Tuning Engines CLI & MCP Server

Own your sovereign AI model. Domain-specific fine-tuning of open-source LLMs and SLMs with total control and zero infrastructure hassle.

Tuning Engines provides specialized tuning agents to tailor top open models to your needs — fast, predictable, fully delivered. Fine-tune Qwen, Llama, DeepSeek, Mistral, Gemma, Phi, StarCoder, and CodeLlama models from 1B to 72B parameters on your data via CLI or any MCP-compatible AI assistant. LoRA, QLoRA, and full fine-tuning supported. GPU provisioning, training orchestration, and model delivery fully managed.

Training Agents

Tuning Engines uses specialized agents that control how your data is analyzed and converted into training data. Each agent produces a different kind of domain-specific fine-tuned model optimized for its use case. Current agents focus on code, with more coming for customer support, data extraction, security review, ops, and other domains.

Cody (code_repo) — Code Autocomplete Agent

Cody fine-tunes on your GitHub repo using QLoRA (4-bit quantized LoRA) via the Axolotl framework (HuggingFace Transformers + PEFT). It learns your codebase's patterns, naming conventions, and project structure to produce a fast, lightweight adapter optimized for real-time completions.

Best for: code autocomplete, inline suggestions, tab-complete, code style matching, pattern completion.

te jobs create --agent code_repo \
  --base-model Qwen/Qwen2.5-Coder-7B-Instruct \
  --repo-url https://github.com/your-org/your-repo \
  --output-name my-cody-model

SIERA (sera_code_repo) — Bug-Fix Specialist

SIERA (Synthetic Intelligent Error Resolution Agent) uses the Open Coding Agents approach from AllenAI to generate targeted bug-fix training data from your repository. It synthesizes realistic error scenarios and their resolutions, then fine-tunes a model that learns your team's debugging style, error handling conventions, and fix patterns.

Best for: debugging, error resolution, patch generation, root cause analysis, fix suggestions.

te jobs create --agent sera_code_repo \
  --quality-tier high \
  --base-model Qwen/Qwen2.5-Coder-7B-Instruct \
  --repo-url https://github.com/your-org/your-repo \
  --output-name my-siera-model

Quality tiers (SIERA only):

• low — Faster, fewer synthetic pairs (default)
• high — Deeper analysis, more training data, better results

Coming Soon

| Agent | Persona | What it does | |-------|---------|-------------| | Resolve | Mira | Fine-tunes on support tickets, macros, and KB articles for automated ticket resolution | | Extractor | Flux | Trains for strict schema extraction from docs, PDFs, and business text | | Guard | Aegis | Security-focused code reviewer that catches risky patterns and proposes safer fixes | | OpsPilot | Atlas | Incident response agent trained on runbooks, postmortems, and on-call notes |

Supported Base Models

| Size | Models | |------|--------| | 3B | Qwen/Qwen2.5-Coder-3B-Instruct | | 7-8B | codellama/CodeLlama-7b-hf, deepseek-ai/deepseek-coder-7b-instruct-v1.5, Qwen/Qwen2.5-Coder-7B-Instruct, Qwen/Qwen3-8B | | 13-15B | codellama/CodeLlama-13b-Instruct-hf, bigcode/starcoder2-15b, Qwen/Qwen2.5-Coder-14B-Instruct, Qwen/Qwen3-14B | | 22-27B | mistralai/Codestral-22B-v0.1, google/gemma-2-27b | | 30-34B | deepseek-ai/deepseek-coder-33b-instruct, codellama/CodeLlama-34b-Instruct-hf, Qwen/Qwen2.5-Coder-32B-Instruct, Qwen/Qwen3-Coder-30B-A3B, Qwen/Qwen3-32B | | 70-72B | codellama/CodeLlama-70b-Instruct-hf, meta-llama/Llama-3.1-70B-Instruct, Qwen/Qwen2.5-72B-Instruct |

Quick Start

npm install -g tuningengines-cli

# Sign up or log in (opens browser — works for new accounts too)
te auth login

# Add credits (opens browser to billing page)
te billing add-credits

# Estimate cost before training
te jobs estimate --base-model Qwen/Qwen2.5-Coder-7B-Instruct

# Train Cody on your repo
te jobs create --agent code_repo \
  --base-model Qwen/Qwen2.5-Coder-7B-Instruct \
  --repo-url https://github.com/your-org/your-repo \
  --output-name my-model

# Monitor training
te jobs status <job-id> --watch

# View your trained models
te models list

MCP Server Setup

The CLI includes a built-in MCP server with 18 tools. Any AI assistant that supports MCP can fine-tune models, manage training jobs, and check billing through natural language.

