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Model: Mxode/NanoLM-0.3B-Instruct-v2 Source: Original Platform
2026-05-29 21:20:27 +08:00
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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.
+"""
+```