Text Generation
Transformers
Safetensors
qwen2
coder
code
agent
conversational
text-generation-inference
Instructions to use AdminReal/NexusCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| # 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) β `nexus/model/rope.py` | |
| - FusedLinear pattern (concatenated Q/K/V projections) β `nexus/integrations/litgpt.py` | |
| - PyTorch SDPA backend selection β `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 β unified Nexus format) β `nexus/integrations/llamafactory.py` | |
| - Concept of unified dataset registry β `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) β `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) β `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 β `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](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 | | |
| |------|-------------|--------------| | |