Update README.md

README.md

@@ -1,142 +1,241 @@
 ---
+
 library_name: transformers
+
 tags:
+
 - phi-2
+
 - code-generation
-- math
-- reasoning
-- gsm8k
+
 - mbpp
+
 - finetuned
+
 datasets:
+
 - google-research-datasets/mbpp
-- gsm8k
-- meta-math/MATH
+
 language:
+
 - en
+
 base_model:
+
 - microsoft/phi-2
+
 pipeline_tag: text-generation
+
 ---
 
-# Model Card for DeryFerd/Qwen-Math-Code-Distill-Phi-2
+
+
+# Model Card for DeryFerd/phi2-finetuned-mbpp-clean
+
+
 
 ## Model Details
 
+
+
 ### Model Description
 
-**UPDATE:** This model is a fine-tuned, versatile version of **`microsoft/phi-2`**, adapted for both **Python code generation** and **step-by-step mathematical reasoning**. The goal of this project was to distill the capabilities of larger "teacher" models (`Qwen2.5-Coder-7B-Instruct` for coding and `Qwen2.5-Math-7B-Instruct` for math) into the compact and efficient Phi-2 architecture.
 
-The model was trained on a combined dataset of Python programming problems (from MBPP) and grade-school math word problems (from GSM8K and MATH). It is designed to generate not just answers, but also the thought process behind them, mimicking the style of its teachers.
+
+This model is a fine-tuned version of **`microsoft/phi-2`**, specifically adapted for Python code generation tasks. It was trained on a high-quality, curated subset of the **MBPP (Mostly Basic Python Programming)** dataset.
+
+
+
+The primary goal of this project was to distill the coding style and capabilities of a larger "teacher" model (`Qwen/Qwen2.5-Coder-7B-Instruct`) into the much more compact and efficient Phi-2 architecture. The model is designed to generate Python functions based on natural language instructions, often including explanations and test cases in its output.
+
+
 
 - **Developed by:** DeryFerd
+
 - **Model type:** Causal Language Model
+
 - **Language(s) (NLP):** English
+
 - **License:** MIT
+
 - **Finetuned from model:** `microsoft/phi-2`
 
+
+
 ### Model Sources
 
-- **Repository:** [https://huggingface.co/DeryFerd/Qwen-Math-Code-Distill-Phi-2](https://huggingface.co/DeryFerd/Qwen-Math-Code-Distill-Phi-2)
+
+
+- **Repository:** [https://huggingface.co/DeryFerd/phi2-finetuned-mbpp-clean](https://huggingface.co/DeryFerd/phi2-finetuned-mbpp-clean)
+
+
 
 ## Uses
 
+
+
 ### Direct Use
 
-This model is intended for direct use in generating Python functions from natural language and solving math word problems with step-by-step explanations. It can be used as a coding/math assistant, for educational purposes, or for rapid prototyping.
 
-**Intended Use:**
-* Generating Python functions from docstrings or natural language instructions.
-* Solving math problems while showing the reasoning process.
+
+This model is intended for direct use in generating Python code snippets, particularly for creating standalone functions based on a descriptive prompt. It can be used for educational purposes, as a coding assistant, or for rapid prototyping.
+
+
+
+**Intended Use:** Generating Python functions from docstrings or natural language instructions.
+
+
 
 ### Out-of-Scope Use
 
-This is a specialized model. It will not perform well on tasks outside of basic Python code and grade-school level math, such as general conversation, translation, or creative writing. It has not been trained or evaluated for safety and may produce incorrect or insecure code, as well as flawed mathematical reasoning.
+
+
+This is a specialized model. It will not perform well on tasks outside of Python code generation, such as general conversation, translation, or creative writing. It has not been trained or evaluated for safety and may produce incorrect or insecure code.
+
+
 
 ## Bias, Risks, and Limitations
 
-This model was trained on the MBPP, GSM8K, and MATH datasets. Its capabilities are limited to these domains. The model may generate code that is syntactically correct but logically flawed, or math solutions that seem logical but contain calculation errors. **Always review and test the generated output before use in production environments.**
+
+
+This model was trained on the MBPP dataset, which consists of basic programming problems. Its capabilities are limited to this domain. The model may generate code that is syntactically correct but logically flawed. **Always review and test the generated code before use in production environments.**
+
+
 
 A notable limitation discovered during development is a potential **low-level GPU memory conflict**. When this model is loaded into the same runtime as a significantly larger and architecturally different model (like Qwen 7B), its fine-tuned capabilities can be silently overridden, causing it to revert to the base model's behavior. It is recommended to run this model in an isolated process.
 
