cria-llama2-7b-v1.3-q4-mlx

CRIA v1.3

💡 Article | 💻 Github | 📔 Colab 1,2

What is CRIA?

krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.

<p align="center"> <img src="https://raw.githubusercontent.com/davzoku/cria/main/assets/icon-512x512.png" width="300" height="300" alt="Cria Logo"> <br> <i>or what ChatGPT suggests, <b>"Crafting a Rapid prototype of an Intelligent llm App using open source resources"</b>.</i> </p>

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.

📦 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
• davzoku/cria-llama2-7b-v1.3-GGUF: GGUF Format

This model is converted from the q4_0 GGML version of CRIA v1.3 using the llama.cpp's convert-llama-ggml-to-gguf.py script

🔧 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

💻 Usage

This model was converted to MLX format from davzoku/cria-llama2-7b-v1.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("davzoku/cria-llama2-7b-v1.3-q4-mlx")
response = generate(model, tokenizer, prompt="hello", verbose=True)

Original 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:

• mlabonne for his article and resources on implementation of instruction tuning
• TheBloke for his script for LLM quantization.