Zer0Fit — Zero-Shot Forecasting & Tabular MCP Server

Zer0Fit exposes Google’s TimesFM 3.0 (time-series forecasting) and TabFM v1.0.0 (tabular classification/regression) foundation models to AI assistants via the Model Context Protocol (SSE/Streamable HTTP).

Zero-shot means no training required — just connect the MCP to a chat client / harness + LLM of your choice, attach a CSV, and describe what you want to predict. No ML expertise, no hyperparameter tuning, no feature engineering.

(Example output from Open WebUI connected to Zer0Fit MCP processing the iris.csv dataset combined with a user prompt)

⚠️ Disclaimer — Use at Your Own Risk

Zer0Fit is provided “AS IS” without warranties of any kind, and is intended for research and educational purposes only. The developer is not responsible for the accuracy of predictions, classifications, or forecasts produced by the underlying models or the LLM interpreting them. This software must not be used as a basis for financial, medical, legal, safety-critical, or employment decisions. TabFM model weights are non-commercial — see the full Disclaimer.

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Features

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Prerequisites

Before running install.sh, you need a Linux server with an NVIDIA GPU and Docker set up. The installer will check for these and exit with an error if any are missing.

Hardware

Software

Verify Your Setup

Run these commands before starting the install. If any fail, install the missing prerequisite using the links above.

# 1. Verify NVIDIA driver is installed and GPU is visible
nvidia-smi
# Should show your GPU name, driver version, and CUDA version

# 2. Verify Docker is installed
docker --version
# Should show Docker version 24.0 or higher

# 3. Verify Docker Compose v2 is available
docker compose version
# Should show Docker Compose version v2.x

# 4. Verify NVIDIA Container Toolkit
docker run --rm --gpus all nvidia/cuda:12.6.3-base-ubuntu24.04 nvidia-smi
# Should show your GPU inside the container — if this fails, the
# NVIDIA Container Toolkit is not properly configured

Note: Zer0Fit runs entirely inside Docker. You do not need to install CUDA, PyTorch, or Python on the host — only the NVIDIA driver, Docker, and the NVIDIA Container Toolkit. The Docker image includes everything else.

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Quick Start

1. Deploy on a GPU Server

git clone https://github.com/porespellar/Zer0Fit.git
cd Zer0Fit
./install.sh

The installer detects your architecture (ARM64 or x86_64), selects the correct CUDA base image and PyTorch wheels, builds the Docker container, and launches the server.

Manual Compose use: ./install.sh is the recommended path because it writes the architecture-specific .env. If you run Compose directly, copy .env.example to .env and set BUILDARCH, BASE_IMAGE, and TORCH_INDEX for the target host before building. The defaults shown in docker-compose.yml are the x86_64/cu124 path; ARM64/Blackwell requires the ARM64/CUDA 13.2/cu130 values from .env.example.

2. Connect to Open WebUI

Admin Settings → Integrations → Manage Tool Servers → Add Connection

• Type: MCP / Streamable HTTP
• URL: http://YOUR-SERVER-IP:8002/mcp (Streamable HTTP, preferred for OWUI 0.10+)
• URL: http://YOUR-SERVER-IP:8002/sse (SSE fallback)

You’ll see four tools registered:

• zer0fit_inspect — discover column names and data types from a file
• zer0fit_upload_csv — upload data files from chat (fallback)
• zer0fit_forecast — time-series forecasting
• zer0fit_tabular — classification and regression

3. Install the Skill (Recommended)

Workspace → Skills → Import Skill → upload openwebui/skill_content.md

This teaches the LLM which tool to use and how to interpret metrics.

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How Tool Selection Works

The LLM chooses the tool based on your prompt words — not by analyzing the data. The same CSV could be used for forecasting or classification; the LLM decides based on what you ask for.

Typical Workflow (Chat-Attached File)

1. Attach a CSV file in Open WebUI chat
2. The LLM extracts the file ID from the `` tag Open WebUI injects
3. LLM calls zer0fit_inspect with the file ID → discovers column names, types, row count
4. LLM calls the appropriate tool based on your request:
   • Forecasting: zer0fit_forecast(file_id, target_column, horizon)
   • Classification: zer0fit_tabular(file_id, target_column, task_type="classification")
   • Regression: zer0fit_tabular(file_id, target_column, task_type="regression")
5. The tool returns predictions plus pre-computed metrics — the LLM presents both

Prompt → Tool Mapping

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Suggested Prompts to Try

Forecasting (TimesFM)

Attach a time-series CSV and type: “Forecast the next 12 months.”

