README.md

---
datasets:
- tctsung/chat_restaurant_recommendation
pipeline_tag: text-generation
---
This model is quantized by autoawq package using `tctsung/chat_restaurant_recommendation` as calibration dataset

Reference model: [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)

## Key results:

1. AWQ quantization resulted in a **1.62x improvement** in inference speed, generating **140.47 new tokens per second**.
2. The model size was compressed from 4.4GB to 0.78GB, representing a reduction in memory footprint to only **17.57%** of the original model.
3. I used 6 different LLM tasks to demonstrate that the quantized model maintains similar accuracy, with a maximum accuracy degradation of only ~1%

For more details, see github repo [tctsung/LLM_quantize](https://github.com/tctsung/LLM_quantize.git)

## Inference tutorial

```python
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

# load model & tokenizer:
model_id = "tctsung/TinyLlama-1.1B-chat-v1.0-awq"
model = LLM(model = model_id, dtype='half', 
            quantization='awq', gpu_memory_utilization=0.9)
sampling_params = SamplingParams(temperature=1.0,
                                 max_tokens=1024,
                                 min_p=0.5,
                                 top_p=0.85)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# define your own sys & user msg:
sys_msg = "..."
user_msg = "..."
chat_msg = [
            {"role": "system", "content": sys_msg},
            {"role": "user",  "content": user_msg}
        ]
input_text = tokenizer.apply_chat_template(chat_msg, tokenize=False, add_generation_prompt=False)  
output = model.generate(input_text, sampling_params)
output_text = output[0].outputs[0].text
print(output_text)   # show the model output
```