llama-2-13b-miniguanaco

🦙🧠 Miniguanaco-13b

📝 Article | 💻 Colab | 📄 Script

<center><img src="https://i.imgur.com/1IZmjU4.png" width="300"></center>

This is a Llama-2-13b-chat-hf model fine-tuned using QLoRA (4-bit precision) on the mlabonne/guanaco-llama2-1k dataset, which is a subset of the timdettmers/openassistant-guanaco.

🔧 Training

It was trained on an RTX 3090. It is mainly designed for educational purposes, not for inference. Parameters:

max_seq_length = 2048
use_nested_quant = True
bnb_4bit_compute_dtype=bfloat16
lora_r=8
lora_alpha=16
lora_dropout=0.05
per_device_train_batch_size=2

💻 Usage

# pip install transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/llama-2-13b-miniguanaco"
prompt = "What is a large language model?"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

sequences = pipeline(
    f'<s>[INST] {prompt} [/INST]',
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
    max_length=200,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")