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "tuning-engines": {
      "command": "npx",
      "args": ["-y", "tuningengines-cli", "mcp", "serve"],
      "env": {
        "TE_API_KEY": "te_your_key_here"
      }
    }
  }
}

Claude Code

claude mcp add tuning-engines -- npx -y tuningengines-cli mcp serve

VS Code / Cursor / Windsurf

Add to your MCP settings (.vscode/mcp.json or equivalent):

{
  "servers": {
    "tuning-engines": {
      "command": "npx",
      "args": ["-y", "tuningengines-cli", "mcp", "serve"],
      "env": {
        "TE_API_KEY": "te_your_key_here"
      }
    }
  }
}

What the AI assistant can do

When connected, your AI assistant can:

• "Fine-tune Qwen 7B on my-org/my-repo using the SIERA agent with high quality"
• "How much would it cost to train a 32B model for 3 epochs on this repo?"
• "Check the status of my latest training job"
• "List my trained models"
• "Export my model to s3://my-bucket/models/"
• "Show my account balance"
• "Train a bug-fix specialist on this repo" (auto-selects SIERA)
• "Create an autocomplete model for this codebase" (auto-selects Cody)

The create_job tool description includes full agent details and model lists, so AI assistants automatically select the right agent and model based on what you ask for.

CLI Commands

Authentication

| Command | Description | |---------|-------------| | te auth login | Sign up or log in via browser | | te auth logout | Clear saved credentials | | te auth status | Show current auth status (email, balance) |

Training Jobs

| Command | Description | |---------|-------------| | te jobs list | List all training jobs | | te jobs show <id> | Show job details | | te jobs create | Submit a training job (--agent, --quality-tier, --base-model, --repo-url, --output-name) | | te jobs status <id> | Live status (--watch for continuous polling) | | te jobs cancel <id> | Cancel a running job | | te jobs retry <id> | Retry from last checkpoint | | te jobs estimate | Cost estimate before submitting | | te jobs validate-s3 | Pre-validate S3 credentials |

Models

| Command | Description | |---------|-------------| | te models list | List your trained models | | te models show <id> | Show model details | | te models base | List supported base models | | te models import | Import a model from S3 | | te models export <id> | Export a model to S3 | | te models delete <id> | Delete a model | | te models status <id> | Check import/export status |

Billing & Account

| Command | Description | |---------|-------------| | te billing show | Balance and transaction history | | te billing add-credits | Open browser to add credits | | te account | Account info |

Configuration

| Command | Description | |---------|-------------| | te config set-token <key> | Set API key manually | | te config set-url <url> | Override API URL | | te config show | Show current config |

All commands support --json for machine-readable output.

MCP Tools Reference

| Tool | Description | |------|-------------| | create_job | Fine-tune an LLM on a GitHub repo. Supports agent selection (Cody, SIERA), quality tier, base model, epochs, S3 export. | | estimate_job | Cost estimate before training. Returns cost range, balance, sufficiency check. | | list_jobs | List training jobs with status filter | | show_job | Full job details including agent, model, GPU usage, cost, retry info | | job_status | Live status with GPU minutes, charges, delivery progress | | cancel_job | Cancel a running/queued job | | retry_job | Retry a failed job from its last checkpoint | | validate_s3 | Test S3 credentials before submitting a job | | list_models | List trained and imported models | | show_model | Model details (status, size, base model, training job) | | delete_model | Delete a model from cloud storage | | import_model | Import a model from S3 | | export_model | Export a model to S3 | | model_status | Import/export progress | | list_supported_models | Available base models with GPU hours per epoch | | get_balance | Account balance and recent transactions | | get_account | Account details |

Environment Variables

| Variable | Description | |----------|-------------| | TE_API_KEY | API key (overrides config file) | | TE_API_URL | API URL (default: https://app.tuningengines.com) |

Authentication

te auth login uses a secure device authorization flow (same pattern as gh auth login):

1. CLI generates a device code and opens your browser
2. Sign up or log in (email/password, Google, or GitHub)
3. Click "Authorize" to grant CLI access
4. Token flows back automatically — no copy-paste

Works for both new sign-ups and existing accounts. Token saved to ~/.tuningengines/config.json with 0600 permissions.

Links

• Website
• MCP Registry
• npm
• GitHub

License

MIT