mishrabp
/

phi2-custom-response-qlora-adapter

@@ -1,199 +1,161 @@
 ---
 library_name: transformers
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 ---
+license: mit
+tags:
+  - causal-lm
+  - instruction-following
+  - loRA
+  - QLoRA
+  - quantized
+language: en
 library_name: transformers
+base_model: microsoft/phi-2
 ---
+# Phi-2 QLoRA Fine-Tuned Model
+**Model:** `mishrabp/phi2-custom-response-qlora-adapter`
+**Base Model:** [`microsoft/phi-2`](https://huggingface.co/microsoft/phi-2)
+**Fine-Tuning Method:** QLoRA (4-bit quantized LoRA)
+**Task:** Instruction-following / Customer Support Responses
+---
+## Model Description
+This repository contains a **Phi-2 language model fine-tuned using QLoRA** on a synthetic dataset of customer support instructions and responses. The fine-tuning uses **4-bit quantized LoRA adapters** for memory-efficient training and can run on GPU or CPU (slower on CPU).
+The model is designed for **instruction-following tasks** like customer support, FAQs, or other dialog generation tasks.
+---
+## Training Data
+The fine-tuning dataset is synthetic, consisting of 3000 instruction-response pairs:
+**Example:**
+```text
+Instruction: "Customer asks about refund window #1"
+Response: "Our refund window is 30 days from delivery."
+```
+Here is the dataset that was used for fine-tunning:
+https://huggingface.co/datasets/mishrabp/customer-support-responses/resolve/main/train.csv
+You can replace the dataset with your own CSV/JSON file to train on real-world data.
+---
+## Intended Use
+* Generate responses to instructions in customer support scenarios.
+* Small-scale instruction-following experiments.
+* Educational or research purposes.
+---
+## How to Use
+### Load the Fine-Tuned Model
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+# -----------------------------
+# Load fine-tuned model from HF
+# -----------------------------
+model_name = "mishrabp/phi2-custom-response-qlora-adapter"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+base_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2")
+model = PeftModel.from_pretrained(base_model, model_name)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+# -----------------------------
+# Sample evaluation dataset
+# -----------------------------
+eval_data = [
+    {"instruction": "Customer asks about refund window", "reference": "Our refund window is 30 days from delivery."},
+    {"instruction": "Order arrived late", "reference": "Sorry for the delay. A delivery credit has been applied."},
+    {"instruction": "Wrong item received", "reference": "We’ll ship the correct item and provide a return label."},
+]
+# -----------------------------
+# Evaluation loop
+# -----------------------------
+for i, example in enumerate(eval_data, 1):
+    prompt = f"### Instruction:
+{example['instruction']}
+### Response:"
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    output_ids = model.generate(**inputs, max_new_tokens=50)
+    generated = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    print(f"Example {i}")
+    print("Instruction:", example["instruction"])
+    print("Generated Response:", generated.split("### Response:")[-1].strip())
+    print("Reference Response:", example["reference"])
+    print("-" * 50)
+# -----------------------------
+# Optional: compute simple token-level accuracy or BLEU
+# -----------------------------
+from nltk.translate.bleu_score import sentence_bleu
+bleu_scores = []
+for example in eval_data:
+    prompt = f"### Instruction:
+{example['instruction']}
+### Response:"
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    output_ids = model.generate(**inputs, max_new_tokens=50)
+    generated = tokenizer.decode(output_ids[0], skip_special_tokens=True).split("### Response:")[-1].strip()
+    reference_tokens = example["reference"].split()
+    generated_tokens = generated.split()
+    bleu = sentence_bleu([reference_tokens], generated_tokens)
+    bleu_scores.append(bleu)
+print("Average BLEU score:", sum(bleu_scores)/len(bleu_scores))
+```
+---
+## Training Script
+The training script performs the following steps:
+1. Loads the **Phi-2 base model**.
+2. Creates a **synthetic dataset** of instruction-response pairs.
+3. Tokenizes and formats the dataset for causal language modeling.
+4. Applies a **LoRA adapter**.
+5. Trains using **QLoRA** if GPU is available, otherwise full-precision LoRA on CPU.
+6. Saves the adapter and tokenizer to `./phi2-qlora`.
+7. Pushes the adapter and tokenizer to Hugging Face Hub.
+### Requirements
+```bash
+pip install torch transformers peft datasets huggingface_hub python-dotenv
+```
+---
+## Parameters
+* `r=8`, `lora_alpha=16`, `lora_dropout=0.05`
+* `target_modules=["q_proj","v_proj"]` (adjust for different base models)
+* Learning rate: `2e-4`
+* Batch si