NanoLM-1B-Instruct-v2

English | 简体中文

Introduction

In order to explore the potential of small models, I have attempted to build a series of them, which are available in the NanoLM Collections.

This is NanoLM-1B-Instruct-v2, fine-tuned on over 4 million high-quality instruction data points.

The model currently supports English only.

Model Details

Nano LMs	Non-emb Params	Arch	Layers	Dim	Heads	Seq Len
25M	15M	MistralForCausalLM	12	312	12	2K
70M	42M	LlamaForCausalLM	12	576	9	2K
0.3B	180M	Qwen2ForCausalLM	12	896	14	4K
1B	840M	Qwen2ForCausalLM	18	1536	12	4K

Metrics

	NanoLM-1B-Instruct-v2	Tinyllama-1.1B	Gemma-2B	Qwen1.5-1.8B	Qwen2-1.5B	Qwen1.5-4B	Mistral-7B-v0.1	Mistral-7B-v0.3	Qwen1.5-7B
GSM8K	44.1	2.3	17.7	33.6	55.8	52.2	37.83	34.5	53.5
MATH	14.8	0.7	11.8	10.1	21.7	10.0	8.48	-	20.3
BBH	0.42	0.30	0.35	0.35	0.36	0.41	0.44	0.45	0.46

How to use

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = 'Mxode/NanoLM-1B-Instruct-v2'

model = AutoModelForCausalLM.from_pretrained(model_path).to('cuda:0', torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_path)


def get_response(prompt: str, **kwargs):
    generation_args = dict(
        max_new_tokens = kwargs.pop("max_new_tokens", 512),
        do_sample = kwargs.pop("do_sample", True),
        temperature = kwargs.pop("temperature", 0.7),
        top_p = kwargs.pop("top_p", 0.8),
        top_k = kwargs.pop("top_k", 40),
        **kwargs
    )

    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": prompt}
    ]
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

    generated_ids = model.generate(model_inputs.input_ids, **generation_args)
    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]

    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return response


prompt = "Calculate (99 - 1) * (3 + 4)"
print(get_response(prompt, do_sample=False))

"""
To calculate \((99 - 1) * (3 + 4)\), follow the order of operations, also known as PEMDAS (Parentheses, Exponents, Multiplication and Division, and Addition and Subtraction).

First, solve the expressions inside the parentheses:

1. \(99 - 1 = 98\)
2. \(3 + 4 = 7\)

Now, multiply the results:

\(98 * 7 = 686\)

So, \((99 - 1) * (3 + 4) = 686\).
"""