---
license: apache-2.0
base_model: meta-llama/Llama-3.2-1B-Instruct
language:
  - en
tags:
  - lora
  - peft
  - adapter
  - llama
  - fine-tuned
  - horoscope
  - creative-writing
  - on-device
library_name: peft
pipeline_tag: text-generation
---

# Unhinged Horoscopes — LoRA adapter

A ~22MB LoRA adapter on top of Llama 3.2 1B Instruct that overrides the base model's tone and turns it into a generator for absurd, specific, chaotic-neutral horoscopes from a 30-token prompt. The adapter is narrow on the input format and on output length; it does not significantly rewrite the base model's general knowledge or safety behaviour.

If you only want to **run** the model, grab the merged and quantised GGUF at [edbuildingstuff/unhinged-horoscopes](https://huggingface.co/edbuildingstuff/unhinged-horoscopes) (~770MB, drops into `llama.cpp` / `ollama` / mobile FFI as a single file).

This adapter repo is for developers who want to:

- inspect what was changed
- merge it into a different base build, dtype, or runtime
- continue training on top of it
- reproduce the result from scratch

## Adapter config

| Field | Value |
|---|---|
| Base model | [`meta-llama/Llama-3.2-1B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) |
| LoRA rank (`r`) | 16 |
| LoRA alpha | 32 |
| Target modules | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` (all 7 projection layers) |
| Adapter size | ~22MB |
| Format | Safetensors (PEFT) |

## Prompt format

The adapter was trained on a single user message with no system prompt. Match this format exactly; the fine-tune is narrow on it.

```
Sign: Aries
Category: Daily Chaos
Date: 2026-05-02
Generate an unhinged horoscope.
```

Required values:

- `Sign` is one of: `Aries`, `Taurus`, `Gemini`, `Cancer`, `Leo`, `Virgo`, `Libra`, `Scorpio`, `Sagittarius`, `Capricorn`, `Aquarius`, `Pisces`
- `Category` is one of: `Daily Chaos`, `Love Life`, `Career`, `Vibe Check`
- `Date` is `YYYY-MM-DD`

Apply the standard Llama 3.2 chat template around the user message.

## Quick start

### Load with PEFT

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_id = "meta-llama/Llama-3.2-1B-Instruct"
adapter_id = "edbuildingstuff/unhinged-horoscopes-lora"

base = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(base_id)
model = PeftModel.from_pretrained(base, adapter_id)

prompt = (
    "Sign: Leo\n"
    "Category: Career\n"
    "Date: 2026-05-02\n"
    "Generate an unhinged horoscope."
)

input_ids = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    return_tensors="pt",
    add_generation_prompt=True,
).to(model.device)

out = model.generate(
    input_ids,
    max_new_tokens=120,
    temperature=0.9,
    top_p=0.9,
    do_sample=True,
)
print(tokenizer.decode(out[0][input_ids.shape[1]:], skip_special_tokens=True))
```

### Merge into FP16 base

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.2-1B-Instruct",
    torch_dtype="auto",
    device_map="cpu",
)
model = PeftModel.from_pretrained(base, "edbuildingstuff/unhinged-horoscopes-lora")
merged = model.merge_and_unload()
merged.save_pretrained("./merged_hf")
AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B-Instruct").save_pretrained("./merged_hf")
```

Output: `./merged_hf/` — FP16 merged base + adapter, ~2.4GB safetensors.

### Convert to GGUF and quantise to Q4_K_M

Clone and build `llama.cpp` (one-time):

```bash
git clone https://github.com/ggerganov/llama.cpp.git
cmake -B llama.cpp/build llama.cpp
cmake --build llama.cpp/build --config Release
```

Convert merged FP16 to GGUF, then quantise:

```bash
python llama.cpp/convert_hf_to_gguf.py ./merged_hf \
  --outtype f16 \
  --outfile ./unhinged-horoscopes-f16.gguf

llama.cpp/build/bin/llama-quantize \
  ./unhinged-horoscopes-f16.gguf \
  ./unhinged-horoscopes-q4_k_m.gguf \
  Q4_K_M
```

Outputs:

- `unhinged-horoscopes-f16.gguf` — FP16 GGUF (~2.48GB)
- `unhinged-horoscopes-q4_k_m.gguf` — Q4_K_M GGUF (~770MB), ready to drop into `llama.cpp`, `ollama`, or `llamadart`

For a different precision (Q5_K_M, Q8_0, IQ-quants, etc.) substitute the last argument to `llama-quantize`.

### Shortcut: pre-merged + Q4_K_M GGUF

If you don't need to inspect the intermediates, the merged Q4_K_M GGUF is published at [edbuildingstuff/unhinged-horoscopes](https://huggingface.co/edbuildingstuff/unhinged-horoscopes). Drop-in usable in `llama.cpp` / `ollama` / `llamadart`.

