---
license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
tags:
- text-generation-inference
- transformers
- lora
- trl
- smollm2
- aptitude
datasets:
- Prathamesh25/aptitude-qa-dataset
language:
- en
pipeline_tag: text-generation
---

# SmolLM2 1.7B Aptitude QA

This model is a fine-tuned version of `HuggingFaceTB/SmolLM2-1.7B-Instruct` optimized for technical aptitude question-answering tasks, including basic programming concepts (C, Python, loops) and fundamental AI/ML reasoning logic.

It was trained using LoRA parameters in pure Float16 precision on a free Google Colab T4 GPU node to handle the target dataset schema formatting seamlessly.

## Model Description

- **Developed by:** Prathamesh25
- **Base Model:** [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co)
- **Dataset Used:** [Prathamesh25/aptitude-qa-dataset](https://huggingface.co)
- **Language:** English
- **License:** Apache 2.0

## Training Information & Hyperparameters

- **Precision:** Pure Float16 (`fp16=True`)
- **Optimizer:** `adamw_torch`
- **Learning Rate:** 2e-4
- **Per Device Train Batch Size:** 4
- **Gradient Accumulation Steps:** 4
- **LoRA Configuration:** Rank ($r$)=16, Alpha ($lpha$)=32, Dropout=0.05
- **Target Modules:** `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`

### Loss Progression Metrics
The training process showed highly stable optimization across epochs:
- **Step 10:** 1.955
- **Step 30:** 0.709
- **Step 100:** 0.412
- **Step 200:** 0.323
- **Step 330:** 0.281

## How to Use

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Prathamesh25/smollm2-1.7b-aptitude-qa-v1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
    device_map="auto"
)

test_prompt = [
    {
        "role": "user", 
        "content": "subject: 06 technical aptitude (basic programming and aiml concepts)\nlevel: 01 basic\ncategory: loops\nquestion: what is the output of this c code: `int sum = 0; for(int i=1; i<=3; i++) { sum += i; } printf(\"%d\", sum);`?"
    }
]

text_prompt = tokenizer.apply_chat_template(test_prompt, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text_prompt], return_tensors="pt").to("cuda")
input_token_len = model_inputs.input_ids.shape[1]

with torch.no_grad():
    generated_ids = model.generate(
        input_ids=model_inputs.input_ids,
        attention_mask=model_inputs.attention_mask,
        max_new_tokens=150, 
        temperature=0.1, 
        do_sample=True,        
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.eos_token_id
    )

final_tokens = generated_ids[0][input_token_len:]
print(tokenizer.decode(final_tokens, skip_special_tokens=True))
```
---