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Model: Mxode/NanoLM-0.3B-Instruct-v2 Source: Original Platform
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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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datasets:
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- Mxode/Magpie-Pro-10K-GPT4o-mini
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pipeline_tag: text2text-generation
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tags:
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- chemistry
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- biology
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- finance
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- legal
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- music
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- code
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- climate
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- medical
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- text-generation-inference
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---
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# NanoLM-0.3B-Instruct-v2
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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-0.3B-Instruct-v2. 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-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.
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As a result, NanoLM-0.3B-Instruct-v1.1 has only 0.3 billion parameters, with approximately **180 million non-embedding parameters**.
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Despite this, NanoLM-0.3B-Instruct-v1.1 still demonstrates strong 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-0.3B-Instruct-v2'
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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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def get_response(prompt: str, **kwargs):
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generation_args = dict(
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max_new_tokens = kwargs.pop("max_new_tokens", 512),
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do_sample = kwargs.pop("do_sample", True),
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temperature = kwargs.pop("temperature", 0.7),
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top_p = kwargs.pop("top_p", 0.8),
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top_k = kwargs.pop("top_k", 40),
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**kwargs
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)
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(model_inputs.input_ids, **generation_args)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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return response
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prompt1 = "Calculate (4 - 1) * 7"
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print(get_response(prompt1, do_sample=False))
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"""
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To calculate the expression (4 - 1) * 7, we need to follow the order of operations (PEMDAS):
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1. Evaluate the expression inside the parentheses: 4 - 1 = 3
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2. Multiply 3 by 7: 3 * 7 = 21
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So, (4 - 1) * 7 = 21.
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"""
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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-0.3B-Instruct-v2
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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-0.3B-Instruct-v2。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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## 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-0.3B-Instruct-v2'
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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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def get_response(prompt: str, **kwargs):
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generation_args = dict(
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max_new_tokens = kwargs.pop("max_new_tokens", 512),
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do_sample = kwargs.pop("do_sample", True),
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temperature = kwargs.pop("temperature", 0.7),
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top_p = kwargs.pop("top_p", 0.8),
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top_k = kwargs.pop("top_k", 40),
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**kwargs
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)
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(model_inputs.input_ids, **generation_args)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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return response
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prompt1 = "Calculate (4 - 1) * 7"
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print(get_response(prompt1, do_sample=False))
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"""
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To calculate the expression (4 - 1) * 7, we need to follow the order of operations (PEMDAS):
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1. Evaluate the expression inside the parentheses: 4 - 1 = 3
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2. Multiply 3 by 7: 3 * 7 = 21
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So, (4 - 1) * 7 = 21.
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"""
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```
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added_tokens.json
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{
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"<|endoftext|>": 151643,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644
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}
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config.json
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{
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"_name_or_path": "Mxode/NanoLM-0.3B-Instruct-v2",
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151643,
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"hidden_act": "silu",
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"hidden_size": 896,
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"initializer_range": 0.02,
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"intermediate_size": 4864,
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"max_position_embeddings": 131072,
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"max_window_layers": 12,
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"model_type": "qwen2",
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"num_attention_heads": 14,
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"num_hidden_layers": 12,
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"num_key_value_heads": 2,
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"rms_norm_eps": 1e-06,
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"rope_theta": 1000000.0,
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"sliding_window": 131072,
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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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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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{
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"do_sample": true,
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"eos_token_id": 151645,
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"max_new_tokens": 2048,
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"pad_token_id": 151643,
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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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merges.txt
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 630184520
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>"
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],
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"eos_token": {
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"content": "<|im_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"added_tokens_decoder": {
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"151643": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"151644": {
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"content": "<|im_start|>",
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"lstrip": false,
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"normalized": false,
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},
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"151645": {
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"normalized": false,
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"rstrip": false,
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}
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},
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>"
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],
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"bos_token": null,
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"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"model_max_length": 32768,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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vocab.json
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