README.md

---
license: apache-2.0
base_model: build-small-hackathon/deku
tags:
- gguf
- llama.cpp
- knowledge-distillation
- qwen2
pipeline_tag: text-generation
---

# Deku — GGUF (llama.cpp)

GGUF builds of [**build-small-hackathon/deku**](https://huggingface.co/build-small-hackathon/deku),
the One for All student: a Qwen2.5-0.5B distilled from 6 teachers via gated CKA
geometry distillation. The LoRA adapter is merged into the base, then converted
with `llama.cpp`'s `convert_hf_to_gguf.py`.

## Files

| File | Size | Use |
|------|------|-----|
| `deku-q8_0.gguf` | ~531 MB | what the Space serves — near-lossless, CPU-friendly |
| `deku-f16.gguf` | ~994 MB | archival full-precision build |
| `gating.npz` | ~22 KB | the teacher-gating head as numpy (`weight` 6×896, `bias` 6) |

## Run

```bash
llama-cli -m deku-q8_0.gguf -p "Explain gradient descent in one sentence."
```

```python
from llama_cpp import Llama
llm = Llama(model_path="deku-q8_0.gguf", n_ctx=2048)
print(llm.create_chat_completion(
    messages=[{"role": "user", "content": "Why is the sky blue?"}]
)["choices"][0]["message"]["content"])
```

## Teacher gating without torch

`gating.npz` lets you reproduce the live "teacher influence" meters from the
[Space](https://huggingface.co/spaces/build-small-hackathon/one-for-all) using
only numpy on a mean-pooled embedding from `llama.cpp`:

```python
import numpy as np
g = np.load("gating.npz")          # g["weight"] (6, 896), g["bias"] (6,)
def gate(emb):                     # emb: 896-dim pooled embedding
    z = g["weight"] @ emb + g["bias"]
    e = np.exp(z - z.max())
    return e / e.sum()             # softmax over the 6 teachers
```

Teacher order: `qwen, smollm, phi, gemma, minicpm, nemotron`.