---
language:
- en
license: apache-2.0
datasets:
- tatsu-lab/alpaca
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---

## lxyuan/llama-3-8b-Instruct-lora-merged

**Model Description**: Finetuned the [Llama-3-8B-Instruct Model](https://huggingface.co./unsloth/llama-3-8b-Instruct-bnb-4bit) using [unsloth](https://github.com/unslothai/unsloth) 
on [Alpaca Dataset](https://huggingface.co./datasets/tatsu-lab/alpaca) for 1000 steps.


- **Developed by:** lxyuan
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
- **Finetuned from model :** tatsu-lab/alpaca

## Installation

```python
import torch

major_version, minor_version = torch.cuda.get_device_capability()

# Must install separately since Colab has torch 2.2.1, which breaks packages
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"

if major_version >= 8:
    # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)
    !pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes
else:
    # Use this for older GPUs (V100, Tesla T4, RTX 20xx)
    !pip install --no-deps xformers trl peft accelerate bitsandbytes
```

## Inference example

```python
from transformers import pipeline
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "lxyuan/llama-3-8b-Instruct-lora-merged",
    dtype = None, # auto detect
    load_in_4bit = True, # default is True
)

FastLanguageModel.for_inference(model) # Enable native 2x faster inference

pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)

messages = [
    {"role": "system", "content": "You are helpful AI bot that follows instruction to complete task."},
    {"role": "user", "content": "Write me 10 sentences that end with 'apple"},
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = pipeline(
  prompt,
  max_new_tokens=512,
  eos_token_id=terminators,
  do_sample=True,
  temperature=0.6,
  top_p=0.9,
)

print(outputs[0]["generated_text"])
```

#### Inference Output
```markdown
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are helpful AI bot that follows instruction to complete task.<|eot_id|><|start_header_id|>user<|end_header_id|>

Write me 10 sentences that end with 'apple<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Here are 10 sentences that end with the word "apple":

1. The farmer grew a juicy red apple.
2. She ate a crunchy green apple.
3. The tree bore a ripe yellow apple.
4. He bit into a sweet Granny Smith apple.
5. The basket was filled with fresh apples.
6. The juice was squeezed from a ripe red apple.
7. She picked a perfect autumn apple.
8. The pie was filled with tender Granny Smith apple.
9. The farmer's market sold a variety of apples.
10. The snack was a crisp, juicy apple.
```

## Training procedure

- [Finetuning notebook](https://github.com/LxYuan0420/nlp/blob/main/notebooks/Lora_finetuning_Llama_3_8b_Instruct_with_Alpaca.ipynb)
- [Original Notebook from unsloth](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing#scrollTo=MKX_XKs_BNZR)