README.md

---
base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
datasets:
- Nitral-AI/Creative_Writing-ShareGPT
---

# Halcyon-1B

**Halcyon-1B** is a creatively fine-tuned variant of the **unsloth/gemma-3-1b-it-unsloth-bnb-4bit** model, specifically tailored for imaginative and expressive creative writing tasks. This model has been fine-tuned to excel in storytelling, literary exploration, and nuanced narrative construction.

---

## Model Details

- **Developed by:** [colesmcintosh](https://huggingface.co/colesmcintosh)
- **Base Model:** [unsloth/gemma-3-1b-it-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-3-1b-it-unsloth-bnb-4bit)
- **Fine-tuning Methodology:** Trained 2x faster leveraging [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

---

## Dataset

This model was fine-tuned using the [(Nitral-AI) Creative Writing ShareGPT](https://huggingface.co/datasets/Nitral-AI/Creative_Writing-ShareGPT) dataset.

---

## Capabilities

- **Creative Writing:** Exceptional at generating narratives, stories, poetry, and prose.
- **Expressive Nuance:** Generates sophisticated, context-aware, and evocative literary outputs.
- **Versatility:** Suitable for writers, creators, educators, and storytellers looking to harness AI for enhanced creative exploration.

---

## Intended Use

- **Creative Inspiration:** Idea generation, overcoming writer’s block, and expanding narrative horizons.
- **Educational Tools:** Supporting literature courses, workshops, and creative writing sessions.
- **Interactive Storytelling:** Enabling interactive fiction, dynamic content creation, and innovative narrative formats.

---

## Usage

You can quickly test Halcyon-1B using Huggingface Transformers:

```python
from unsloth import FastModel
from transformers import TextStreamer

# Load model and tokenizer
model, tokenizer = FastModel.from_pretrained(
    model_name = "colesmcintosh/Halcyon-1B",
    max_seq_length = 2048,
    load_in_4bit = True,
)

# Format prompt using Gemma-3 chat template
messages = [{
    "role": "user",
    "content": [{"type" : "text", "text" : "Write a mythological tale about how the oceans came to be."}]
}]

text_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
text_str = tokenizer.decode(text_ids)

# Generate response
outputs = model.generate(
    **tokenizer([text_str], return_tensors="pt").to("cuda"),
    max_new_tokens=64,
    temperature=1.0,
    top_p=0.95,
    top_k=64,
    streamer=TextStreamer(tokenizer, skip_prompt=True),
)
```