103 lines
3.1 KiB
Markdown
103 lines
3.1 KiB
Markdown
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---
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license: gpl-3.0
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language:
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- en
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datasets:
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- Mxode/Magpie-Pro-10K-GPT4o-mini
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pipeline_tag: text2text-generation
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tags:
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- chemistry
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- biology
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- finance
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- legal
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- music
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- code
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- climate
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- medical
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- text-generation-inference
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---
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# NanoLM-0.3B-Instruct-v2
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English | [简体中文](README_zh-CN.md)
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## Introduction
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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).
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This is NanoLM-0.3B-Instruct-v2. The model currently supports **English only**.
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## Model Details
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| Nano LMs | Non-emb Params | Arch | Layers | Dim | Heads | Seq Len |
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| :----------: | :------------------: | :---: | :----: | :-------: | :---: | :---: |
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| 25M | 15M | MistralForCausalLM | 12 | 312 | 12 |2K|
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| 70M | 42M | LlamaForCausalLM | 12 | 576 | 9 |2K|
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| **0.3B** | **180M** | **Qwen2ForCausalLM** | **12** | **896** | **14** | **4K** |
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| 1B | 840M | Qwen2ForCausalLM | 18 | 1536 | 12 |4K|
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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.
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As a result, NanoLM-0.3B-Instruct-v1.1 has only 0.3 billion parameters, with approximately **180 million non-embedding parameters**.
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Despite this, NanoLM-0.3B-Instruct-v1.1 still demonstrates strong instruction-following capabilities.
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## How to use
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_path = 'Mxode/NanoLM-0.3B-Instruct-v2'
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model = AutoModelForCausalLM.from_pretrained(model_path).to('cuda:0', torch.bfloat16)
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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def get_response(prompt: str, **kwargs):
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generation_args = dict(
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max_new_tokens = kwargs.pop("max_new_tokens", 512),
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do_sample = kwargs.pop("do_sample", True),
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temperature = kwargs.pop("temperature", 0.7),
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top_p = kwargs.pop("top_p", 0.8),
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top_k = kwargs.pop("top_k", 40),
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**kwargs
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)
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(model_inputs.input_ids, **generation_args)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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return response
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prompt1 = "Calculate (4 - 1) * 7"
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print(get_response(prompt1, do_sample=False))
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"""
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To calculate the expression (4 - 1) * 7, we need to follow the order of operations (PEMDAS):
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1. Evaluate the expression inside the parentheses: 4 - 1 = 3
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2. Multiply 3 by 7: 3 * 7 = 21
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So, (4 - 1) * 7 = 21.
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"""
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```
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