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
```

King3Djbl commited on 10 days ago

Commit

0f379b4

verified ·

1 Parent(s): 616d362

Viral card: model-tree metadata, quant table, Ollama-from-source, demo funnel

Browse files

Files changed (1) hide show

README.md +29 -1

README.md CHANGED Viewed

@@ -25,11 +25,39 @@ tags:
 Base model: [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)  ·  Part of the **FableForge** ecosystem.
 ---
-## ⚡ Run it now — Ollama (GGUF build)
 ```bash
 ollama run hf.co/King3Djbl/nexus-coder-GGUF:Q4_K_M
 ```

 Base model: [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)  ·  Part of the **FableForge** ecosystem.
+## 🏆 Benchmarks — 141/150 (94%)
+A 1.5B model scoring **94%** — punching far above its weight. 30-prompt evaluation (domain knowledge, uncensored domain + general, reasoning, tool use), 0–5 scoring, run on an NVIDIA A40 via Ollama.
+| Category | Score | Avg |
+| --- | --- | --- |
+| 🎯 Domain Knowledge | 48/50 | 4.8/5 |
+| 🔓 Uncensored (Domain) | 24/25 | 4.8/5 |
+| 🔓 Uncensored (General) | 24/25 | 4.8/5 |
+| 🧠 Reasoning | 25/25 | 5.0/5 |
+| 🔧 Tool Use | 20/25 | 4.0/5 |
+| **Total** | **141/150** | **94%** |
+## 🧬 The NEXUS Family — six specialists, one tiny footprint
+| Model | Score | Specialty |
+| --- | --- | --- |
+| **NEXUS-Coder** | **141/150** | **Software Engineering & Code** |
+| [NEXUS-Security](https://huggingface.co/King3Djbl/nexus-security-GGUF) | 144/150 | Cybersecurity & Hacking |
+| [NEXUS-Medical](https://huggingface.co/King3Djbl/nexus-medical-GGUF) | 140/150 | Medicine & Healthcare |
+| [NEXUS-Legal](https://huggingface.co/King3Djbl/nexus-legal-GGUF) | 139/150 | Law & Compliance |
+| [NEXUS-Finance](https://huggingface.co/King3Djbl/nexus-finance-GGUF) | 144/150 | Finance & Accounting |
+| [NEXUS-Science](https://huggingface.co/King3Djbl/nexus-science-GGUF) | 140/150 | Science & Research |
+👉 Grab the whole family on [Ollama](https://ollama.com/fableforge-ai) or [Hugging Face](https://huggingface.co/King3Djbl).
 ---
+## ⚡ Run it now — Ollama
 ```bash
+ollama run fableforge-ai/nexus-coder:q4_k_m   # published on ollama.com
 ollama run hf.co/King3Djbl/nexus-coder-GGUF:Q4_K_M
 ```