Or: “Predict future values for the Passengers column with a horizon of 12.”

Classification (TabFM)

Attach iris.csv and type: “Classify the species.”

Or: “Predict which category each row belongs to. Target column is species.”

Regression (TabFM)

Attach california_housing_small.csv and type: “Run a regression on this data predicting MedHouseVal.”

Or: “Predict the target column. Use regression.”

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Release verification (v1.2.4)

The TimesFM 3.0 integration was smoke-tested with the released timesfm==3.0.1 package and the live google/timesfm-3.0-pytorch checkpoint on the included airline_passengers.csv sample:

• 144 context points; 3-step forecast
• Point output shape (3,); quantile output shape (3, 9)
• All output values finite
• CPU load: 24.66s; CPU inference: 0.16s (smoke-test timings, not a performance benchmark)
• ARM64 and x86_64 release images both build and pass /health; model weights persist in the Compose-managed Hugging Face cache volume.
• Empty and all-NaN time series are rejected with a clear validation error instead of reaching model inference.

TabFM remains on the v1.0.0 pretrained weights with the upstream source at commit fbb665569425fd2f490c6576b3af967876fe11ff, which matches official main as checked for this release. No newer official TabFM model release was found.

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Performance notes

The historical DGX Spark measurements below were collected before the TimesFM 3.0 migration and should not be treated as TimesFM 3.0 benchmark results. Re-run them on the target GPU and workload before making performance claims.

The released checkpoint revision is 43046b85ec22d584a13f8098c2ed39c889e129c2. Zer0Fit pins this revision so a later mutable Hugging Face main update cannot silently change the model behind an existing installation. The current model-cache total is approximately 14.47GB for TimesFM plus both TabFM task checkpoints; allow at least 40GB free for the cache, Docker image, and build layers.

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MCP Tool Reference

zer0fit_inspect

Discover column names, data types, and row count from a data file.

Returns: Column metadata (name, dtype, non-null count, unique count, sample values).

zer0fit_upload_csv

Upload a data file to the server (for files not already attached in chat).

Returns: Server-side file path. Files auto-delete after 6 hours.

zer0fit_forecast

Zero-shot time-series forecasting via Google TimesFM 3.0.

Returns: Point forecasts, quantile forecasts (confidence intervals), and series length.

zer0fit_tabular

Zero-shot tabular classification/regression via Google TabFM v1.0.0.

Returns: Predictions, ground truth, plus a metrics block:

Classification metrics:

• accuracy — overall percent correct (e.g. 0.9467 = 94.67%)
• per_class — per-class precision, recall, F1, support
• confusion — misclassification counts (e.g. "versicolor→virginica": 4)

Regression metrics:

• r_squared — coefficient of determination
• mae — mean absolute error (in target units)
• rmse — root mean squared error
• mape_pct — mean absolute percentage error
• prediction_range / ground_truth_range — min/max values

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Clients & Integrations

Zer0Fit speaks standard MCP over SSE and Streamable HTTP. The following clients have been tested and verified:

Open WebUI (Primary)

Admin Settings → Integrations → Manage Tool Servers → Add Connection

• Type: MCP / Streamable HTTP
• URL: http://YOUR-SERVER-IP:8002/mcp
• Fallback: http://YOUR-SERVER-IP:8002/sse

All four tools (zer0fit_inspect, zer0fit_upload_csv, zer0fit_forecast, zer0fit_tabular) are automatically discovered. For best results, also install the Zer0Fit skill.

Claude Code

Configure via the CLI (--transport sse):

claude mcp add --transport sse zer0fit http://YOUR-SERVER-IP:8002/sse

Or add to your ~/.claude/settings.json:

{
  "mcpServers": {
    "zerofit": {
      "transport": "sse",
      "url": "http://YOUR-SERVER-IP:8002/sse"
    }
  }
}

All tools are discovered automatically. Call them from Claude Code using natural language — e.g., “Inspect the iris dataset and classify the species.”

Project context: The repo includes a CLAUDE.md file (auto-loaded by Claude Code) with architecture, conventions, and common commands. A Claude Code skill at .claude/skills/zerofit-workflow.md teaches Claude how to use the four MCP tools correctly.

Codex CLI

Configure via the CLI (--url for Streamable HTTP):

codex mcp add zer0fit --url http://YOUR-SERVER-IP:8002/mcp

Then use with codex exec:

codex exec "Use zer0fit to inspect the data and classify the species."

