Instructions to use AdminReal/NexusCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdminReal/NexusCoder with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AdminReal/NexusCoder")
model = AutoModelForCausalLM.from_pretrained("AdminReal/NexusCoder", 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 AdminReal/NexusCoder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AdminReal/NexusCoder

SGLang

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

Docker Model Runner
How to use AdminReal/NexusCoder with Docker Model Runner:
```
docker model run hf.co/AdminReal/NexusCoder
```

NexusCoder / ATTRIBUTIONS.md

AdminReal

Import NexusCoder from github.com/mhieuhonda/NexusCoder

eca5751 verified about 1 month ago

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Attributions

Nexus Coder v0.3 adapts ideas and code patterns from the following open-source projects. All credit for the original algorithms goes to their respective authors. The code in nexus/integrations/ is rewritten to integrate cleanly into Nexus Coder's architecture; it is NOT a vendored copy.

Reference Frameworks

1. LitGPT (Lightning AI)

License: Apache 2.0
Source: https://github.com/Lightning-AI/litgpt
What we adapted:
- RoPE scaling strategies (linear / NTK-aware / YaRN) → nexus/model/rope.py
- FusedLinear pattern (concatenated Q/K/V projections) → nexus/integrations/litgpt.py
- PyTorch SDPA backend selection → nexus/model/flash_attention.py
Original attribution: LitGPT: Lightning AI's LLM training toolkit. Authors: Karpathy et al. (Lightning AI), 2023-2024.

2. LLaMA Factory (hiyouga)

License: Apache 2.0
Source: https://github.com/hiyouga/LlamaFactory (also https://github.com/hiyouga/LLaMA-Factory)
What we adapted:
- Dataset format converters (Alpaca / ShareGPT / ChatML / Completion → unified Nexus format) → nexus/integrations/llamafactory.py
- Concept of unified dataset registry → nexus/data/collectors/
Original attribution: LlamaFactory: Unify Fine-tuning 100+ LLMs. Author: hiyouga.

3. Axolotl (axolotl-ai-cloud)

License: Apache 2.0
Source: https://github.com/axolotl-ai-cloud/axolotl
What we adapted:
- AxolotlStyleConfig dataclass (typed training config schema) → nexus/integrations/axolotl.py
- Concept of single-YAML training configuration
Original attribution: Axolotl: a simple tool for fine-tuning LLMs. Authors: winglian + axolotl-ai-cloud contributors.

4. OpenHands

License: MIT
Source: https://github.com/OpenHands/OpenHands
What we adapted:
- AgentLoop pattern (planner / executor / observer / reflector) → nexus/integrations/openhands.py
- Concept of structured agent loop with reflection
Original attribution: OpenHands (formerly OpenDevin): an open platform for AI software developers. Authors: OpenHands contributors.

5. omp-gym (Dylan Tirandaz)

License: MIT
Source: https://github.com/dylantirandaz/omp-gym
What we adapted:
- OpenMP optimization benchmark tasks → nexus/integrations/omp_gym.py
- Concept of "predict-the-optimization" eval task
Original attribution: omp-gym: An OpenMP optimization gym environment. Author: Dylan Tirandaz.

Other Attribution

Algorithms implemented in `nexus/model/`

RoPE: Su et al., "RoFormer: Enhanced Transformer with Rotary Position Embedding" (2021). https://arxiv.org/abs/2104.09864
FlashAttention: Dao et al., "FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness" (2022). https://arxiv.org/abs/2205.14135
FlashAttention-2: Dao, "FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning" (2023). https://arxiv.org/abs/2307.08691
ALiBi: Press et al., "Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation" (ICLR 2022). https://arxiv.org/abs/2108.12409
Sliding Window Attention: Beltagy et al., "Longformer: The Long-Document Transformer" (2020). https://arxiv.org/abs/2004.05150
YaRN: Peng et al., "YaRN: Efficient Context Window Extension of Large Language Models" (2023). https://arxiv.org/abs/2309.00071
NTK-aware RoPE scaling: bloc97, "NTK-Aware Scaled RoPE" (2023). https://www.reddit.com/r/LocalLLaMA/comments/14lzrgj/
SwiGLU: Shazeer, "GLU Variants Improve Transformer" (2020). https://arxiv.org/abs/2002.05202
RMSNorm: Zhang & Sennrich, "Root Mean Square Layer Normalization" (2019). https://arxiv.org/abs/1910.07467
GQA: Ainslie et al., "GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints" (2023). https://arxiv.org/abs/2305.13245
MoE: Shazeer et al., "Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer" (2017). https://arxiv.org/abs/1701.06538
Switch Transformer: Fedus et al., "Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity" (2021). https://arxiv.org/abs/2101.03961

Datasets referenced in `configs/sources.yaml`

The-Stack v2: BigCode, https://huggingface.co/datasets/bigcode/the-stack-v2-train-full-ids
StarCoder2-data: BigCode, https://huggingface.co/datasets/bigcode/starcoder2data
CodeParrot: CodeParrot, https://huggingface.co/codeparrot
Wikipedia: Wikimedia, https://huggingface.co/wikimedia/wikipedia
OSCAR: https://oscar-project.org
UltraChat: HuggingFaceH4, https://huggingface.co/HuggingFaceH4/ultrachat_200k
OpenHermes: teknium, https://huggingface.co/teknium/OpenHermes-2.5
OpenOrca: https://huggingface.co/Open-Orca/OpenOrca
MetaMathQA: https://huggingface.co/meta-math/MetaMathQA
GSM8K: https://huggingface.co/datasets/gsm8k
HumanEval: OpenAI, https://huggingface.co/datasets/openai_humaneval
MBPP: Google Research, https://huggingface.co/datasets/mbpp
MATH: https://huggingface.co/datasets/competition_math
FineWeb: HuggingFaceFW, https://huggingface.co/datasets/HuggingFaceFW/fineweb
Open-Web-Math: https://huggingface.co/datasets/open-web-math/open-web-math
Dolma: AllenAI, https://huggingface.co/datasets/allenai/dolma
Pile: EleutherAI, https://huggingface.co/datasets/EleutherAI/pile
C4: Google, https://huggingface.co/datasets/c4

Tools inspired by existing libraries

The Tool and Skill base classes follow the OpenAI function-calling schema pattern
Database tools wrap established client libraries (psycopg2, pymysql, redis, pymongo, etc.)
Web tools use requests + BeautifulSoup conventions

License

Nexus Coder is licensed under the MIT License (see LICENSE).

The adaptations from the above projects comply with their respective licenses:

Apache 2.0 components: retain notice, state changes
MIT components: retain copyright notice

Where algorithms are reimplemented from academic papers, the original papers are cited in the source files.

This file is part of Nexus Coder v0.3 by Hieu Louis (2026).

Contributors

Maintained by hand. Add yourself here when your PR is merged, or ask a maintainer to add you. AI agents are welcome contributors.

Date	Contributor	Contribution