+
+
 ## How to Get Started with the Model
 
+
+
 Use the code below to get started with the model using the `transformers` library.
 
+
+
 ```python
+
 from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 
-model_id = "DeryFerd/Qwen-Math-Code-Distill-Phi-2"
+
+
+model_id = "DeryFerd/phi2-finetuned-mbpp-clean"
+
+
 
 # Load the tokenizer and model
+
 tokenizer = AutoTokenizer.from_pretrained(model_id)
+
 model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    torch_dtype="auto",
-    device_map="auto",
-    trust_remote_code=True
+
+    model_id,
+
+    torch_dtype="auto",
+
+    device_map="auto",
+
+    trust_remote_code=True
+
 )
 
+
+
 # Create a text-generation pipeline
+
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
-# --- Example 1: Coding ---
-code_instruction = "Write a Python function that takes a list of strings and returns a new list with all strings converted to uppercase."
-prompt = f"Instruct: {code_instruction.strip()}\nOutput:"
 
-outputs = pipe(
-    prompt,
-    max_new_tokens=256,
-    do_sample=False,
-    pad_token_id=tokenizer.eos_token_id
-)
-response = outputs[0]['generated_text'].split("Output:")[1].strip()
-print("--- Coding Example ---")
-print(response)
 
-# --- Example 2: Math ---
-math_instruction = "A bakery has 150 cookies. They sell 60 in the morning and 35 in the afternoon. How many cookies are left at the end of the day?"
-prompt = f"Instruct: {math_instruction.strip()}\nOutput:"
+# Define your instruction
+
+instruction = "Write a Python function that takes a list of strings and returns a new list with all strings converted to uppercase."
+
+prompt = f"Instruct: {instruction.strip()}\nOutput:"
+
+
+
+# Generate the response
 
 outputs = pipe(
-    prompt,
-    max_new_tokens=512,
-    do_sample=False,
-    pad_token_id=tokenizer.eos_token_id
+
+    prompt,
+
+    max_new_tokens=256,
+
+    do_sample=False,
+
+    pad_token_id=tokenizer.eos_token_id
+
 )
+
+
+
 response = outputs[0]['generated_text'].split("Output:")[1].strip()
-print("\n--- Math Example ---")
+
 print(response)
 
+```
+
+
+
 ## Training Details
 
+
+
 ### Training Data
 
-The model was fine-tuned on a combined dataset of **3,474 instruction-response pairs**:
-- **2,500 math problems:** A mix of 2,000 samples from the GSM8K dataset and 500 samples from the MATH dataset. Generated using `Qwen2.5-Math-7B-Instruct`.
-- **974 coding problems:** A curated subset of the MBPP dataset. Generated using `Qwen2.5-Coder-7B-Instruct`.
+
+
+The model was fine-tuned on `mbpp_974_final.jsonl`, a curated dataset containing 974 high-quality instruction-response pairs for Python programming problems, derived from the MBPP dataset. The data was generated using `Qwen/Qwen2.5-Coder-7B-Instruct`.
+
+
 
 ### Training Procedure
 
+
+
 The model was fine-tuned using the LoRA (Low-Rank Adaptation) method for parameter-efficient fine-tuning (PEFT).
 
+
+
 #### Training Hyperparameters
 
+
+
 - **Framework:** `trl.SFTTrainer`
+
 - **LoRA `r`:** 16
+
 - **LoRA `alpha`:** 32
+
 - **Target Modules:** `q_proj`, `k_proj`, `v_proj`, `dense`
+
 - **Learning Rate:** 2e-4
-- **LR Scheduler:** Constant
+
+- **LR Scheduler:** Cosine
+
 - **Epochs:** 3
+
 - **Batch Size:** 1 (with gradient accumulation of 8)
+
 - **Optimizer:** Paged AdamW 8-bit
 
+
+
 ### Compute Infrastructure
 
+
+
 - **Hardware Type:** Single NVIDIA T4 GPU
+
 - **Cloud Provider:** Kaggle Notebooks
 
+
+
 ## Citation
 
-If you use this model, please consider citing the original Phi-2, MBPP, GSM8K, and MATH papers.
+
+
+If you use this model, please consider citing the original Phi-2 and MBPP papers.