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Model: Mxode/NanoLM-70M-Instruct-v1
Source: Original Platform

.gitattributes

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README.md

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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)
+```

README_zh-CN.md

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+# NanoLM-70M-Instruct-v1
+
+[English](README.md) | 简体中文
+
+
+## Introduction
+
+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)。
+
+这是 NanoLM-70M-Instruct-v1。该模型目前仅支持**英文**。
+
+
+## 模型详情
+
+| 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|
+
+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。
+
+本质上是纯粹的 LLaMA 架构，即 LlamaForCausalLM。
+
+因此，NanoLM-70M-Instruct-v1 的参数量只有 70 M。
+
+尽管如此，NanoLM-70M-Instruct-v1 仍展示了指令跟随能力。
+
+
+## 如何使用
+
+```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)
+```

config.json

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+{
+  "_name_or_path": "Mxode/NanoLM-70M-Instruct-v1",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "hidden_act": "silu",
+  "hidden_size": 576,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "max_position_embeddings": 2048,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 9,
+  "num_hidden_layers": 12,
+  "num_key_value_heads": 3,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.42.0",
+  "use_cache": false,
+  "vocab_size": 49152
+}

generation_config.json

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+{
+  "do_sample": true,
+  "eos_token_id": 2,
+  "max_new_tokens": 2048,
+  "pad_token_id": 0,
+  "temperature": 0.3,
+  "top_k": 20,
+  "top_p": 0.7,
+  "transformers_version": "4.42.0"
+}

model.safetensors

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+oid sha256:1fdb5f7e7783d526107ed1092bfa4c278566f7410b00c97811bccdfe80ee3cce
+size 141598784

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|endoftext|>",
+    "<|im_start|>",
+    "<|im_end|>",
+    "<repo_name>",
+    "<reponame>",
+    "<file_sep>",
+    "<filename>",
+    "<gh_stars>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<jupyter_script>",
+    "<empty_output>"
+  ],
+  "bos_token": {
+    "content": "<|im_start|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
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+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|im_start|>",
+      "lstrip": false,
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+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<repo_name>",
+      "lstrip": false,
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+      "single_word": false,
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+    },
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+      "special": true
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+      "single_word": false,
+      "special": true
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+      "lstrip": false,
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+      "single_word": false,
+      "special": true
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|endoftext|>",
+    "<|im_start|>",
+    "<|im_end|>",
+    "<repo_name>",
+    "<reponame>",
+    "<file_sep>",
+    "<filename>",
+    "<gh_stars>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<jupyter_script>",
+    "<empty_output>"
+  ],
+  "bos_token": "<|im_start|>",
+  "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 %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "model_max_length": 2048,
+  "pad_token": "<|im_end|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152
+}