Internlm3 8b Instruct

🚀 InternLM

InternLM（書生·浦語）是由上海人工智能實驗室開發的對話式語言模型，其第3代開源了80億參數的指令模型InternLM3-8B-Instruct，面向通用使用與高階推理，在性能和成本上有顯著優勢，還具備深度思考能力。

🚀 快速開始

你可以根據以下步驟快速使用InternLM3-8B-Instruct模型：

1. 確保滿足依賴要求：transformers >= 4.48。
2. 選擇合適的推理方式，如Transformers、LMDeploy、Ollama或vLLM，並按照相應的代碼示例進行操作。

✨ 主要特性

• 更低的代價取得更高的性能：在推理、知識類任務上取得同量級最優性能，超過Llama3.1-8B和Qwen2.5-7B。InternLM3只用了4萬億詞元進行訓練，對比同級別模型訓練成本節省75%以上。
• 深度思考能力：支持通過長思維鏈求解複雜推理任務的深度思考模式，同時兼顧用戶體驗更流暢的通用回覆模式。

📦 安裝指南

依賴安裝

transformers >= 4.48

不同推理工具安裝

• LMDeploy：

pip install lmdeploy

• Ollama：

# install ollama
curl -fsSL https://ollama.com/install.sh | sh
# fetch 模型
ollama pull internlm/internlm3-8b-instruct
# install python庫
pip install ollama

• vLLM：

pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly

💻 使用示例

常規對話模式

Transformers 推理

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_dir = "internlm/internlm3-8b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
# (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
  # InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
  # pip install -U bitsandbytes
  # 8-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_8bit=True)
  # 4-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_4bit=True)
model = model.eval()

system_prompt = """You are an AI assistant whose name is InternLM (書生·浦語).
- InternLM (書生·浦語) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能實驗室). It is designed to be helpful, honest, and harmless.
- InternLM (書生·浦語) can understand and communicate fluently in the language chosen by the user such as English and 中文."""
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Please tell me five scenic spots in Shanghai"},
 ]
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")

generated_ids = model.generate(tokenized_chat, max_new_tokens=1024, temperature=1, repetition_penalty=1.005, top_k=40, top_p=0.8)

generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(tokenized_chat, generated_ids)
]
prompt = tokenizer.batch_decode(tokenized_chat)[0]
print(prompt)
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

LMDeploy 推理

import lmdeploy
model_dir = "internlm/internlm3-8b-instruct"
pipe = lmdeploy.pipeline(model_dir)
response = pipe(["Please tell me five scenic spots in Shanghai"])
print(response)

啟動兼容 OpenAI API 的服務：

lmdeploy serve api_server internlm/internlm3-8b-instruct --model-name internlm3-8b-instruct --server-port 23333 

向服務端發起聊天請求：

curl http://localhost:23333/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "internlm3-8b-instruct",
    "messages": [
    {"role": "user", "content": "介紹一下深度學習。"}
    ]
    }'

Ollama 推理

import ollama

system_prompt = """You are an AI assistant whose name is InternLM (書生·浦語).
- InternLM (書生·浦語) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能實驗室). It is designed to be helpful, honest, and harmless.
- InternLM (書生·浦語) can understand and communicate fluently in the language chosen by the user such as English and 中文."""

messages = [
    {
        "role": "system",
        "content": system_prompt,
    },
    {
        "role": "user",
        "content": "Please tell me five scenic spots in Shanghai"
    },
]

stream = ollama.chat(
    model='internlm/internlm3-8b-instruct',
    messages=messages,
    stream=True,
)

for chunk in stream:
  print(chunk['message']['content'], end='', flush=True)

vLLM 推理

from vllm import LLM, SamplingParams

llm = LLM(model="internlm/internlm3-8b-instruct")
sampling_params = SamplingParams(temperature=1, repetition_penalty=1.005, top_k=40, top_p=0.8)

system_prompt = """You are an AI assistant whose name is InternLM (書生·浦語).
- InternLM (書生·浦語) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能實驗室). It is designed to be helpful, honest, and harmless.
- InternLM (書生·浦語) can understand and communicate fluently in the language chosen by the user such as English and 中文."""

