Amber Fable 1.0 is a 1.7B parameter specialized language model, fine-tuned using LoRA (Low-Rank Adaptation) on the powerful Qwen3-1.7B base model.

This model is engineered specifically for mathematical reasoning and algorithmic logic. It achieves remarkable performance on math benchmarks (75% on GSM8K) for its size class, making it a highly efficient solution for educational tools and logic-based tasks, although it trades off some general world knowledge (MMLU) to achieve this peak reasoning capability.

Developed by: Arioron
Model type: Decoder-only Transformer (LoRA Adapter)
Language(s): English
License: Apache 2.0
Finetuned from model: Qwen/Qwen3-1.7B

Model Sources

Repository: https://huggingface.co/Arioron/Amber-Fable-1.0
Documentation: Arioron Model Docs

Performance

Amber Fable 1.0 demonstrates state-of-the-art efficiency in mathematical tasks.

Benchmark	Metric	Score	Description
GSM8K	Accuracy	75.0%	Grade School Math
MATH	Accuracy	55.0%	Advanced Math Problems
HumanEval	Pass@1	42.0%	Python Coding Capability
MMLU	Accuracy	22.0%	General World Knowledge

Quick Start

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "Arioron/Amber-Fable-1.0"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Math reasoning example
messages = [
    {"role": "user", "content": "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?"},
]

input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True,
    top_p=0.9,
    pad_token_id=tokenizer.eos_token_id
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Model Summary

Model: Amber Fable 1.0 (1.7B)
Specialty: Advanced Math Reasoning
Logic: Chain-of-Thought (CoT)
Coding: Python & Algorithms (42%)
Tuning: LoRA on Synthetic/Textbooks
Base: Qwen3-1.7B (PyTorch/PEFT)
Usage: Tutoring, Puzzles & Scripts
Caution: Verify all calculations
Author: Arioron (2025) If you use this model in your research, please cite: code Bibtex @misc{amberfable1.0, title = {Amber Fable 1.0: A Specialized 1.7B Math Model}, author = {Arioron}, year = {2025}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Arioron/Amber-Fable-1.0}}

contact Email: inquiry@arioron.com Website: https://arioron.com

Documentation: https://docs.arioron.com }

XythicK/Amber-Fable-1.0-GGUF

type: text-generation name: Mathematical Reasoning dataset: name: MATH type: math split: test metrics: - name: Accuracy type: accuracy value: 55.0

Amber Fable 1.0

Model Description

Model Sources

Performance

Quick Start

Model Summary

XythicK/
Amber-Fable-1.0-GGUF