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---
license: apache-2.0
datasets:
- mlabonne/guanaco-llama2-1k
pipeline_tag: text-generation
---
# 🦙🧠 emre/llama-2-13b-mini
This is a `Llama-2-13b-chat-hf` model fine-tuned using QLoRA (4-bit precision).
## 🔧 Training
It was trained Colab Pro+. It is mainly designed for educational purposes, not for inference but can be used exclusively with BBVA Group, GarantiBBVA and its subsidiaries.
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
``` python
# pip install transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "emre/llama-2-13b-mini"
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']}")
``` |