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research-slm-360m-lora
kushalicious/research-slm-360m-lora
research-slm-360m-lora is a text generation model from kushalicious. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as apache-2.0.
LoRA adapter fine-tuned on SmolLM2-360M-Instruct for structured research skills (JSON outputs).
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At a glance
- Task
- Text Generation
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Jun 10, 2026
- Updated
- Jun 10, 2026
- SHA
- 46788d9a
Try a prompt
Base models
Model card
From the Hugging Face model README
ResearchSLM-360M LoRA
LoRA adapter fine-tuned on SmolLM2-360M-Instruct for structured research skills (JSON outputs).
Training
| Parameter | Value |
|---|---|
| Base model | SmolLM2-360M-Instruct |
| Method | LoRA (r=16, alpha=32) via Unsloth |
| Data | research-slm-dataset — 15k train / 500 eval |
| Hardware | Google Colab free T4 |
| Steps | 250 (3k examples subsampled) |
Evaluation (rule-based, 30 examples)
| Model | Overall |
|---|---|
| Base | 66.1% |
| This adapter | 67.8% |
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
base = "HuggingFaceTB/SmolLM2-360M-Instruct"
adapter = "kushalicious/research-slm-360m-lora"
tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.float16, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)
Or use the full research loop from the GitHub repo:
huggingface-cli download kushalicious/research-slm-360m-lora --local-dir lora_adapter
python -m runtime.main "Your research question" --adapter lora_adapter
GitHub
Full code, eval scripts, and Colab notebook: github.com/kushalicious/research-slm
- Task
- Text Generation
- Library
- peft
- License
- apache-2.0
- Created
- Jun 10, 2026
- Updated
- Jun 10, 2026