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---
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)
``` |