91 lines
2.8 KiB
Markdown
91 lines
2.8 KiB
Markdown
---
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license: apache-2.0
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base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
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tags:
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- text-generation-inference
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- transformers
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- lora
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- trl
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- smollm2
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- aptitude
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datasets:
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- Prathamesh25/aptitude-qa-dataset
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language:
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- en
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pipeline_tag: text-generation
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---
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# SmolLM2 1.7B Aptitude QA
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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.
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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.
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## Model Description
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- **Developed by:** Prathamesh25
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- **Base Model:** [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co)
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- **Dataset Used:** [Prathamesh25/aptitude-qa-dataset](https://huggingface.co)
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- **Language:** English
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- **License:** Apache 2.0
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## Training Information & Hyperparameters
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- **Precision:** Pure Float16 (`fp16=True`)
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- **Optimizer:** `adamw_torch`
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- **Learning Rate:** 2e-4
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- **Per Device Train Batch Size:** 4
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- **Gradient Accumulation Steps:** 4
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- **LoRA Configuration:** Rank ($r$)=16, Alpha ($lpha$)=32, Dropout=0.05
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- **Target Modules:** `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
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### Loss Progression Metrics
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The training process showed highly stable optimization across epochs:
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- **Step 10:** 1.955
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- **Step 30:** 0.709
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- **Step 100:** 0.412
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- **Step 200:** 0.323
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- **Step 330:** 0.281
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## How to Use
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "Prathamesh25/smollm2-1.7b-aptitude-qa-v1"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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test_prompt = [
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{
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"role": "user",
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"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);`?"
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}
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]
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text_prompt = tokenizer.apply_chat_template(test_prompt, tokenize=False, add_generation_prompt=True)
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model_inputs = tokenizer([text_prompt], return_tensors="pt").to("cuda")
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input_token_len = model_inputs.input_ids.shape[1]
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with torch.no_grad():
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generated_ids = model.generate(
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input_ids=model_inputs.input_ids,
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attention_mask=model_inputs.attention_mask,
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max_new_tokens=150,
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temperature=0.1,
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do_sample=True,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.eos_token_id
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)
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final_tokens = generated_ids[0][input_token_len:]
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print(tokenizer.decode(final_tokens, skip_special_tokens=True))
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```
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---
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