IDC_Global_Merged

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)

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)

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.

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📊 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>

{
  "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>

{
  "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
  }
}