Update README.md

README.md

@@ -1,3 +1,82 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Coder-7B-Instruct
+pipeline_tag: text-generation
+library_name: peft
+tags:
+- lora
+- peft
+- qwen2.5
+- miniscript
+- code
+---
+
+# miniscript-code-helper-lora
+
+This repository contains a LoRA adapter for `Qwen/Qwen2.5-Coder-7B-Instruct`, fine-tuned to help answer questions about the MiniScript programming language.
+
+The adapter was trained on a small MiniScript Q&A corpus. On its own, it improves MiniScript awareness somewhat, but best results come when it is used together with a RAG pipeline over MiniScript reference materials.
+
+## Base model
+
+- Qwen/Qwen2.5-Coder-7B-Instruct
+
+## What this repo contains
+
+- PEFT/LoRA adapter weights only
+- Not the full base model
+
+## Intended use
+
+- Answering questions about MiniScript
+- Assisting with MiniScript syntax and examples
+- Best used with retrieval augmentation (RAG)
+
+## Limitations
+
+- The adapter alone is not fully reliable
+- It may still fall back to Python-flavored assumptions from the base model
+- For best accuracy, pair it with a MiniScript documentation retriever
+
+## Example usage
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model_id = "Qwen/Qwen2.5-Coder-7B-Instruct"
+adapter_id = "YOUR_USERNAME/miniscript-code-helper-lora"
+
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+model = PeftModel.from_pretrained(base_model, adapter_id)
+model.eval()
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant specializing in MiniScript programming."},
+    {"role": "user", "content": "How do I iterate over a map in MiniScript?"},
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+output = model.generate(**inputs, max_new_tokens=512)
+response = tokenizer.decode(
+    output[0][len(inputs.input_ids[0]):],
+    skip_special_tokens=True,
+)
+
+print(response)
+```