README.md

---
library_name: peft
base_model: unsloth/Qwen2-1.5B-Instruct-bnb-4bit
tags:
- sft
- lora
- qwen2
- adaptive-learning
- multi-level
- cmrl
license: apache-2.0
---

# CMRL Adaptive Generator

Multi-level adaptive generator trained with SFT for the C-MRL project.

## Model Description

Adapts explanations to different difficulty levels:
- **Novice**: 6th grade level, simple analogies
- **Intermediate**: College student level
- **Expert**: Technical/professional explanations

## Training Details

| Parameter | Value |
|-----------|-------|
| Base Model | unsloth/Qwen2-1.5B-Instruct-bnb-4bit |
| LoRA Rank | 16 |
| LoRA Alpha | 32 |
| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Epochs | 3 |
| Learning Rate | 0.0001 |
| Final Train Loss | 1.5193 |
| Final Eval Loss | 0.0000 |

## Team

**Team kats** - IIIT Hyderabad

## Usage

```python
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    "Ishaank18/cmrl-adaptive-generator",
    max_seq_length=2048,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

messages = [
    {"role": "system", "content": "You are an adaptive tutor."},
    {"role": "user", "content": "Explain photosynthesis to a 6th grader."}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```