CRIA v1.3
💡 Article | 💻 Github | 📔 Colab 1,2
What is CRIA?
krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.
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
🔧 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
# 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: