Festi
/

festi-coder-full-2025-06

@@ -1,199 +1,77 @@
 ---
-library_name: transformers
-tags: []
 ---
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 ---
+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.ai) 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"])