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Model: Prathamesh25/smollm2-1.7b-aptitude-qa-v1
Source: Original Platform

README.md

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+---
+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))
+```
+---