---
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