prompts = [
    {
        "role": "system",
        "content": system_prompt,
    },
    {
        "role": "user",
        "content": "Please tell me five scenic spots in Shanghai"
    },
]
outputs = llm.chat(prompts,
                   sampling_params=sampling_params,
                   use_tqdm=False)
print(outputs)

深度思考模式

Transformers 推理

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_dir = "internlm/internlm3-8b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
# (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
  # InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
  # pip install -U bitsandbytes
  # 8-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_8bit=True)
  # 4-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_4bit=True)
model = model.eval()

thinking_system_prompt = """You are an expert mathematician with extensive experience in mathematical competitions. You approach problems through systematic thinking and rigorous reasoning. When solving problems, follow these thought processes:
## Deep Understanding
Take time to fully comprehend the problem before attempting a solution. Consider:
- What is the real question being asked?
- What are the given conditions and what do they tell us?
- Are there any special restrictions or assumptions?
- Which information is crucial and which is supplementary?
## Multi-angle Analysis
Before solving, conduct thorough analysis:
- What mathematical concepts and properties are involved?
- Can you recall similar classic problems or solution methods?
- Would diagrams or tables help visualize the problem?
- Are there special cases that need separate consideration?
## Systematic Thinking
Plan your solution path:
- Propose multiple possible approaches
- Analyze the feasibility and merits of each method
- Choose the most appropriate method and explain why
- Break complex problems into smaller, manageable steps
## Rigorous Proof
During the solution process:
- Provide solid justification for each step
- Include detailed proofs for key conclusions
- Pay attention to logical connections
- Be vigilant about potential oversights
## Repeated Verification
After completing your solution:
- Verify your results satisfy all conditions
- Check for overlooked special cases
- Consider if the solution can be optimized or simplified
- Review your reasoning process
Remember:
1. Take time to think thoroughly rather than rushing to an answer
2. Rigorously prove each key conclusion
3. Keep an open mind and try different approaches
4. Summarize valuable problem-solving methods
5. Maintain healthy skepticism and verify multiple times
Your response should reflect deep mathematical understanding and precise logical thinking, making your solution path and reasoning clear to others.
When you're ready, present your complete solution with:
- Clear problem understanding
- Detailed solution process
- Key insights
- Thorough verification
Focus on clear, logical progression of ideas and thorough explanation of your mathematical reasoning. Provide answers in the same language as the user asking the question, repeat the final answer using a '\\boxed{}' without any units, you have [[8192]] tokens to complete the answer.
"""
messages = [
    {"role": "system", "content": thinking_system_prompt},
    {"role": "user", "content": "已知函數\(f(x)=\mathrm{e}^{x}-ax - a^{3}\)。\n（1）當\(a = 1\)時，求曲線\(y = f(x)\)在點\((1,f(1))\)處的切線方程；\n（2）若\(f(x)\)有極小值，且極小值小於\(0\)，求\(a\)的取值範圍。"},
 ]
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")

generated_ids = model.generate(tokenized_chat, max_new_tokens=8192)

generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(tokenized_chat, generated_ids)
]
prompt = tokenizer.batch_decode(tokenized_chat)[0]
print(prompt)
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

LMDeploy 推理

from lmdeploy import pipeline, GenerationConfig, ChatTemplateConfig
model_dir = "internlm/internlm3-8b-instruct"
chat_template_config = ChatTemplateConfig(model_name='internlm3')
pipe = pipeline(model_dir, chat_template_config=chat_template_config)

