Update README.md

README.md

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 ---
 license: mit
+datasets:
+- mhhmm/typescript-instruct-20k
+base_model:
+- google/gemma-4-12B
+tags:
+- typescript
+- finetuned
+- spaceoutpl
+- unsloth
+- code
+- text-generation
+- instruct
+- programming
+- gemma
+- lora
+- javascript
 ---
+
+# Gemma-4-12B TypeScript
+
+This model is a specialized, fine-tuned version of [Google's Gemma-4 12B](https://huggingface.co/google/gemma-4-12B) designed specifically for TypeScript code generation, refactoring, and instruction. It was fine-tuned efficiently using the [Unsloth](https://github.com/unslothai/unsloth) library by [spaceoutpl].
+
+## 💻 Model Details
+
+* **Base Model:** google/gemma-4-12B
+* **License:** MIT
+* **Language:** English / TypeScript
+* **Fine-tuning Framework:** Unsloth
+* **Dataset:** [mhhmm/typescript-instruct-20k](https://huggingface.co/datasets/mhhmm/typescript-instruct-20k)
+
+## 🚀 Intended Use
+
+This model is ideal for developers and researchers looking for an AI assistant heavily specialized in the TypeScript ecosystem. Use cases include:
+* **Code Generation:** Writing complex TypeScript functions, interfaces, and types.
+* **Code Refactoring:** Converting standard JavaScript to strictly-typed TypeScript.
+* **Instruction & Explanation:** Explaining TypeScript errors, generics, utility types, and best practices.
+
+## 🛠️ Getting Started
+
+You can load this model directly using the `transformers` library. Since it was trained with Unsloth, you can also utilize Unsloth's optimized inference engines for faster generation.
+
+### Installation
+
+```bash
+pip install transformers torch accelerate
+```
+
+### Usage (Hugging Face Transformers)
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "spaceoutpl/gemma-4-12B-typescript" # Update with your actual repo name
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16
+)
+
+prompt = "Write a generic TypeScript function to fetch and strictly type data from an API."
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## 📊 Training Data
+
+The model was fine-tuned on the `mhhmm/typescript-instruct-20k` dataset, which contains 20,000 high-quality instructional pairs focusing on TypeScript programming concepts, syntax, and problem-solving.
+
+## ⚠️ Limitations & Biases
+
+* **Hallucinations:** Like all LLMs, the model may occasionally generate plausible-looking but syntactically incorrect or non-compiling TypeScript code. Always test generated code in your environment.
+* **Knowledge Cutoff:** The model's knowledge is limited to the training data of the base Gemma-4 model and the specific TypeScript dataset used for fine-tuning. It may not reflect the absolute latest TypeScript beta features.