hiyouga/Llama-2-70b-AQLM-2Bit-QLoRA-function-calling

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

This model is fine-tuned from 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.

It requires at least 20GB GRAM at inference.

Training and evaluation data

This model is fine-tuned using 2,000 examples of the Alpaca-GPT4 and Glaive-function-calling-v2 datasets.

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": "system",
      "content": (
        "You have access to the following tools:\n"
        "> Tool Name: get_current_weather\nTool Description: Get the current weather in a given location\n"
        "Tool Args:\n"
        "  - location (string, required): The city and state, e.g. San Francisco, CA\n"
        "  - unit (string): should be one of [\"celsius\", \"fahrenheit\"]\n\n"
        "Use the following format if using a tool:\n"
        "```\n"
        "Action: tool name (one of [get_current_weather]).\n"
        "Action Input: the input to the tool, in a JSON format representing the kwargs "
        "(e.g. ```{\"input\": \"hello world\", \"num_beams\": 5}```).\n"
        "```\n"
      )
    },
    {"role": "user", "content": "What is the weather like in Boston?"}
]
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

Benchmark results

MMLU Benchmark	Bits	Metric	Accurary
Average	2	5-shot, top-1	62.38
STEM	2	5-shot, top-1	51.57
Social Sciences	2	5-shot, top-1	73.44
Humanities	2	5-shot, top-1	57.82
Other	2	5-shot, top-1	68.56

Framework versions

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