Instructions to use fableforge-ai/FableForge-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fableforge-ai/FableForge-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="fableforge-ai/FableForge-1.5B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fableforge-ai/FableForge-1.5B")
model = AutoModelForCausalLM.from_pretrained("fableforge-ai/FableForge-1.5B")

llama-cpp-python

How to use fableforge-ai/FableForge-1.5B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="fableforge-ai/FableForge-1.5B",
	filename="fableforge-1.5b-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use fableforge-ai/FableForge-1.5B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fableforge-ai/FableForge-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf fableforge-ai/FableForge-1.5B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fableforge-ai/FableForge-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf fableforge-ai/FableForge-1.5B: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 fableforge-ai/FableForge-1.5B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf fableforge-ai/FableForge-1.5B: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 fableforge-ai/FableForge-1.5B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fableforge-ai/FableForge-1.5B:Q4_K_M

Use Docker

docker model run hf.co/fableforge-ai/FableForge-1.5B:Q4_K_M

LM Studio
Jan

vLLM

How to use fableforge-ai/FableForge-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fableforge-ai/FableForge-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fableforge-ai/FableForge-1.5B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fableforge-ai/FableForge-1.5B:Q4_K_M

SGLang

How to use fableforge-ai/FableForge-1.5B 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 "fableforge-ai/FableForge-1.5B" \
    --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": "fableforge-ai/FableForge-1.5B",
		"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 "fableforge-ai/FableForge-1.5B" \
        --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": "fableforge-ai/FableForge-1.5B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use fableforge-ai/FableForge-1.5B with Ollama:
```
ollama run hf.co/fableforge-ai/FableForge-1.5B:Q4_K_M
```

Unsloth Studio

How to use fableforge-ai/FableForge-1.5B 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 fableforge-ai/FableForge-1.5B 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 fableforge-ai/FableForge-1.5B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fableforge-ai/FableForge-1.5B to start chatting

Atomic Chat new
Docker Model Runner
How to use fableforge-ai/FableForge-1.5B with Docker Model Runner:
```
docker model run hf.co/fableforge-ai/FableForge-1.5B:Q4_K_M
```

Lemonade

How to use fableforge-ai/FableForge-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fableforge-ai/FableForge-1.5B:Q4_K_M

Run and chat with the model

lemonade run user.FableForge-1.5B-Q4_K_M

List all available models

lemonade list

FableForge-1.5B / README.md

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metadata

license: apache-2.0
language:
  - en
library_name: transformers
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-1.5B-Instruct
base_model_relation: finetune
tags:
  - coding
  - fableforge
  - generalist
  - gguf
  - imatrix
  - llama.cpp
  - lm-studio
  - nexus
  - ollama
  - reasoning
  - text-generation
  - uncensored

FableForge-1.5B

FableForge generalist — all NEXUS domains distilled into one model.

Base model: Qwen/Qwen2.5-1.5B-Instruct · Part of the FableForge ecosystem.

⚡ Quickest start — Ollama (pulls straight from this repo → credits the source)

ollama run hf.co/fableforge-ai/FableForge-1.5B:Q4_K_M

🦙 llama.cpp

llama-cli -hf fableforge-ai/FableForge-1.5B:Q4_K_M -p "Hello!"

💠 LM Studio

Search fableforge-ai/FableForge-1.5B and pick a quant below.

📦 Provided quants (download one file, not the whole repo)

File	Quant	Size	Notes
fableforge-1.5b-f16.gguf	`F16`	3.09 GB	Full precision (unquantized). Reference / conversion.
fableforge-1.5b-q8_0.gguf	`Q8_0`	1.65 GB	Extremely high quality — usually overkill.
fableforge-1.5b-q6_k.gguf	`Q6_K`	1.27 GB	Very high quality, near-perfect. Recommended if you have the RAM.
fableforge-1.5b-q5_k_m.gguf	`Q5_K_M`	1.13 GB	High quality. Recommended.
fableforge-1.5b-q4_k_m.gguf	`Q4_K_M`	0.99 GB	⭐ Best size/quality balance — default pick for most users.
fableforge-1.5b-iq4_xs.gguf	`IQ4_XS`	0.90 GB	imatrix — great quality, smaller than Q4_K_S. Recommended for low RAM.
fableforge-1.5b-q4_0.gguf	`Q4_0`	0.93 GB	Legacy format — prefer Q4_K_M.
fableforge-1.5b-q3_k_m.gguf	`Q3_K_M`	0.82 GB	Lower quality; usable when RAM is tight.
fableforge-1.5b-iq3_xxs.gguf	`IQ3_XXS`	0.67 GB	imatrix very low quality.
fableforge-1.5b-q2_k.gguf	`Q2_K`	0.68 GB	Very low quality — surprisingly usable.
fableforge-1.5b-iq2_xxs.gguf	`IQ2_XXS`	0.51 GB	Tiny imatrix — last resort.

Not sure? Take Q4_K_M. Low RAM → IQ4_XS. Max quality → Q6_K.

🧠 Prompt format (ChatML)

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

🌌 See it live — the FableForge demos

This family powers a galaxy of free, interactive HF Spaces:

⭐ Like & share — it helps people find the source instead of a mirror.