---
language:
- en
---
## `faizack/kronos-small-custom`

Custom Hugging Face model repo for deploying the **Kronos-small** time-series forecasting model as an Inference Endpoint.

This folder is structured so you can **zip it or `git init` it and push directly** to Hugging Face under your account [`faizack`](https://huggingface.co/faizack).

### 1. Files expected in this repo

You will need the following files in the root of the Hugging Face repo:

- `config.json` – copied or downloaded from `NeoQuasar/Kronos-small`
- `model.safetensors` – weights from `NeoQuasar/Kronos-small`
- `model.py` – Kronos model definition (from the official GitHub repo)
- `tokenizer.py` – Kronos tokenizer implementation
- `predictor.py` – `KronosPredictor` wrapper
- `inference.py` – entrypoint used by Hugging Face Inference Endpoints (already provided here)
- `requirements.txt` – Python dependencies (already provided here)

This folder currently includes:

- `README.md` (this file)
- `inference.py`
- `requirements.txt`
- `.env.example`
- `.gitattributes` (Git LFS for safetensors)
- `.gitignore`

You still need to add:

- `config.json`
- `model.safetensors`
- `model.py`
- `tokenizer.py`
- `predictor.py`

### 2. How to prepare and push to Hugging Face

From this folder:

```bash
cd kronos-small-custom

# (optional) initialize git
git init
git lfs install

# Log in to Hugging Face
huggingface-cli login

# Create the remote repo under your account
huggingface-cli repo create faizack/kronos-small-custom --type model

# Add HF remote
git remote add origin https://huggingface.co/faizack/kronos-small-custom
```

Now copy in the Kronos implementation and weights:

1. From the official Kronos GitHub repo, copy:
   - `model.py`
   - `tokenizer.py`
   - `predictor.py`
2. From `NeoQuasar/Kronos-small` on Hugging Face, download:
   - `config.json`
   - `model.safetensors`

Then commit and push:

```bash
git add .
git commit -m "Initial Kronos-small custom deployment"
git push -u origin main
```

### 3. Inference contract

`inference.py` exposes a `predict(request)` function that Hugging Face Inference Endpoints will call.

Expected JSON body:

```json
{
  "inputs": {
    "df": [
      {"open": 1.0, "high": 1.1, "low": 0.9, "close": 1.05},
      {"open": 1.05, "high": 1.12, "low": 1.0, "close": 1.08}
    ],
    "x_timestamp": ["2024-01-01T00:00:00Z", "2024-01-01T01:00:00Z"],
    "y_timestamp": ["2024-01-01T02:00:00Z", "2024-01-01T03:00:00Z"],
    "pred_len": 2,
    "T": 1.0,
    "top_p": 0.9,
    "sample_count": 1
  }
}
```

Response structure:

```json
{
  "predictions": [
    {
      "open": ...,
      "high": ...,
      "low": ...,
      "close": ...
    },
    {
      "open": ...,
      "high": ...,
      "low": ...,
      "close": ...
    }
  ]
}
```

You can adapt this contract as needed, as long as `predict` returns JSON-serializable data.