| | --- |
| | license: apache-2.0 |
| | datasets: |
| | - HuggingFaceFW/fineweb-edu |
| | - sahil2801/CodeAlpaca-20k |
| | language: |
| | - en |
| | tags: |
| | - small |
| | - cpu |
| | - fast |
| | - opensource |
| | - open |
| | - free |
| | - code |
| | base_model: |
| | - LH-Tech-AI/Apex-1.5-Instruct-350M |
| | --- |
| | |
| | # THIS IS OUR BEST MODEL - in March 2026! |
| |
|
| | **Apex 1.5 Coder: Improved reasoning, logic and code. Fixed coding bugs on Apex 1.5 Instruct by finetuning Apex 1.5 Instruct with CodeAlpaca!** |
| |
|
| |
|
| | # How to train it |
| | You can train it, using the **finetuned model** LH-Tech-AI/Apex-1.5-Instruct-350M. |
| | Then, use the prepare-script and the finetuning script in the files list of this HF model. |
| |
|
| | # How to use it |
| | You can download the `apex_1.5-coder.gguf` or use `ollama run hf.co/LH-Tech-AI/Apex-1.5-Coder-Instruct-350M`. And you can also use it in LM Studio for example, just by searching for "Apex 1.5 Coder". |
| |
|
| | Alternative for ONNX models weights: You can directly download the final model as ONNX format - so it runs without the need to install a huge Python environment with PyTorch, CUDA, etc... - as INT8 and in full precision. |
| | Use `inference.py` for local inference on CUDA or CPU! First, install `pip install onnxruntime-gpu tiktoken numpy nvidia-cudnn-cu12 nvidia-cublas-cu12` on your system (in a Python VENV for Linux users). |
| |
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| |
|
| | Have fun! :D |
