Update README.md

README.md

@@ -15,4 +15,81 @@ base_model:
 - stabilityai/stablecode-completion-alpha-3b-4k
 tags:
 - code
----
+---
+# Model Card for Lara — Hybrid Code Model (DeepSeek + StableCode)
+
+Lara is a hybrid fine‑tuned **code generation & completion model** built from  
+**DeepSeek‑Coder 6.7B** and **StableCode Alpha 3B‑4K**.  
+Designed for **general‑purpose programming** — from quick completions to multi‑file scaffolding —  
+and optionally capable of **Chandler Bing‑style sarcastic commentary** for developer amusement.
+
+MIT licensed — free to use, modify, and redistribute.
+
+---
+
+## Model Details
+
+- **Developed by:** [@dgtalbug](https://huggingface.co/dgtalbug)
+- **Funded by:** Self‑funded
+- **Shared by:** [@dgtalbug](https://huggingface.co/dgtalbug)
+- **Model type:** Causal Language Model for code generation & completion
+- **Language(s):** English (primary), multilingual code comments possible
+- **License:** MIT
+- **Finetuned from:**  
+  - [`deepseek-ai/deepseek-coder-6.7b-instruct`](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct)  
+  - [`stabilityai/stablecode-completion-alpha-3b-4k`](https://huggingface.co/stabilityai/stablecode-completion-alpha-3b-4k)
+
+---
+
+## Model Sources
+- **Repository:** [https://huggingface.co/dgtalbug/lara](https://huggingface.co/dgtalbug/lara)
+- **Paper:** N/A (based on open‑source models)
+- **Demo:** Coming soon
+
+---
+
+## Uses
+
+### Direct Use
+- Code completion in IDEs
+- Script & function generation
+- Annotated code examples for learning
+- Humorous coding commentary (optional, via prompt)
+
+### Downstream Use
+- Fine‑tune for a single language (e.g., Java‑only bot)
+- Integrate into AI coding assistants
+- Educational & training platforms
+
+### Out‑of‑Scope Use
+- Malicious code generation
+- Non‑code general chat
+- Security‑critical code without review
+
+---
+
+## Bias, Risks, and Limitations
+- May hallucinate APIs or syntax  
+- Humor mode may inject irrelevant lines  
+- Biases from public code sources may appear in output
+
+### Recommendations
+- Always review generated code before deployment
+- Use sarcasm mode in casual or learning contexts, not production
+- Test code in sandbox environments
+
+---
+
+## How to Get Started with the Model
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "dgtalbug/lara"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto")
+
+prompt = "Write a Python function to reverse a string"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=150)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))