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", device_map="auto")
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

Upload README.md with huggingface_hub

da35449 verified about 2 months ago

2.11 kB

license: apache-2.0
language:
  - en
pipeline_tag: text-generation
tags:
  - fableforge
  - nexus
  - domain-specialist
  - uncensored
  - qwen2.5
  - 1.5b
  - merged
  - lora
base_model: Qwen/Qwen2.5-1.5B-Instruct

NEXUS-Coder

Specialized code generation and analysis model

Description

Fine-tuned for code generation, debugging, code review, and software architecture across multiple programming languages.

This model was created by merging a domain-specialized LoRA adapter onto Qwen2.5-1.5B-Instruct. It is part of the NEXUS model series by FableForge AI — a collection of uncensored, domain-expert small language models.

Training

Base Model: Qwen/Qwen2.5-1.5B-Instruct
Method: QLoRA (r=16, alpha=16)
Format: 4-bit NF4 quantized LoRA, merged to bfloat16
Data: Domain-curated subset of the FableForge NEXUS training corpus (18 curated sources, ~162K examples)
License: Apache 2.0

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("fableforge-ai/NEXUS-Coder", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("fableforge-ai/NEXUS-Coder")

prompt = "<your prompt here>"
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0]))

Ollama

ollama pull fableforge-ai/nexus-coder

Quantized GGUF Versions

Quantized GGUF versions for llama.cpp / Ollama are available:

King3Djbl/NEXUS-Coder-GGUF

Includes all standard quantization formats from Q2_K through Q8_0 and F16.

Benchmarks

This model achieves strong performance on domain-specific tasks while maintaining a compact 1.5B parameter footprint. See the GGUF repository for detailed benchmark results across standard evaluation suites.