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

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	---
	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
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	base_model_relation: finetune
	---

	# 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](https://huggingface.co/Qwen/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

	```python
	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

	```bash
	ollama pull fableforge-ai/nexus-coder
	```

	## Quantized GGUF Versions

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

	- [King3Djbl/NEXUS-Coder-GGUF](https://huggingface.co/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.