- Strong multi-turn dialogue capabilities
- Fast inference on consumer hardware

DeepSeek-Coder Series

Sizes: 1.3B, 6.7B, 33B
License: MIT (highly permissive)
Training: 2T tokens (87% code, 13% natural language)
Context: 16K window size
Performance: DeepSeek-Coder-6.7B matches CodeLlama-34B performance
Best for commits: 1.3B or 6.7B
Strengths:
- Excellent code understanding
- Project-level code completion trained
- Fill-in-the-blank capabilities
- Very permissive license

Tier 2: General Purpose Small Models (Good Alternative)

Phi-3 / Phi-3.5 Series

Size: 3.8B (quantized to ~2.4GB)
License: MIT (highly permissive)
Performance: Matches ~7B models in quality
Context: 128K tokens
Strengths:
- “Pound for pound champion” for accuracy
- Extremely well-optimized
- Runs on phones and edge devices
Best Match: Phi-3-Mini, Phi-3.5-Mini-Instruct

Llama 3.2 Series

Sizes: 1B, 3B, 8B
License: Meta Community License (commercial use allowed with conditions)
Context: 128K tokens
Performance: Llama 3.2-3B matches 70B models in summarization relevance
Best for commits: 1B or 3B
Strengths:
- Excellent instruction following
- Optimized for on-device applications
- Strong summarization capabilities
- Best all-around model under 10B (for 8B variant)

Tier 3: Ultra-Tiny Models (Speed-Optimized)

Qwen2.5-0.5B-Instruct

Sub-1B parameters
128K context window
Optimized for multi-turn dialogue
Extremely fast inference (100+ tokens/sec)

TinyLlama 1.1B

Loads in seconds
Runs on old laptops
Good for basic summarization

2. OSS LLM Inference Servers

Comparison Matrix

Feature	Ollama	vLLM	llama.cpp	LocalAI	TGI
Ease of Setup	Excellent	Good	Moderate	Good	Good
Throughput	Low-Medium	Excellent	Medium	Medium	High
Batch Processing	Limited	Excellent	Good	Good	Good
OpenAI API	Yes	Yes	Via server	Yes	Yes
GPU Support	Yes	Yes	Optional	Yes	Yes
CPU Efficiency	Good	Low	Excellent	Good	Medium
Model Format	GGUF	HF/SafeTensors	GGUF	Multiple	HF/SafeTensors
Production Ready	No	Yes	Yes	Yes	Yes
Docker Support	Yes	Yes	Yes	Yes	Yes

Detailed Comparison

Ollama (RECOMMENDED for Development)

Strengths:

Simplest setup (single command: ollama run qwen2.5-coder:1.5b)
Perfect for prototyping and development
Automatic model management
Built-in model library
OpenAI-compatible API
Easy Docker deployment
Import custom GGUF models

Weaknesses:

Limited throughput (~1-3 req/sec concurrent)
Sequential processing (default 4 parallel requests cap)
Not optimized for production scale

Performance:

Single request: Fast (50-100+ tokens/sec for small models)
Concurrent: Poor (flat performance, no scaling)
Best for: Development, prototyping, single-user

Deployment:

# Simple Docker deployment
docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
 
# Run model
ollama run qwen2.5-coder:1.5b

vLLM (RECOMMENDED for Production)

Strengths:

Highest throughput (35x more than llama.cpp at peak)
Continuous batching (dynamic batch sizing)
793 TPS peak vs Ollama’s 41 TPS
Low P99 latency (80ms vs 673ms for Ollama)
Excellent GPU utilization
Production-grade scalability
OpenAI-compatible API

Weaknesses:

More complex setup
Requires GPU for optimal performance
Higher resource baseline
Steeper learning curve

Performance:

Throughput: 3.2x Ollama’s requests/sec
Batch processing: 43x faster than sequential (152s vs 3.58s for 100 prompts)
Best for: High-concurrency production, batch processing

Deployment:

# Docker deployment
docker run --runtime nvidia --gpus all \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -p 8000:8000 --ipc=host \
  vllm/vllm-openai:latest \
  --model Qwen/Qwen2.5-Coder-1.5B-Instruct

llama.cpp

Strengths:

