初始化项目，由ModelHub XC社区提供模型
Model: Mxode/NanoLM-70M-Instruct-v1 Source: Original Platform
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74
README.md
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README.md
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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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pipeline_tag: text2text-generation
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---
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# NanoLM-70M-Instruct-v1
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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-70M-Instruct-v1. 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-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.
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Essentially, it is a pure LLaMA architecture, specifically LlamaForCausalLM.
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As a result, NanoLM-70M-Instruct-v1 has only 70 million parameters.
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Despite this, NanoLM-70M-Instruct-v1 still demonstrates 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-70M-Instruct-v1'
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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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text = "Why is it important for entrepreneurs to prioritize financial management?"
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prompt = tokenizer.apply_chat_template(
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[
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{'role': 'system', 'content': 'You are a helpful assistant.'},
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{'role': 'user', 'content': text}
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],
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add_generation_prompt=True,
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tokenize=True,
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return_tensors='pt'
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).to('cuda:0')
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outputs = model.generate(
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prompt,
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max_new_tokens=1024,
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do_sample=True,
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temperature=0.7,
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repetition_penalty=1.1,
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eos_token_id=tokenizer.eos_token_id,
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)
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response = tokenizer.decode(outputs[0])
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print(response)
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```
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README_zh-CN.md
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README_zh-CN.md
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# NanoLM-70M-Instruct-v1
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[English](README.md) | 简体中文
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## Introduction
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为了探究小模型的潜能，我尝试构建一系列小模型，并存放于 [NanoLM Collections](https://huggingface.co/collections/Mxode/nanolm-66d6d75b4a69536bca2705b2)。
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这是 NanoLM-70M-Instruct-v1。该模型目前仅支持**英文**。
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## 模型详情
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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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NanoLM-70M-Instruct-v1 的分词器和模型架构与 [SmolLM-135M](https://huggingface.co/HuggingFaceTB/SmolLM-135M) 相同，但层数从30减少到12。
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本质上是纯粹的 LLaMA 架构，即 LlamaForCausalLM。
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因此，NanoLM-70M-Instruct-v1 的参数量只有 70 M。
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尽管如此，NanoLM-70M-Instruct-v1 仍展示了指令跟随能力。
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## 如何使用
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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-70M-Instruct-v1'
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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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text = "Why is it important for entrepreneurs to prioritize financial management?"
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prompt = tokenizer.apply_chat_template(
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[
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{'role': 'system', 'content': 'You are a helpful assistant.'},
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{'role': 'user', 'content': text}
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],
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add_generation_prompt=True,
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tokenize=True,
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return_tensors='pt'
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).to('cuda:0')
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outputs = model.generate(
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prompt,
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max_new_tokens=1024,
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do_sample=True,
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temperature=0.7,
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repetition_penalty=1.1,
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eos_token_id=tokenizer.eos_token_id,
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)
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response = tokenizer.decode(outputs[0])
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print(response)
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```
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config.json
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{
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"_name_or_path": "Mxode/NanoLM-70M-Instruct-v1",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"eos_token_id": 0,
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"hidden_act": "silu",
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"hidden_size": 576,
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"max_position_embeddings": 2048,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 9,
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"num_hidden_layers": 12,
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"num_key_value_heads": 3,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.42.0",
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"use_cache": false,
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"vocab_size": 49152
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}
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{
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"temperature": 0.3,
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"top_k": 20,
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"top_p": 0.7,
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"transformers_version": "4.42.0"
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}
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oid sha256:1fdb5f7e7783d526107ed1092bfa4c278566f7410b00c97811bccdfe80ee3cce
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size 141598784
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special_tokens_map.json
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"bos_token": "<|im_start|>",
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"chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
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"unk_token": "<|endoftext|>",
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"vocab_size": 49152
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}
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vocab.json
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Reference in New Issue
Block a user