---
license: mit
language:
- en
base_model:
- deepseek-ai/deepseek-coder-6.7b-instruct
tags:
- code
- Festi
---
# Festi Coder LoRA 2025-06

This is a LoRA fine-tuned version of `deepseek-coder-6.7b-instruct`, optimized for generating and understanding code built on the [Festi Framework](https://festi.io). The model is designed to assist with plugin generation, trait and service scaffolding, and other automation tasks relevant to the Festi ecosystem.

---

## Model Details

### Model Description

- **Developed by:** Festi
- **Model type:** Causal Language Model with LoRA fine-tuning
- **Base model:** [`deepseek-coder-6.7b-instruct`](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct)
- **Language(s):** English, PHP (Festi-specific syntax and DSL)
- **License:** [To be specified — likely mirrors base model]
- **Fine-tuned with:** PEFT + LoRA

---

## Uses

### Direct Use

This model is intended for developers using the Festi Framework who want to:

- Generate new plugins (e.g., SubscribePlugin)
- Scaffold services, traits, CLI commands
- Complete and explain Festi-specific PHP code

### Out-of-Scope Use

- General NLP tasks (e.g., chat, summarization)
- Non-Festi PHP applications
- High-stakes decision making

---

## Bias, Risks, and Limitations

This model is domain-specific and not suitable for general-purpose programming. Generated code may require manual review, especially in production settings. It inherits any limitations and biases from its base model (`deepseek-coder-6.7b-instruct`).

### Recommendations

- Always review generated code.
- Do not expose model outputs directly to end-users without validation.

---

## How to Get Started with the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig

peft_model_id = "Festi/festi-coder-lora-2025-06"
base_model = "deepseek-ai/deepseek-coder-6.7b-instruct"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, peft_model_id)

prompt = "<|user|>\nCreate a plugin to collect emails for a newsletter subscription.\n<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))