---
license: gpl-3.0
language:
- en
datasets:
- Mxode/Magpie-Pro-10K-GPT4o-mini
pipeline_tag: text2text-generation
tags:
- chemistry
- biology
- finance
- legal
- music
- art
- code
- climate
- medical
- text-generation-inference
---
# NanoLM-1B-Instruct-v1.1


English | [简体中文](README_zh-CN.md)


## 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](https://huggingface.co/collections/Mxode/nanolm-66d6d75b4a69536bca2705b2).

This is NanoLM-1B-Instruct-v1.1. 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** |



## How to use

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = 'Mxode/NanoLM-1B-Instruct-v1.1'

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 (4 - 1)^(9 - 5)"
print(get_response(prompt, do_sample=False))

"""
The expression (4 - 1)^(9 - 5) can be simplified as follows:

(4 - 1) = 3

So the expression becomes 3^(9 - 5)

3^(9 - 5) = 3^4

3^4 = 81

Therefore, (4 - 1)^(9 - 5) = 81.
"""
```