---
library_name: transformers
tags:
  - llama
  - lora-merged
  - math-tutor
license: llama3.1
language:
  - en
base_model:
  - Sashank-810/LFT_Final_FineTuned_Increased_Metrics
---

# LFT + IDC Math Tutor (LoRA-merged)

Summary: A math-tutor student model with an integrated IDC critic adapter merged into the base Llama-3.1-8B-Instruct (LoRA weights merged into base). Intended for math tutoring and doubt clarification.

## Model Details

- Base: meta-llama/Llama-3.1-8B-Instruct
- Finetuned for: math tutoring + IDC-style critique/fix
- Precision: FP16/BF16 compatible
- Hardware: Single-GPU inference recommended

## Intended Use

- Educational tutoring, step-by-step math help, critique-and-fix of student answers.

## Out-of-Scope

- Safety-sensitive, legal, medical, or any harmful/abusive use.

## How to Use (Transformers)

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

name = "Sashank-810/IDC_Global_Merged"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForCausalLM.from_pretrained(name, torch_dtype="auto", device_map="auto")
prompt = "Explain the derivative of sin(x)."
out = model.generate(--tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=128)
print(tok.decode(out[0], skip_special_tokens=True))
```

## How to Use (vLLM)

```bash
python -m vllm.entrypoints.api_server \
  --model Sashank-810/IDC_Global_Merged \
  --dtype auto \
  --tensor-parallel-size 1
```

## License & Responsible Use

- Use responsibly for education; avoid harmful or malicious outputs.

---

# 📊 Evaluation Results (Llama 3.1-8B-Instruct Base vs Fine‑Tuned)

## ✅ Structured Evaluation Summary

--Total Questions:-- 2617

### Base Model Performance

- --Correct:-- 625
- --Accuracy:-- 23.88%

### Fine‑Tuned Model Performance

- --Correct:-- 916
- --Accuracy:-- 35.00%

### 🎯 Improvement

- --Accuracy Gain:-- +11.12 percentage points
- --Improved Answers:-- 483
- --Regressed Answers:-- 192

---

# 📝 Text Generation Metrics

## Base Model

--BLEU:-- 38.24
--ROUGE-1:-- 0.2947
--ROUGE-2:-- 0.0934
--ROUGE-L:-- 0.2936
--METEOR:-- 0.1633

<details>
<summary>Full Base Model Metrics</summary>

```json
{
  "bleu": {
    "score": 38.24172039700722,
    "counts": [2214, 1378, 1110, 875],
    "totals": [3765, 2033, 1740, 1462],
    "precisions": [58.80, 67.78, 63.79, 59.85],
    "bp": 0.612276654279684,
    "sys_len": 3765,
    "ref_len": 5612
  },
  "rouge": {
    "rouge1": 0.29469964396406867,
    "rouge2": 0.09342261992242887,
    "rougeL": 0.2935582970928785,
    "rougeLsum": 0.2940696059343364
  },
  "meteor": {
    "meteor": 0.16327044830765994
  }
}
```

</details>

---

## Fine‑Tuned Model

--BLEU:-- 59.31
--ROUGE-1:-- 0.4423
--ROUGE-2:-- 0.1247
--ROUGE-L:-- 0.4424
--METEOR:-- 0.2478

<details>
<summary>Full Fine‑Tuned Metrics</summary>

```json
{
  "bleu": {
    "score": 59.31334282676538,
    "counts": [3324, 2048, 1600, 1201],
    "totals": [5734, 3124, 2659, 2219],
    "precisions": [57.97, 65.55, 60.17, 54.12],
    "bp": 1.0,
    "sys_len": 5734,
    "ref_len": 5612
  },
  "rouge": {
    "rouge1": 0.4423208144549374,
    "rouge2": 0.1247048391679649,
    "rougeL": 0.4424399985443162,
    "rougeLsum": 0.4414589284956114
  },
  "meteor": {
    "meteor": 0.24778242330127054
  }
}
```