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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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+# NanoLM-0.3B-Instruct-v2
+
+[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-0.3B-Instruct-v2。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|
+
+
+## 如何使用
+
+```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.
+"""
+```