metadata
inference: false
language: en
license: llama2
model_type: llama
datasets:
- mlabonne/CodeLlama-2-20k
pipeline_tag: text-generation
tags:
- llama-2
CRIA v1.3
💡 Article | 💻 Github | 📔 Colab 1,2
What is CRIA?
krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.
or what ChatGPT suggests, "Crafting a Rapid prototype of an Intelligent llm App using open source resources".
This model is a llama-2-7b-chat-hf model fine-tuned using QLoRA (4-bit precision) on the mlabonne/CodeLlama-2-20k dataset and it is used to power CRIA chat.
📦 Model Release
CRIA v1.3 comes with several variants.
- davzoku/cria-llama2-7b-v1.3: Merged Model
- davzoku/cria-llama2-7b-v1.3-GGML: Quantized Merged Model
- davzoku/cria-llama2-7b-v1.3_peft: PEFT adapter
🔧 Training
It was trained on a Google Colab notebook with a T4 GPU and high RAM.
💻 Usage
# pip install transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "davzoku/cria-llama2-7b-v1.3"
prompt = "What is a cria?"
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']}")
References
We'd like to thank: