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---
license: gpl-3.0
language:
- en
datasets:
- Mxode/Magpie-Pro-10K-GPT4o-mini
pipeline_tag: text2text-generation
tags:
- chemistry
- biology
- finance
- legal
- music
- code
- climate
- medical
- text-generation-inference
---
# NanoLM-0.3B-Instruct-v2
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-0.3B-Instruct-v2. 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-0.3B-Instruct-v1.1 are the same as [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B), but the number of layers has been reduced from 24 to 12.
As a result, NanoLM-0.3B-Instruct-v1.1 has only 0.3 billion parameters, with approximately **180 million non-embedding parameters**.
Despite this, NanoLM-0.3B-Instruct-v1.1 still demonstrates strong instruction-following capabilities.
## How to use
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = 'Mxode/NanoLM-0.3B-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
prompt1 = "Calculate (4 - 1) * 7"
print(get_response(prompt1, do_sample=False))
"""
To calculate the expression (4 - 1) * 7, we need to follow the order of operations (PEMDAS):
1. Evaluate the expression inside the parentheses: 4 - 1 = 3
2. Multiply 3 by 7: 3 * 7 = 21
So, (4 - 1) * 7 = 21.
"""
``` |