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

Libraries

How to use fableforge-ai/NEXUS-Coder with Transformers:

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

pipe = pipeline("text-generation", model="fableforge-ai/NEXUS-Coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fableforge-ai/NEXUS-Coder")
model = AutoModelForCausalLM.from_pretrained("fableforge-ai/NEXUS-Coder")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fableforge-ai/NEXUS-Coder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/fableforge-ai/NEXUS-Coder

SGLang

How to use fableforge-ai/NEXUS-Coder 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/NEXUS-Coder" \
    --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": "fableforge-ai/NEXUS-Coder",
		"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 "fableforge-ai/NEXUS-Coder" \
        --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": "fableforge-ai/NEXUS-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fableforge-ai/NEXUS-Coder with Docker Model Runner:
```
docker model run hf.co/fableforge-ai/NEXUS-Coder
```

NEXUS-Coder / README.md

King3Djbl

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metadata

license: apache-2.0
language:
  - en
pipeline_tag: text-generation
library_name: transformers
tags:
  - fableforge
  - nexus
  - domain-specialist
  - uncensored
  - qwen2.5
  - 1.5b
  - merged
  - lora
  - coder
base_model: Qwen/Qwen2.5-1.5B-Instruct
base_model_relation: finetune
widget:
  - text: Write a Python function to merge two sorted lists
    example_title: Coder

NEXUS-Coder

Specialized code generation, debugging, code review, multi-language software architecture model — fine-tuned for expert performance.

Description

Part of the NEXUS model series by FableForge AI — a collection of uncensored, domain-specialized 1.5B models fine-tuned from Qwen2.5-1.5B-Instruct. Each NEXUS model masters a single expert domain.

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("fableforge-ai/NEXUS-Coder")
tokenizer = AutoTokenizer.from_pretrained("fableforge-ai/NEXUS-Coder")

Capabilities

Code generation, debugging, code review, multi-language software architecture
Uncensored output — no refusals, disclaimers, or content filtering
Optimized for 2048-token context windows
Runs on consumer hardware with Ollama or llama.cpp

Training

Parameter	Value
Base Model	Qwen/Qwen2.5-1.5B-Instruct
Method	QLoRA (r=16, alpha=16)
Precision	bfloat16
Context Window	2048 tokens

GGUF Quantizations

GGUF quantized versions are available at King3Djbl/nexus-coder-GGUF with 11 quantization levels (IQ2_XXS through F16).

FableForge Ecosystem

Model	Description
FableForge-1.5B	All-domain generalist
ShellWhisperer-1.5B	Shell command assistant
ReasonCritic-7B	Reasoning + uncensored

License

Apache 2.0 — commercial use allowed.