Maximum portability (runs anywhere)
Excellent CPU efficiency
Minimal dependencies
Fast startup time
GGUF format optimized for CPU
Most energy-efficient (int4 kernels)
Great for offline/edge deployment

Weaknesses:

Queuing model causes high TTFT
Not optimized for concurrent requests
Lower throughput than GPU solutions

Best for: Offline batch processing, CPU-only environments, edge devices

LocalAI

Strengths:

Multi-model format support
OpenAI API compatible
Good community support

Weaknesses:

Less specialized than alternatives
Medium performance

Best for: Mixed model deployments, API compatibility priority

Text Generation Inference (TGI)

Strengths:

HuggingFace ecosystem integration
Production-grade features
Good performance

Weaknesses:

More complex than Ollama
Less throughput than vLLM

Best for: HuggingFace-centric workflows

3. Quantization Strategies

Format Comparison

GGUF (RECOMMENDED for CPU/Mixed Inference)

Used by: Ollama, llama.cpp
Best for: CPU inference, Apple Silicon
Performance: 4-bit most energy-efficient
Quality: 8-bit negligible degradation, 4-bit minor degradation

GPTQ

Used by: vLLM, TGI
Best for: GPU-only inference
Performance: 5x faster than GGUF on pure GPU with optimized kernels
Quality: Similar to GGUF at same bit-width

AWQ

Best for: Balanced GPU/CPU performance
Performance: Faster than GPTQ with similar/better quality
Innovation: Skips less important weights during quantization

Recommended Quantization Levels

8-bit (Q8_0)

Almost no quality degradation
~50% size reduction
Good for quality-critical tasks
RAM: ~8GB for 7B model

4-bit (Q4_K_M - RECOMMENDED)

Minor quality degradation
~75% size reduction
Optimal balance for git commits
RAM: ~4GB for 7B model, ~2GB for 3B, ~1GB for 1.5B

3-bit (Q3_K)

Noticeable quality loss
Not recommended for technical text

Performance Benchmarks (Llama 3 8B)

Method	Bits	Accuracy (MMLU)	Perplexity	Size
FP16	16	56.2%	8.4	17GB
GPTQ	4	55.21%	8.575	4GB
AWQ	4	55.55%	8.483	4GB

4. Deployment Approaches

Option A: Ollama + Docker (Simple Development)

Best for: Development, prototyping, low-concurrency production

Architecture:

Git Commits → Batch Script → Ollama API (port 11434) → GGUF Model → Summaries

Docker Compose:

version: '3'
services:
  ollama:
    image: ollama/ollama:latest
    ports:
      - "11434:11434"
    volumes:
      - ollama:/root/.ollama
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
 
volumes:
  ollama:

Setup:

docker-compose up -d
docker exec -it ollama ollama pull qwen2.5-coder:1.5b

API Usage:

import requests
 
def summarize_commit(diff_text):
    response = requests.post('http://localhost:11434/api/generate',
        json={
            'model': 'qwen2.5-coder:1.5b',
            'prompt': f'Summarize this git commit in one line:\n\n{diff_text}',
            'stream': False
        })
    return response.json()['response']

Resource Requirements:

RAM: 4GB (with 1.5B model)
VRAM: 2GB (optional GPU acceleration)
Disk: 1GB for model
Speed: ~50-100 tokens/sec, <1s per summary

Option B: vLLM + Docker (Production Scale)

Best for: High-throughput production, batch processing at scale

Architecture:

Git Commits → Batch Processor → vLLM API (port 8000) → HF Model → Summaries
                                    ↓
                            Continuous Batching
                            Dynamic Scaling

Docker Deployment:

docker run -d --runtime nvidia --gpus all \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -p 8000:8000 \
  --ipc=host \
  --name vllm-server \
  vllm/vllm-openai:latest \
  --model Qwen/Qwen2.5-Coder-1.5B-Instruct \
  --dtype float16 \
  --max-model-len 4096

