A merged fine-tune of Llama-3.2-1B trained with QLoRA (4-bit) on the SAMSum dialogue summarization dataset. This is the full merged model (base weights + LoRA adapter), so it can be loaded and used directly without peft.

Model Details

Base model: meta-llama/Llama-3.2-1B
Fine-tuning method: QLoRA (4-bit NF4 quantized training, adapter merged into base weights post-training)
Task: Dialogue-to-summary generation
Dataset: SAMSum (~14K dialogue/summary pairs)

Best hyperparameters (selected via systematic HPT):

Hyperparameter	Value
LoRA rank (r)	16
LoRA alpha	32
Learning rate	2e-4
Target modules	q_proj, k_proj, v_proj, o_proj
Training steps	300 (~1 epoch)
Batch size	4 (grad accumulation = 4)

Results

Model	ROUGE-1	ROUGE-2	ROUGE-L
Llama-3.2-1B (base)	34.18%	11.52%	27.24%
This model	47.41%	24.06%	39.55%

How to Use

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "MohamedQiqa/Llama-3.2-1B-QLoRA-Summarizer"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

dialogue = """
Amanda: I baked cookies. Do you want some?
Jerry: Sure! Bring them over.
Amanda: I'll be there in 10 minutes.
"""

messages = [
    {"role": "system", "content": "Summarize the following dialogue in one or two sentences."},
    {"role": "user", "content": dialogue},
]

inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

output = model.generate(inputs, max_new_tokens=64, do_sample=False)
summary = tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True)
print(summary)

Quantized inference (optional, lower memory)

python

from transformers import BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto",
)

Intended Use

Short dialogue/conversation summarization (chat logs, meeting notes, messenger exchanges)
English text only
Best suited for short-to-medium length dialogues similar in style to SAMSum

Limitations

Trained on a single epoch over SAMSum; may not generalize well to very long or technical documents
Inherits any biases present in the base Llama-3.2-1B model and the SAMSum dataset
Not instruction-tuned for tasks beyond summarization

Training Details

Fine-tuned using QLoRA (4-bit quantization + LoRA adapters) on a single GPU, with hyperparameters selected through a one-at-a-time sensitivity sweep over LoRA rank, learning rate, and target modules, tracked in Weights & Biases.

We optimized 1B model now outperforms the base GPT-4o-mini | Model | Type | ROUGE-1 | ROUGE-2 | ROUGE-L | | --- | --- | --- | --- | --- | | Llama 3.2 1B (base) | Open-weight baseline | 34.18% | 11.52% | 27.24% | | GPT-4o-mini (base) | Frontier baseline | 40.76% | 16.15% | 32.91% | | Llama 3.2 1B (Lesson 4) | First fine-tune (r8, q+v) | 46.74% | 23.17% | 38.88% | | Llama 3.2 1B (optimized) | Best HPT config 🏆 | 47.41% | 24.06% | 39.55% | | GPT-4o-mini (fine-tuned) | Managed fine-tuned | 53.68% | 30.73% | 45.96% |

Experiment 1: How Much Capacity Do Adapters Need?

Experiment 2: How Fast Should the Model Learn?

Experiment 3: Which Model Components Should We Train?

Target Modules	ROUGE-1	ROUGE-2	ROUGE-L	Δ vs Baseline
q + v	46.74%	23.17%	38.88%	baseline
Full attention	47.41%	24.06%	39.55%	+0.67% ✅🏆
Attention + MLP	47.62%	23.73%	39.23%	+0.35%

The Winning Configuration

Putting it all together, the best setup for Llama 3.2 1B on SAMSum is:

Hyperparameters:

LoRA rank: 16
LoRA alpha: 32
earning rate: 2e-4
Target modules: q, k, v, o
Training steps: 300 (~1 epoch)
Batch size: 4 (grad accumulation = 4)