README.md

metadata

license: llama2
library_name: peft
tags:
  - llama-factory
  - lora
  - generated_from_trainer
base_model: BlackSamorez/Llama-2-70b-AQLM-2Bit-1x16-hf
inference: false
model-index:
  - name: llama2_70b_aqlm_toolcall
    results: []
datasets:
  - vicgalle/alpaca-gpt4
  - glaiveai/glaive-function-calling-v2
language:
  - en
pipeline_tag: text-generation

LLaMA-2 70B AQLM 2-bit QLoRA with function calling

This model is a fine-tuned version of BlackSamorez/Llama-2-70b-AQLM-2Bit-1x16-hf using LLaMA Factory.

The maximum GPU usage during training is 24GB, and the model has preliminary conversation and tool-using abilities.

Training and evaluation data

This model is fine-tuned using 1,000 examples of the Alpaca-GPT4 and Glaive-function-calling-v2 datasets, respectively.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from peft import PeftModel

tokenizer = AutoTokenizer.from_pretrained("hiyouga/Llama-2-70b-AQLM-2Bit-QLoRA-function-calling")
model = AutoModelForCausalLM.from_pretrained("BlackSamorez/Llama-2-70b-AQLM-2Bit-1x16-hf", torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, "hiyouga/Llama-2-70b-AQLM-2Bit-QLoRA-function-calling")
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

messages = [
    {"role": "user", "content": "Who are you?"}
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
inputs = inputs.to("cuda")
generate_ids = model.generate(inputs, streamer=streamer)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 1
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• num_epochs: 1.0
• mixed_precision_training: Native AMP

Training results

Framework versions

• PEFT 0.9.0
• Transformers 4.39.0.dev0
• Pytorch 2.2.1+cu121
• Datasets 2.15.0
• Tokenizers 0.15.2