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
7B	codellama/CodeLlama-7b-hf, deepseek-ai/deepseek-coder-7b-instruct-v1.5, Qwen/Qwen2.5-Coder-7B-Instruct
13-15B	codellama/CodeLlama-13b-Instruct-hf, bigcode/starcoder2-15b, Qwen/Qwen2.5-Coder-14B-Instruct
32-34B	deepseek-ai/deepseek-coder-33b-instruct, codellama/CodeLlama-34b-Instruct-hf, Qwen/Qwen2.5-Coder-32B-Instruct
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 35+ tools. Any AI assistant that supports MCP can fine-tune models, manage training jobs, run evaluations, check inference usage, and manage datasets 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

Datasets

Command	Description
te datasets list	List all datasets
te datasets show <id>	Show dataset details
te datasets create	Create a dataset from S3 (--name, --s3-url, --for-evaluation)
te datasets delete <id>	Delete a dataset
te datasets status <id>	Check import/processing status

Evaluations

Command	Description
te evals list	List all evaluations
te evals show <id>	Show evaluation details and scores
te evals create	Run an evaluation (--model, --dataset, --evaluators)
te evals cancel <id>	Cancel a running evaluation
te evals status <id>	Live evaluation progress
te evals evaluators	List available evaluators
te evals estimate	Cost estimate for an evaluation

Inference

Command	Description
te inference models	List available inference models
te inference usage	Show inference API usage stats
te inference jwt	Get a JWT for direct API access

Agents

Command	Description
te agents list	List available agents
te agents show <id>	Show agent details and capabilities

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

Training Jobs

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

Models

Tool	Description
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

Marketplace

Tool	Description
list_catalog_models	Browse pre-built models and datasets
get_catalog_model	Details of a marketplace item
export_catalog_model	Export marketplace item to S3
catalog_export_status	Check marketplace export progress

Datasets

Tool	Description
list_datasets	List datasets for training and evaluation
show_dataset	Dataset details and status
create_dataset	Create a dataset from S3
delete_dataset	Delete a dataset
dataset_status	Check dataset import/processing status

Evaluations

Tool	Description
list_evaluations	List model evaluations
show_evaluation	Evaluation details, scores, and metrics
create_evaluation	Run an evaluation against a dataset
cancel_evaluation	Cancel a running evaluation
evaluation_status	Live evaluation progress
list_evaluators	Available evaluators (code_execution, similarity, llm_judge, etc.)
estimate_evaluation	Cost estimate for an evaluation

Inference

Tool	Description
list_inference_models	Models available for inference
inference_usage	Inference API usage statistics
get_inference_jwt	Get JWT token for direct API access

Agents

Tool	Description
list_agents	List available agents
show_agent	Agent details and capabilities

Account

Tool	Description
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