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cmrl-adaptive-generator
Ishaank18/cmrl-adaptive-generator
cmrl-adaptive-generator is a machine learning model from Ishaank18. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
Multi-level adaptive generator trained with SFT for the C-MRL project.
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At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Feb 1, 2026
- Updated
- Feb 2, 2026
- SHA
- 14fc59fd
Base models
Model card
From the Hugging Face model README
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
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))
- Library
- peft
- License
- apache-2.0
- Created
- Feb 1, 2026
- Updated
- Feb 2, 2026