Batch Processing:

from openai import OpenAI
 
client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="token-abc123"  # dummy key
)
 
def batch_summarize_commits(diffs):
    results = []
    for diff in diffs:
        response = client.chat.completions.create(
            model="Qwen/Qwen2.5-Coder-1.5B-Instruct",
            messages=[{
                "role": "user",
                "content": f"Summarize this git commit in one line:\n\n{diff}"
            }],
            max_tokens=100,
            temperature=0.3
        )
        results.append(response.choices[0].message.content)
    return results

Resource Requirements:

RAM: 8GB
VRAM: 4GB minimum for 1.5B model
Disk: 3GB for model
Speed: 100+ tokens/sec, batch 100 commits in ~10 seconds

Option C: llama.cpp Server (Minimal Resources)

Best for: CPU-only environments, edge deployment, minimal resource usage

Deployment:

# Download GGUF model
wget https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF/resolve/main/qwen2.5-coder-1.5b-instruct-q4_k_m.gguf
 
# Run server
./llama-server -m qwen2.5-coder-1.5b-instruct-q4_k_m.gguf \
  --port 8080 \
  --ctx-size 4096 \
  --n-gpu-layers 0  # CPU only

Resource Requirements:

RAM: 2GB for 1.5B Q4 model
VRAM: 0 (CPU only)
Speed: 20-40 tokens/sec on modern CPU

5. Specific Recommendations

Primary Recommendation: Qwen2.5-Coder-1.5B

Why Qwen2.5-Coder?

Purpose-built for code understanding (87% code training data)
Apache 2.0 license (commercial use allowed)
Excellent technical text summarization
Fast inference (50-100+ tokens/sec)
Low resource usage (1-2GB quantized)
128K context window (handles large diffs)
Strong performance vs larger models
Active development and community

Model Selection by Use Case:

Use Case	Model Size	RAM/VRAM	Speed	Quality
Development/Testing	Qwen2.5-Coder-0.5B	0.5-1GB	100+ t/s	Good
Production (RECOMMENDED)	Qwen2.5-Coder-1.5B	1-2GB	70-100 t/s	Excellent
High Quality	Qwen2.5-Coder-3B	2-4GB	50-70 t/s	Excellent
Maximum Quality	Qwen2.5-Coder-7B	4-6GB	30-50 t/s	Best

Quantization Recommendation:

Development: Q4_K_M (best balance)
Production (quality): Q5_K_M or Q8_0
Production (speed): Q4_0 or Q4_K_S

Alternative Recommendation: DeepSeek-Coder-1.3B

When to use:

Need MIT license (more permissive than Apache 2.0)
Smaller footprint critical (<1GB)
Code completion features desired

Trade-offs:

Older model (less recent training data)
Smaller context (16K vs 128K)
Slightly lower performance than Qwen2.5

Backup Recommendation: Phi-3-Mini

When to use:

General purpose summarization (not just code)
MIT license required
Need proven track record

Trade-offs:

Not code-specialized
Slightly larger (3.8B vs 1.5B)

6. Inference Server Recommendations

Development Phase

Use Ollama

Simplest setup
Easy model switching
Good for experimentation
Acceptable performance for daily batch jobs

Production Phase (Low Scale)

Use Ollama

If processing <100 commits/batch
If updates are daily/weekly
If simplicity valued over performance

Production Phase (High Scale)

Use vLLM

If processing 1000+ commits/batch
If real-time/hourly updates needed
If GPU resources available
If throughput is critical

7. Expected Performance Metrics

Speed Benchmarks (Qwen2.5-Coder-1.5B Q4_K_M)

Ollama (Development)

Single commit: <1 second
10 commits sequential: 5-10 seconds
100 commits sequential: 50-100 seconds
Throughput: 1-2 commits/sec

vLLM (Production)

Single commit: <1 second
10 commits batched: 2-3 seconds
100 commits batched: 10-15 seconds
Throughput: 5-10 commits/sec

llama.cpp (CPU)

Single commit: 2-3 seconds
100 commits sequential: 200-300 seconds
Throughput: 0.3-0.5 commits/sec

Quality Benchmarks

Based on summarization research (Phi3-Mini, Llama3.2-3B):

