Instructions to use Arioron/Amber-Fable-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Arioron/Amber-Fable-1.0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Arioron/Amber-Fable-1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Arioron/Amber-Fable-1.0")
model = AutoModelForCausalLM.from_pretrained("Arioron/Amber-Fable-1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Arioron/Amber-Fable-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Arioron/Amber-Fable-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Arioron/Amber-Fable-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Arioron/Amber-Fable-1.0

SGLang

How to use Arioron/Amber-Fable-1.0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Arioron/Amber-Fable-1.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Arioron/Amber-Fable-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Arioron/Amber-Fable-1.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Arioron/Amber-Fable-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Arioron/Amber-Fable-1.0 with Docker Model Runner:
```
docker model run hf.co/Arioron/Amber-Fable-1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A newer version of this model is available: Arioron/Vex-Amber-Mini-1.2

  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 }

Downloads last month: 14

Safetensors

Model size

2B params

Tensor type

F16

Model tree for Arioron/Amber-Fable-1.0

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

(741)

this model

Quantizations

2 models