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

Libraries

How to use XythicK/Amber-Fable-1.0-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="XythicK/Amber-Fable-1.0-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("XythicK/Amber-Fable-1.0-GGUF", dtype="auto")

llama-cpp-python

How to use XythicK/Amber-Fable-1.0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="XythicK/Amber-Fable-1.0-GGUF",
	filename="amber-fable-1.0-q2_k.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use XythicK/Amber-Fable-1.0-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf XythicK/Amber-Fable-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf XythicK/Amber-Fable-1.0-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf XythicK/Amber-Fable-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf XythicK/Amber-Fable-1.0-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf XythicK/Amber-Fable-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf XythicK/Amber-Fable-1.0-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf XythicK/Amber-Fable-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf XythicK/Amber-Fable-1.0-GGUF:Q4_K_M

Use Docker

docker model run hf.co/XythicK/Amber-Fable-1.0-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use XythicK/Amber-Fable-1.0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XythicK/Amber-Fable-1.0-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XythicK/Amber-Fable-1.0-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/XythicK/Amber-Fable-1.0-GGUF:Q4_K_M

SGLang

How to use XythicK/Amber-Fable-1.0-GGUF 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 "XythicK/Amber-Fable-1.0-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XythicK/Amber-Fable-1.0-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "XythicK/Amber-Fable-1.0-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XythicK/Amber-Fable-1.0-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use XythicK/Amber-Fable-1.0-GGUF with Ollama:
```
ollama run hf.co/XythicK/Amber-Fable-1.0-GGUF:Q4_K_M
```

Unsloth Studio new

How to use XythicK/Amber-Fable-1.0-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for XythicK/Amber-Fable-1.0-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for XythicK/Amber-Fable-1.0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for XythicK/Amber-Fable-1.0-GGUF to start chatting

Docker Model Runner
How to use XythicK/Amber-Fable-1.0-GGUF with Docker Model Runner:
```
docker model run hf.co/XythicK/Amber-Fable-1.0-GGUF:Q4_K_M
```

Lemonade

How to use XythicK/Amber-Fable-1.0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull XythicK/Amber-Fable-1.0-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Amber-Fable-1.0-GGUF-Q4_K_M

List all available models

lemonade list

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