Relevance: Matches 70B models
Coherence: Excellent for structured text
Factual Consistency: High for code/technical text
Conciseness: Shorter summaries than larger models (good for commits)

Resource Usage (1.5B Model Q4_K_M)

Ollama:

Idle: ~200MB RAM
Running: 1.5-2GB RAM/VRAM
Disk: 1GB model file

vLLM:

Idle: ~1GB VRAM
Running: 3-4GB VRAM
Disk: 3GB model file

8. Implementation Strategy

Phase 1: Proof of Concept (Week 1)

Install Ollama

curl https://ollama.ai/install.sh | sh
ollama pull qwen2.5-coder:1.5b

Test with sample commits

import subprocess
import json
 
def get_commit_diff(commit_hash):
    return subprocess.check_output(
        ['git', 'show', '--format=%B%n---', commit_hash],
        text=True
    )
 
def summarize_with_ollama(diff):
    import requests
    response = requests.post('http://localhost:11434/api/generate',
        json={
            'model': 'qwen2.5-coder:1.5b',
            'prompt': f'Write a concise one-line summary of this git commit:\n\n{diff}',
            'stream': False,
            'options': {
                'temperature': 0.3,
                'num_predict': 100
            }
        })
    return response.json()['response']

Evaluate quality on 10-20 sample commits
Measure speed and resource usage

Phase 2: Development Integration (Week 2)

Create batch processing script
Add error handling and retries
Implement caching for processed commits
Add logging and metrics

Phase 3: Production Deployment (Week 3-4)

If Ollama is sufficient:

Docker Compose deployment
CI/CD integration
Monitoring setup

If vLLM needed:

Deploy vLLM with Docker
Implement batch API client
Setup load balancing (if needed)
Monitoring and alerting

9. Existing Tools & Integration

Git Commit AI Tools (for reference)

OpenCommit

Top git commit tool
Supports Ollama + local models
Features: git hooks, GitHub Actions, conventional commits
Can use qwen2.5-coder as backend
GitHub: https://github.com/di-sukharev/opencommit

Usage with local models:

oc config set OCO_AI_PROVIDER=ollama
oc config set OCO_MODEL=qwen2.5-coder:1.5b
git add . && oc

LLMCommit

Offline-first design
Local processing only
Fast generation (2.5 seconds)

ai-commit

Simple CLI
Ollama integration

Integration Approach

Rather than using these tools directly, extract patterns:

Git hook integration
Diff parsing strategies
Prompt engineering techniques
Output formatting

10. Recommended Tech Stack

Minimal Stack (Development)

Git Repository
    ↓
Python Script (git diff extraction)
    ↓
Ollama (port 11434)
    ↓
Qwen2.5-Coder-1.5B-Instruct (Q4_K_M)
    ↓
JSON Output (summaries)

Components:

OS: Linux (Ubuntu 22.04+)
Runtime: Python 3.10+
Inference: Ollama 0.1.0+
Model: Qwen2.5-Coder-1.5B-Instruct-GGUF-Q4_K_M
RAM: 4GB minimum
Disk: 5GB

Production Stack (Scale)

Git Repository
    ↓
Batch Processing Service (Python/FastAPI)
    ↓
vLLM Server (Docker, port 8000)
    ↓
Qwen2.5-Coder-1.5B-Instruct (FP16/GPTQ)
    ↓
Database (PostgreSQL - summaries)
    ↓
API (REST/GraphQL)

Components:

Orchestration: Docker Compose / Kubernetes
Inference: vLLM 0.3.0+
API: FastAPI
Database: PostgreSQL
Monitoring: Prometheus + Grafana
GPU: NVIDIA GPU with 6GB+ VRAM
RAM: 16GB
Disk: 20GB

11. Prompt Engineering for Commits

Basic Prompt Template

PROMPT_TEMPLATE = """Write a concise one-line summary of this git commit.
Focus on what changed and why.
Use imperative mood (e.g., "Add feature" not "Added feature").
 
Git Commit:
{diff}
 
Summary:"""

Advanced Prompt (Conventional Commits)

CONVENTIONAL_PROMPT = """Analyze this git commit and generate a conventional commit message.
 
