README.md

---
license: apache-2.0
language:
- en
base_model:
- deepseek-ai/deepseek-coder-6.7b-instruct
tags:
- code
- Festi
- php
- developer-agent
---

# Festi Coder Full 2025-06

This is a fully fine-tuned version of `deepseek-ai/deepseek-coder-6.7b-instruct`, built by [Festi](https://festi.io) to support advanced backend development on the Festi Framework. The model is trained on real-world Festi codebases and supports tasks like plugin generation, trait and service scaffolding, and backend automation.

---

## Model Details

### Model Description

- **Developed by:** Festi
- **Model type:** Causal Language Model (full fine-tune)
- **Base model:** [`deepseek-ai/deepseek-coder-6.7b-instruct`](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct)
- **Language(s):** English, PHP (Festi syntax)
- **License:** Apache-2.0
- **Fine-tuned with:** Transformers (no LoRA)

---

## Uses

### Direct Use

This model is intended for developers working in the Festi ecosystem who want to:

- Generate Festi plugins, services, CLI commands, and traits
- Edit and extend existing Festi modules
- Explain and document PHP code following Festi patterns

### Out-of-Scope Use

- Natural language chat or general NLP tasks
- Use with non-Festi PHP frameworks (e.g., Laravel, Symfony)
- Autonomous execution without human validation

---

## Bias, Risks, and Limitations

This is a domain-specific model, not suitable for general-purpose programming. The code generated may contain syntactic or semantic issues and should be reviewed by experienced developers before use in production.

### Recommendations

- Validate model output before use
- Use only in backend contexts aligned with Festi's architecture
- Do not expose model output to end-users directly

---

## How to Get Started with the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_id = "Festi/festi-coder-full-2025-06"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "<|user|>\nCreate a plugin to subscribe users via email.\n<|assistant|>\n"
output = generator(prompt, max_new_tokens=300)
print(output[0]["generated_text"])