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

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)
+```