Format: <type>(<scope>): <description>
 
Types: feat, fix, docs, style, refactor, test, chore
Scope: component or file affected
Description: imperative mood, lowercase, no period
 
Git Commit:
{diff}
 
Conventional Commit:"""

Structured Output Prompt

STRUCTURED_PROMPT = """Analyze this git commit and provide a structured summary.
 
Git Commit:
{diff}
 
Respond in JSON format:
{{
  "type": "feat|fix|docs|refactor|test|chore",
  "scope": "affected component",
  "summary": "one-line description",
  "changes": ["key change 1", "key change 2"],
  "impact": "high|medium|low"
}}
 
JSON:"""

12. Performance Optimization Tips

Model-Level Optimizations

Use quantized models (Q4_K_M) - 75% smaller, minimal quality loss
Enable flash attention - 2x faster inference (vLLM)
Adjust context length - Use 4096 instead of 128K for speed
Lower temperature - 0.2-0.3 for factual summaries
Limit output tokens - Set max_tokens=100 for summaries

Server-Level Optimizations

Ollama:

# Increase parallel requests
OLLAMA_NUM_PARALLEL=8 ollama serve
 
# Set context size
OLLAMA_MAX_LOADED_MODELS=2

vLLM:

# Optimize for throughput
--max-model-len 4096 \
--gpu-memory-utilization 0.9 \
--max-num-seqs 256 \
--enable-chunked-prefill

Application-Level Optimizations

Batch commits - Process in groups of 10-100
Parallel requests - Use async HTTP clients
Cache results - Store summaries in database
Rate limiting - Avoid overloading server
Retry logic - Handle transient failures

13. Cost Analysis

Resource Costs (On-Premise)

Development Setup (Ollama + 1.5B)

Initial: $0 (use existing hardware)
Hardware: 4GB RAM, 2GB VRAM (optional)
Power: ~50W idle, 100W active
Monthly: ~$10-20 electricity

Production Setup (vLLM + 1.5B)

GPU: NVIDIA RTX 3060 (~$300) or cloud GPU
RAM: 16GB (~$50)
Power: ~200W active
Monthly: ~ $50 - 100 e l ec t r i c i t yor$ 100-200 cloud

Time Savings

Manual commit summarization:

2-3 minutes per commit
100 commits = 200-300 minutes (3-5 hours)

AI summarization:

<1 second per commit
100 commits = 100 seconds (1.5 minutes)

ROI: Saves 3-5 hours per 100 commits

14. License Summary

Model	License	Commercial Use	Restrictions
Qwen2.5-Coder (1.5B, 7B, 14B)	Apache 2.0	Yes	None
Qwen2.5-Coder (3B)	Qwen License	Yes	Check terms
DeepSeek-Coder	MIT	Yes	None
Phi-3	MIT	Yes	None
Llama 3.2	Meta Community	Yes	700M MAU limit

Inference Server	License	Commercial Use
Ollama	MIT	Yes
vLLM	Apache 2.0	Yes
llama.cpp	MIT	Yes
LocalAI	MIT	Yes
TGI	Apache 2.0	Yes

15. Next Steps & Action Items

Immediate Actions (This Week)

Install Ollama on development machine
Download Qwen2.5-Coder-1.5B model
Test with 10-20 sample commits from actual repositories
Benchmark speed and quality
Document findings and edge cases

Short-term Actions (This Month)

Long-term Considerations

Evaluate if vLLM needed for scale
Monitor model updates (Qwen2.5 → Qwen3)
Fine-tune on company-specific commit style (optional)
Integrate with CI/CD pipeline
Build web UI for browsing summaries

16. References & Resources

Model Resources

Qwen2.5-Coder:

GitHub: https://github.com/QwenLM/Qwen3-Coder
HuggingFace: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct
GGUF: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF
Ollama: https://ollama.com/library/qwen2.5-coder

DeepSeek-Coder:

GitHub: https://github.com/deepseek-ai/DeepSeek-Coder
HuggingFace: https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-instruct
GGUF: https://huggingface.co/TheBloke/deepseek-coder-1.3b-instruct-GGUF

