ebrahimzaher
/

qwen_adaptive_tutor

@@ -1,208 +1,54 @@
 ---
 base_model: unsloth/Qwen2.5-1.5B-Instruct
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:./qwen_base_model
-- lora
-- transformers
 - unsloth
 ---
-# Model Card for Model ID
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 ## Model Details
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-### Framework versions
-- PEFT 0.19.1

 ---
+license: apache-2.0
 base_model: unsloth/Qwen2.5-1.5B-Instruct
 tags:
 - unsloth
+- lora
+- peft
+- conversational
+- educational
+- coding
 ---
+# Qwen Adaptive Programming Tutor 🧑‍🏫
 ## Model Details
 ### Model Description
+The **Qwen Adaptive Programming Tutor** is a fine-tuned language model designed to act as a Socratic programming instructor. Instead of providing direct solutions or writing code for the user, it analyzes buggy code and provides conceptual, encouraging hints to guide students toward the correct answer. This model was fine-tuned to reduce memory footprint and latency while maintaining high pedagogical quality.
+- **Developed by:** Ebrahim Zaher
+- **Model type:** Causal Language Model (LoRA Adapter)
+- **Language(s) (NLP):** English
+- **License:** Apache-2.0
+- **Finetuned from model:** `unsloth/Qwen2.5-1.5B-Instruct`
 ## Uses
 ### Direct Use
+This model is intended to be used as a backend for educational technology platforms, coding bootcamps, and IDE extensions. It takes student code and an instruction as input and outputs a short, guiding question or hint.
 ### Out-of-Scope Use
+- Generating full, production-ready code blocks (the model is explicitly trained *not* to do this).
+- Replacing standard debugging tools for complex, multi-file enterprise codebases.
 ## How to Get Started with the Model
+You can easily load this model using `transformers` and `peft`.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+# 1. Load the base model
+base_model_name = "unsloth/Qwen2.5-1.5B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(base_model_name)
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+# 2. Load the LoRA adapter
+adapter_repo = "ebrahimzaher/qwen_adaptive_tutor"
+model = PeftModel.from_pretrained(model, adapter_repo)
+# 3. Example usage
+prompt = "<|im_start|>user\nAnalyze this buggy code and provide a Socratic hint:\ndef add(a, b):\n    return a - b<|im_end|>\n<|im_start|>assistant\n"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=128)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))