thinking_system_prompt = """You are an expert mathematician with extensive experience in mathematical competitions. You approach problems through systematic thinking and rigorous reasoning. When solving problems, follow these thought processes:
## Deep Understanding
Take time to fully comprehend the problem before attempting a solution. Consider:
- What is the real question being asked?
- What are the given conditions and what do they tell us?
- Are there any special restrictions or assumptions?
- Which information is crucial and which is supplementary?
## Multi-angle Analysis
Before solving, conduct thorough analysis:
- What mathematical concepts and properties are involved?
- Can you recall similar classic problems or solution methods?
- Would diagrams or tables help visualize the problem?
- Are there special cases that need separate consideration?
## Systematic Thinking
Plan your solution path:
- Propose multiple possible approaches
- Analyze the feasibility and merits of each method
- Choose the most appropriate method and explain why
- Break complex problems into smaller, manageable steps
## Rigorous Proof
During the solution process:
- Provide solid justification for each step
- Include detailed proofs for key conclusions
- Pay attention to logical connections
- Be vigilant about potential oversights
## Repeated Verification
After completing your solution:
- Verify your results satisfy all conditions
- Check for overlooked special cases
- Consider if the solution can be optimized or simplified
- Review your reasoning process
Remember:
1. Take time to think thoroughly rather than rushing to an answer
2. Rigorously prove each key conclusion
3. Keep an open mind and try different approaches
4. Summarize valuable problem-solving methods
5. Maintain healthy skepticism and verify multiple times
Your response should reflect deep mathematical understanding and precise logical thinking, making your solution path and reasoning clear to others.
When you're ready, present your complete solution with:
- Clear problem understanding
- Detailed solution process
- Key insights
- Thorough verification
Focus on clear, logical progression of ideas and thorough explanation of your mathematical reasoning. Provide answers in the same language as the user asking the question, repeat the final answer using a '\\boxed{}' without any units, you have [[8192]] tokens to complete the answer.
"""
messages = [
        {"role": "system", "content": thinking_system_prompt},
        {"role": "user", "content": "已知函數\(f(x)=\mathrm{e}^{x}-ax - a^{3}\)。\n（1）當\(a = 1\)時，求曲線\(y = f(x)\)在點\((1,f(1))\)處的切線方程；\n（2）若\(f(x)\)有極小值，且極小值小於\(0\)，求\(a\)的取值範圍。"},
]

response = pipe(messages, gen_config=GenerationConfig(max_new_tokens=2048))
print(response)

Ollama 推理

import ollama

thinking_system_prompt = """You are an expert mathematician with extensive experience in mathematical competitions. You approach problems through systematic thinking and rigorous reasoning. When solving problems, follow these thought processes:
## Deep Understanding
Take time to fully comprehend the problem before attempting a solution. Consider:
- What is the real question being asked?
- What are the given conditions and what do they tell us?
- Are there any special restrictions or assumptions?
- Which information is crucial and which is supplementary?
## Multi-angle Analysis
Before solving, conduct thorough analysis:
- What mathematical concepts and properties are involved?
- Can you recall similar classic problems or solution methods?
- Would diagrams or tables help visualize the problem?
- Are there special cases that need separate consideration?
## Systematic Thinking
Plan your solution path:
- Propose multiple possible approaches
- Analyze the feasibility and merits of each method
- Choose the most appropriate method and explain why
- Break complex problems into smaller, manageable steps
## Rigorous Proof
During the solution process:
- Provide solid justification for each step
- Include detailed proofs for key conclusions
- Pay attention to logical connections
- Be vigilant about potential oversights
## Repeated Verification
After completing your solution:
- Verify your results satisfy all conditions
- Check for overlooked special cases
- Consider if the solution can be optimized or simplified
- Review your reasoning process
Remember:
1. Take time to think thoroughly rather than rushing to an answer
2. Rigorously prove each key conclusion
3. Keep an open mind and try different approaches
4. Summarize valuable problem-solving methods
5. Maintain healthy skepticism and verify multiple times
Your response should reflect deep mathematical understanding and precise logical thinking, making your solution path and reasoning clear to others.
When you're ready, present your complete solution with:
- Clear problem understanding
- Detailed solution process
- Key insights
- Thorough verification
Focus on clear, logical progression of ideas and thorough explanation of your mathematical reasoning. Provide answers in the same language as the user asking the question, repeat the final answer using a '\\boxed{}' without any units, you have [[8192]] tokens to complete the answer.
"""
messages = [
    {
        "role": "system",
        "content": thinking_system_prompt,
    },
    {
        "role": "user",
        "content": "Given the function\(f(x)=\mathrm{e}^{x}-ax - a^{3}\),\n(1) When \(a = 1\), find the equation of the tangent line to the curve \(y = f(x)\) at the point \((1,f(1))\).\n(2) If \(f(x)\) has a local minimum and the minimum value is less than \(0\), determine the range of values for \(a\)."
    },
]