Phi-3:

HuggingFace: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct

Inference Server Resources

Ollama:

Website: https://ollama.ai
GitHub: https://github.com/ollama/ollama
Docker: https://hub.docker.com/r/ollama/ollama

vLLM:

Website: https://vllm.ai
GitHub: https://github.com/vllm-project/vllm
Docs: https://docs.vllm.ai
Docker: https://hub.docker.com/r/vllm/vllm-openai

llama.cpp:

GitHub: https://github.com/ggerganov/llama.cpp

Additional Tools

OpenCommit:

GitHub: https://github.com/di-sukharev/opencommit

Quantization:

GPTQ: https://github.com/IST-DASLab/gptq
AWQ: https://github.com/mit-han-lab/llm-awq

17. Conclusion

For git commit summarization, the optimal stack is:

Development: Ollama + Qwen2.5-Coder-1.5B (Q4_K_M)

Simple setup, good performance, low cost
<1 second per summary
2GB RAM/VRAM required
Apache 2.0 license

Production: vLLM + Qwen2.5-Coder-1.5B (FP16 or Q8)

High throughput for batch processing
100 commits in ~10 seconds
4GB VRAM required
Scales to 1000s of commits

Both approaches are 100% OSS, run on-premise, and provide excellent quality summaries for technical content. Start with Ollama for proof-of-concept, migrate to vLLM if scale demands it.

Expected Outcomes:

95%+ reduction in summarization time
Consistent, structured commit messages
Zero external API dependencies
Full data privacy
Low operational cost

Research compiled by: Sparky AI Studio Date: 2025-11-13 Status: Ready for implementation

Raibid Labs Documentation

Explorer

git-commit-summarization-oss-models

Git Commit Summarization: OSS Models & Tools Research

Executive Summary

1. Lightweight Summarization Models

Top Models for Technical Text (1B-7B params)

Tier 1: Code-Aware Models (BEST for Git Commits)

Tier 2: General Purpose Small Models (Good Alternative)

Tier 3: Ultra-Tiny Models (Speed-Optimized)

2. OSS LLM Inference Servers

Comparison Matrix

Detailed Comparison

Ollama (RECOMMENDED for Development)

vLLM (RECOMMENDED for Production)

llama.cpp

LocalAI

Text Generation Inference (TGI)

3. Quantization Strategies

Format Comparison

Recommended Quantization Levels

Performance Benchmarks (Llama 3 8B)

4. Deployment Approaches

Option A: Ollama + Docker (Simple Development)

Option B: vLLM + Docker (Production Scale)

Option C: llama.cpp Server (Minimal Resources)

5. Specific Recommendations

Primary Recommendation: Qwen2.5-Coder-1.5B

Alternative Recommendation: DeepSeek-Coder-1.3B

Backup Recommendation: Phi-3-Mini

6. Inference Server Recommendations

Development Phase

Production Phase (Low Scale)

Production Phase (High Scale)

7. Expected Performance Metrics

Speed Benchmarks (Qwen2.5-Coder-1.5B Q4_K_M)

Quality Benchmarks

Resource Usage (1.5B Model Q4_K_M)

8. Implementation Strategy

Phase 1: Proof of Concept (Week 1)

Phase 2: Development Integration (Week 2)

Phase 3: Production Deployment (Week 3-4)

9. Existing Tools & Integration

Git Commit AI Tools (for reference)

Integration Approach

10. Recommended Tech Stack

Minimal Stack (Development)

Production Stack (Scale)

11. Prompt Engineering for Commits

Basic Prompt Template

Advanced Prompt (Conventional Commits)

Structured Output Prompt

12. Performance Optimization Tips

Model-Level Optimizations

Server-Level Optimizations

Application-Level Optimizations

13. Cost Analysis

Resource Costs (On-Premise)

Time Savings

14. License Summary

15. Next Steps & Action Items

Immediate Actions (This Week)

Short-term Actions (This Month)

Long-term Considerations

16. References & Resources

Model Resources

Inference Server Resources

Additional Tools

17. Conclusion

Graph View

Table of Contents