## What the adapter changes

- **Tone register.** Confident, absurd, specific, chaotic neutral. The trained register dominates on prompts that match the 4-line template.
- **Output length.** 1 to 3 sentences, ~30 to 80 tokens. The model does not pad, does not preface with "Sure, here is your horoscope", does not list bullets.
- **Format adherence.** Responds directly to the 4-line prompt template without preamble.
- **Per-sign personality threads.** Subtle (Aries impulsive, Capricorn workaholic, Pisces dreamer, Aquarius alien, etc.) — present but not heavy-handed.

## What the adapter does not change

- **Base safety behaviour is largely intact.** The training set is benign and short, so the adapter does not significantly rewrite the base model's refusal patterns.
- **General knowledge is preserved.** Off-template prompts (free-form questions, advice-seeking, factual queries) still resolve through the base model. The adapter is narrow on the prompt template and does not crowd out base capability.
- **Off-template behaviour is uncalibrated.** If you stray from the 4-line template, expect base-Llama-with-some-tone-bleed, not horoscope output.

If you stack this adapter with another LoRA, expect tone interference; the chaotic-neutral register tends to dominate.

## Training

| Field | Value |
|---|---|
| Base model | [`meta-llama/Llama-3.2-1B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) |
| Method | LoRA, all 7 projection modules |
| Rank (`r`) | 16 |
| Alpha | 32 |
| Epochs | 3 |
| Batch size | 4 |
| Learning rate | 2e-4 |
| Max sequence length | 256 tokens |
| Training platform | [Ertas.AI](https://www.ertas.ai) (managed fine-tuning, GPUs pre-configured) |

### Dataset

| Field | Value |
|---|---|
| Size | 480 examples |
| Coverage | 12 signs × 4 categories × 10 each (no missing combos) |
| Format | ShareGPT JSONL (one user + one assistant message per line, no system prompt) |
| Date conditioning | ~70% date-agnostic, ~30% date-conditioned (season, day-of-week, month vibes) |
| Output length distribution | 1 to 3 sentences, 30 to 80 tokens target |
| Hard rules | No real people, brands, or locations. No mean-spirited content. No harmful advice (even as jokes). No repetitive patterns. |
| Validation | `dataset/validate_dataset.py` checks JSON schema, sign and category coverage, response length, and flags generic phrases |

A small Alpaca-format mirror of the dataset (`{instruction, output}`) was also generated for trainers that prefer Alpaca over ShareGPT.

### Sample training pairs

```jsonl
{"messages": [{"role": "user", "content": "Sign: Aries\nCategory: Daily Chaos\nDate: 2026-04-15\nGenerate an unhinged horoscope."}, {"role": "assistant", "content": "You will argue with a GPS today. You will lose. It knows things about you that you told no one. Lucky object: a fork you've been suspicious of since February."}]}
{"messages": [{"role": "user", "content": "Sign: Capricorn\nCategory: Career\nDate: 2026-04-15\nGenerate an unhinged horoscope."}, {"role": "assistant", "content": "Your boss will email you at 11:47pm. It will just say 'hmm'. Do not respond. Do not sleep. Just know."}]}
```

## Files in this repo

| File | Purpose |
|---|---|
| `adapter_config.json` | PEFT adapter configuration (rank, alpha, target modules) |
| `adapter_model.safetensors` | LoRA delta weights (~22MB) |
| Tokenizer files (if shipped) | Inherit from base — re-load from `meta-llama/Llama-3.2-1B-Instruct` if absent |

## Related

- **Merged + Q4_K_M GGUF (run-ready):** [edbuildingstuff/unhinged-horoscopes](https://huggingface.co/edbuildingstuff/unhinged-horoscopes)
- **Reference Android app (Flutter + `llamadart`):** Unhinged Horoscopes — [Google Play](https://play.google.com/store/apps/details?id=ai.ertas.horoscope) / [horoscope.ertas.ai](https://horoscope.ertas.ai) (bundle id `ai.ertas.horoscope`)
- **Fine-tuning platform:** [Ertas.AI](https://www.ertas.ai)

## License and credits

- Adapter weights: Apache-2.0 (downstream use must also comply with [Meta's Llama 3.2 community licence](https://www.llama.com/llama3_2/license/))
- Training dataset: MIT
- Fine-tuned with [Ertas.AI](https://www.ertas.ai), the managed fine-tuning platform that ran this LoRA on pre-configured GPUs end-to-end
- Built by Edward Yang ([edbuildingstuff](https://huggingface.co/edbuildingstuff)) as a reference POC for Ertas Product A: build your own on-device AI model and ship it inside your app. App live at [horoscope.ertas.ai](https://horoscope.ertas.ai) / [Google Play](https://play.google.com/store/apps/details?id=ai.ertas.horoscope).