| | --- |
| | license: cc-by-nc-4.0 |
| | datasets: |
| | - iamtarun/python_code_instructions_18k_alpaca |
| | language: |
| | - en |
| | base_model: Pacific-Prime/pacific-prime-code |
| | tags: |
| | - code |
| | - python |
| | - i64 |
| | - complexity-deep |
| | - sft |
| | pipeline_tag: text-generation |
| | library_name: transformers |
| | --- |
| | |
| | # Pacific-Prime: Python Node |
| |
|
| | **Pure Python specialist** fine-tuned from Pacific-Prime Code (I64 architecture, 1.5B parameters). |
| |
|
| | ## Skills |
| |
|
| | - Python basics & standard library |
| | - Algorithms & data structures |
| | - Object-oriented programming |
| | - Decorators & generators |
| | - List comprehensions |
| | - File I/O & error handling |
| |
|
| | ## Training |
| |
|
| | - **Architecture**: I64 (Complexity-Deep) |
| | - **Parameters**: 1.5B |
| | - **Base model**: pacific-prime-code (checkpoint epoch 70) |
| | - **Method**: Full SFT (no LoRA) |
| | - **Dataset**: python_code_instructions_18k_alpaca (18K samples) |
| | - **Epochs**: 1000 |
| | - **Max context**: 4096 tokens |
| |
|
| | ## Inference with vLLM-I64 |
| |
|
| | Use our custom vLLM engine with native I64 support: |
| |
|
| | 👉 **[vllm-i64](https://github.com/Complexity-ML/vllm-i64)** |
| |
|
| | ```bash |
| | git clone https://github.com/Complexity-ML/vllm-i64.git |
| | cd vllm-i64 |
| | pip install -e . |
| | ``` |
| |
|
| | ```python |
| | from vllm import LLM, SamplingParams |
| | |
| | model = LLM(model="Pacific-Prime/python-node") |
| | params = SamplingParams(temperature=0.7, max_tokens=4096) |
| | |
| | prompt = "User: Write a Python function to find the longest common subsequence of two strings.\nAssistant:" |
| | output = model.generate([prompt], params) |
| | print(output[0].outputs[0].text) |
| | ``` |
| |
|
| | ## Serve Your Own I64 Model |
| |
|
| | Trained your own I64 model with [complexity-deep](https://github.com/Complexity-ML/complexity-deep)? Serve it with vllm-i64: |
| |
|
| | ```python |
| | from vllm import LLM, SamplingParams |
| | |
| | model = LLM(model="/path/to/your/i64-model") |
| | params = SamplingParams(temperature=0.7, max_tokens=4096) |
| | output = model.generate(["User: Hello!\nAssistant:"], params) |
| | print(output[0].outputs[0].text) |
| | ``` |
| |
|
| | ## Links |
| |
|
| | - [Complexity Framework](https://github.com/Complexity-ML/complexity-framework) — ML framework for building & training I64 models |
| | - [Complexity-Deep](https://github.com/Complexity-ML/complexity-deep) — Training framework & architecture |
| | - [vllm-i64](https://github.com/Complexity-ML/vllm-i64) — Inference engine for I64 models |
| |
|
| | ## License |
| |
|
| | CC BY-NC 4.0 |
| |
|
