README.md

---
license: gpl-3.0
language:
- en
pipeline_tag: text2text-generation
---
# NanoLM-70M-Instruct-v1


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-70M-Instruct-v1. 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|

The tokenizer and model architecture of NanoLM-70M-Instruct-v1 are the same as [SmolLM-135M](https://huggingface.co/HuggingFaceTB/SmolLM-135M), but the number of layers has been reduced from 30 to 12.

Essentially, it is a pure LLaMA architecture, specifically LlamaForCausalLM.

As a result, NanoLM-70M-Instruct-v1 has only 70 million parameters.

Despite this, NanoLM-70M-Instruct-v1 still demonstrates instruction-following capabilities.



## How to use

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

model_path = 'Mxode/NanoLM-70M-Instruct-v1'

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


text = "Why is it important for entrepreneurs to prioritize financial management?"
prompt = tokenizer.apply_chat_template(
    [
        {'role': 'system', 'content': 'You are a helpful assistant.'},
        {'role': 'user', 'content': text}
    ],
    add_generation_prompt=True,
    tokenize=True,
    return_tensors='pt'
).to('cuda:0')


outputs = model.generate(
    prompt,
    max_new_tokens=1024,
    do_sample=True,
    temperature=0.7,
    repetition_penalty=1.1,
    eos_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(outputs[0])
print(response)
```