stream = ollama.chat(
    model='internlm/internlm3-8b-instruct',
    messages=messages,
    stream=True,
)

for chunk in stream:
  print(chunk['message']['content'], end='', flush=True)

vLLM 推理

from vllm import LLM, SamplingParams

llm = LLM(model="internlm/internlm3-8b-instruct")
sampling_params = SamplingParams(temperature=1, repetition_penalty=1.005, top_k=40, top_p=0.8, max_tokens=8192)

thinking_system_prompt = """You are an expert mathematician with extensive experience in mathematical competitions. You approach problems through systematic thinking and rigorous reasoning. When solving problems, follow these thought processes:
## Deep Understanding
Take time to fully comprehend the problem before attempting a solution. Consider:
- What is the real question being asked?
- What are the given conditions and what do they tell us?
- Are there any special restrictions or assumptions?
- Which information is crucial and which is supplementary?
## Multi-angle Analysis
Before solving, conduct thorough analysis:
- What mathematical concepts and properties are involved?
- Can you recall similar classic problems or solution methods?
- Would diagrams or tables help visualize the problem?
- Are there special cases that need separate consideration?
## Systematic Thinking
Plan your solution path:
- Propose multiple possible approaches
- Analyze the feasibility and merits of each method
- Choose the most appropriate method and explain why
- Break complex problems into smaller, manageable steps
## Rigorous Proof
During the solution process:
- Provide solid justification for each step
- Include detailed proofs for key conclusions
- Pay attention to logical connections
- Be vigilant about potential oversights
## Repeated Verification
After completing your solution:
- Verify your results satisfy all conditions
- Check for overlooked special cases
- Consider if the solution can be optimized or simplified
- Review your reasoning process
Remember:
1. Take time to think thoroughly rather than rushing to an answer
2. Rigorously prove each key conclusion
3. Keep an open mind and try different approaches
4. Summarize valuable problem-solving methods
5. Maintain healthy skepticism and verify multiple times
Your response should reflect deep mathematical understanding and precise logical thinking, making your solution path and reasoning clear to others.
When you're ready, present your complete solution with:
- Clear problem understanding
- Detailed solution process
- Key insights
- Thorough verification
Focus on clear, logical progression of ideas and thorough explanation of your mathematical reasoning. Provide answers in the same language as the user asking the question, repeat the final answer using a '\\boxed{}' without any units, you have [[8192]] tokens to complete the answer.
"""
prompts = [
    {
        "role": "system",
        "content": thinking_system_prompt,
    },
    {
        "role": "user",
        "content": "已知函數\(f(x)=\mathrm{e}^{x}-ax - a^{3}\)。\n（1）當\(a = 1\)時，求曲線\(y = f(x)\)在點\((1,f(1))\)處的切線方程；\n（2）若\(f(x)\)有極小值，且極小值小於\(0\)，求\(a\)的取值範圍。"
    },
]
outputs = llm.chat(prompts,
                   sampling_params=sampling_params,
                   use_tqdm=False)
print(outputs)

📚 詳細文檔

性能評測

我們使用開源評測工具 OpenCompass 從學科綜合能力、語言能力、知識能力、推理能力、理解能力五大能力維度對InternLM開展全面評測，部分評測結果如下表所示，你可以訪問 OpenCompass 榜單 獲取更多的評測結果。

• 表中標粗的數值表示在對比的開源模型中的最高值。
• 以上評測結果基於 OpenCompass 獲得(部分數據標註*代表使用深度思考模式進行評測)，具體測試細節可參見 OpenCompass 中提供的配置文件。
• 評測數據會因 OpenCompass 的版本迭代而存在數值差異，請以 OpenCompass 最新版的評測結果為主。