Project context: The repo includes an AGENTS.md file (auto-loaded by Codex CLI) with architecture, conventions, common commands, and Zer0Fit MCP tool usage instructions.

Not Supported Natively

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Limits & Configurability

Zer0Fit enforces several limits to protect the GPU server from OOM crashes, runaway predictions, and oversized JSON responses. These are hardcoded constants in server.py that you can adjust for your hardware.

Increasing the Tabular Chunk Limit

If you have a large GPU (e.g., 80GB H100) and need to process more than 10,000 rows per request:

# In server.py, change:
MAX_CHUNKS_LIMIT = 10    # → 20, 50, etc.

Increasing the Forecast Horizon

If you need forecasts beyond 256 steps, update the validation check in server.py and review the response-size, runtime, and GPU-memory impact on your target hardware. TimesFM 3.0 does not use the former ForecastConfig compile step; its evaluator handles patch-rounded decode windows internally.

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Project Structure

Zer0Fit/
├── install.sh               # One-command installer (architecture-aware)
├── .env.example              # Config reference
├── Dockerfile                # Multi-arch (ARM64 + x86_64)
├── docker-compose.yml        # GPU profile, reads from .env
├── requirements.txt          # Runtime dependency bounds
├── requirements-dev.txt      # Offline test dependencies
├── model_manager.py          # VRAM governor (TTL, mutual exclusion)
├── pipelines.py              # Multi-format reader, chunking, downsampling
├── server.py                 # MCP server (port 8002, Streamable HTTP + SSE)
├── README.md                 # This file
├── ARCHITECTURE.md           # Technical design doc
├── DISCLAIMER.md            # No warranty, use-at-your-own-risk notice
├── CLAUDE.md                # Claude Code project context (auto-loaded)
├── AGENTS.md                # Codex CLI project instructions (auto-loaded)
├── LICENSE                   # Apache 2.0
├── ATTRIBUTION.md            # Third-party model attributions
├── .claude/
│   └── skills/
│       └── zerofit-workflow.md  # Claude Code skill for Zer0Fit MCP tools
├── docs/
│   └── DEPLOYMENT_GUIDE.md   # Full guide for non-ML experts
├── openwebui/
│   └── skill_content.md      # Open WebUI skill (markdown)
├── .github/workflows/tests.yml # Offline regression CI
├── data/
    ├── iris.csv              # Sample: classification (150 rows)
    ├── california_housing_small.csv  # Sample: regression (2,500 rows)
    └── airline_passengers.csv  # Sample: forecasting (144 points)

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Documentation

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Attribution & Licenses

This project is licensed under the Apache License, Version 2.0. See LICENSE for details.

Google TimesFM 3.0

• Source code: google-research/timesfm
• Package: timesfm==3.0.1
• Checkpoint: google/timesfm-3.0-pytorch
• Source license: Apache License, Version 2.0
• Pretrained-weight license: TimesFM Non-Commercial License v1.0; non-commercial, non-production use only
• Citation: Das, A., et al. “A decoder-only foundation model for time-series forecasting.” ICML 2024. — Paper

Google TabFM v1.0.0

• Source: google-research/tabfm
• Release: v1.0.1 source snapshot at commit fbb665569425fd2f490c6576b3af967876fe11ff
• Weights: v1.0.0, under the TabFM Non-Commercial License v1.0
• Copyright: 2026 Google LLC
• Citation: TabFM: A Zero-Shot Foundation Model for Tabular Data — Google Research Blog

Sample Datasets

• Iris — R.A. Fisher, 1936. Public domain benchmark dataset.
• Airline Passengers — Box & Jenkins, 1976. Public domain time-series dataset.
• California Housing — Pace & Barry, 1997. Public domain regression dataset.

TimesFM source code and package are Apache 2.0, but TimesFM 3.0 pretrained weights are separately licensed under the TimesFM Non-Commercial License v1.0 and are restricted to non-commercial, non-production use. TabFM source code is Apache 2.0, while TabFM model weights are separately licensed under the TabFM Non-Commercial License v1.0. No modifications have been made to the original model weights. This project provides a server/transport layer around these models; the models themselves are separate works with their own licenses. See DISCLAIMER.md and ATTRIBUTION.md for details.

Testing

Install the optional development dependencies and run the offline regression suite:

python3 -m venv .venv
.venv/bin/pip install -r requirements-dev.txt
.venv/bin/pytest -q

The live TimesFM checkpoint smoke test is intentionally opt-in because it downloads model weights:

.venv/bin/python tests/smoke_timesfm3.py