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CRIA v1.3

πŸ’‘ Article | πŸ’» Github | πŸ“” Colab 1,2

What is CRIA?

krΔ“-Ι™ plural crias. : a baby llama, alpaca, vicuΓ±a, or guanaco.

Cria Logo
or what ChatGPT suggests, "Crafting a Rapid prototype of an Intelligent llm App using open source resources".

The initial objective of the CRIA project is to develop a comprehensive end-to-end chatbot system, starting from the instruction-tuning of a large language model and extending to its deployment on the web using frameworks such as Next.js.

Specifically, we have fine-tuned the llama-2-7b-chat-hf model with QLoRA (4-bit precision) using the mlabonne/CodeLlama-2-20k dataset. This fine-tuned model serves as the backbone for the CRIA chat platform.

πŸ“¦ Model Release

CRIA v1.3 comes with several variants.

πŸ”§ Training

It was trained on a Google Colab notebook with a T4 GPU and high RAM.

Training procedure

The following bitsandbytes quantization config was used during training:

  • load_in_8bit: False
  • load_in_4bit: True
  • llm_int8_threshold: 6.0
  • llm_int8_skip_modules: None
  • llm_int8_enable_fp32_cpu_offload: False
  • llm_int8_has_fp16_weight: False
  • bnb_4bit_quant_type: nf4
  • bnb_4bit_use_double_quant: False
  • bnb_4bit_compute_dtype: float16

Framework versions

  • PEFT 0.4.0

πŸ’» 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(

sequences = pipeline(
    f'<s>[INST] {prompt} [/INST]',
for seq in sequences:
    print(f"Result: {seq['generated_text']}")


We'd like to thank:

  • mlabonne for his article and resources on implementation of instruction tuning
  • TheBloke for his script for LLM quantization.
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Dataset used to train davzoku/cria-llama2-7b-v1.3-GGML