base_model: Qwen/Qwen3-1.7B
library_name: transformers
model_name: Vex_Amber_mini_2.5
tags:
- generated_from_trainer
- trl
- sft
- code
- reasoning
- 2B
licence: license
license: cc-by-nc-4.0
language:
- en
- fa
- fr
metrics:
- code_eval
new_version: Arioron/Vex-Amber-Mini-1.2
pipeline_tag: text-generation
num_parameters: 2000000000
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
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
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 }