---
language:
- en
license: apache-2.0
tags:
- text-generation
- llama
- qlora
- peft
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
datasets:
- HuggingFaceH4/ultrachat_200k
---

# hoangtung386/TinyLlama-1.1B-qlora

Fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) using QLoRA.

## Model Details

- **Base Model:** TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
- **Method:** QLoRA (Quantized Low-Rank Adaptation)
- **Dataset:** HuggingFaceH4/ultrachat_200k
- **Training Samples:** 5,000

## Training Configuration

### LoRA Config
```yaml
r: 64
lora_alpha: 32
lora_dropout: 0.1
target_modules: {'k_proj', 'gate_proj', 'up_proj', 'down_proj', 'v_proj', 'q_proj', 'o_proj'}
```

### Training Args
```yaml
learning_rate: 0.0002
epochs: 3
batch_size: 2
gradient_accumulation: 4
optimizer: OptimizerNames.PAGED_ADAMW
scheduler: SchedulerType.COSINE
```

## Training Results

| Metric | Value |
|--------|-------|
| Loss | 1.2668 |
| Runtime | 7698.13s |
| Samples/sec | 1.95 |
| Steps | N/A |

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hoangtung386/TinyLlama-1.1B-qlora")
model = AutoModelForCausalLM.from_pretrained("hoangtung386/TinyLlama-1.1B-qlora")

prompt = "<|user|>\nWhat is AI?</s>\n<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Framework Versions
- Transformers: 4.41.2
- PyTorch: 2.5.1+cu124
- PEFT: 0.11.1
- TRL: 0.9.4