深度思考模式說明

深度思考 Demo

深度思考 system prompt

thinking_system_prompt = """You are an expert mathematician with extensive experience in mathematical competitions. You approach problems through systematic thinking and rigorous reasoning. When solving problems, follow these thought processes:
## Deep Understanding
Take time to fully comprehend the problem before attempting a solution. Consider:
- What is the real question being asked?
- What are the given conditions and what do they tell us?
- Are there any special restrictions or assumptions?
- Which information is crucial and which is supplementary?
## Multi-angle Analysis
Before solving, conduct thorough analysis:
- What mathematical concepts and properties are involved?
- Can you recall similar classic problems or solution methods?
- Would diagrams or tables help visualize the problem?
- Are there special cases that need separate consideration?
## Systematic Thinking
Plan your solution path:
- Propose multiple possible approaches
- Analyze the feasibility and merits of each method
- Choose the most appropriate method and explain why
- Break complex problems into smaller, manageable steps
## Rigorous Proof
During the solution process:
- Provide solid justification for each step
- Include detailed proofs for key conclusions
- Pay attention to logical connections
- Be vigilant about potential oversights
## Repeated Verification
After completing your solution:
- Verify your results satisfy all conditions
- Check for overlooked special cases
- Consider if the solution can be optimized or simplified
- Review your reasoning process
Remember:
1. Take time to think thoroughly rather than rushing to an answer
2. Rigorously prove each key conclusion
3. Keep an open mind and try different approaches
4. Summarize valuable problem-solving methods
5. Maintain healthy skepticism and verify multiple times
Your response should reflect deep mathematical understanding and precise logical thinking, making your solution path and reasoning clear to others.
When you're ready, present your complete solution with:
- Clear problem understanding
- Detailed solution process
- Key insights
- Thorough verification
Focus on clear, logical progression of ideas and thorough explanation of your mathematical reasoning. Provide answers in the same language as the user asking the question, repeat the final answer using a '\\boxed{}' without any units, you have [[8192]] tokens to complete the answer.
"""

🔧 技術細節

文檔中未提供詳細的技術實現細節。

📄 許可證

本倉庫的代碼和權重依照 Apache-2.0 協議開源。

引用

@misc{cai2024internlm2,
      title={InternLM2 Technical Report},
      author={Zheng Cai and Maosong Cao and Haojiong Chen and Kai Chen and Keyu Chen and Xin Chen and Xun Chen and Zehui Chen and Zhi Chen and Pei Chu and Xiaoyi Dong and Haodong Duan and Qi Fan and Zhaoye Fei and Yang Gao and Jiaye Ge and Chenya Gu and Yuzhe Gu and Tao Gui and Aijia Guo and Qipeng Guo and Conghui He and Yingfan Hu and Ting Huang and Tao Jiang and Penglong Jiao and Zhenjiang Jin and Zhikai Lei and Jiaxing Li and Jingwen Li and Linyang Li and Shuaibin Li and Wei Li and Yining Li and Hongwei Liu and Jiangning Liu and Jiawei Hong and Kaiwen Liu and Kuikun Liu and Xiaoran Liu and Chengqi Lv and Haijun Lv and Kai Lv and Li Ma and Runyuan Ma and Zerun Ma and Wenchang Ning and Linke Ouyang and Jiantao Qiu and Yuan Qu and Fukai Shang and Yunfan Shao and Demin Song and Zifan Song and Zhihao Sui and Peng Sun and Yu Sun and Huanze Tang and Bin Wang and Guoteng Wang and Jiaqi Wang and Jiayu Wang and Rui Wang and Yudong Wang and Ziyi Wang and Xingjian Wei and Qizhen Weng and Fan Wu and Yingtong Xiong and Chao Xu and Ruiliang Xu and Hang Yan and Yirong Yan and Xiaogui Yang and Haochen Ye and Huaiyuan Ying and Jia Yu and Jing Yu and Yuhang Zang and Chuyu Zhang and Li Zhang and Pan Zhang and Peng Zhang and Ruijie Zhang and Shuo Zhang and Songyang Zhang and Wenjian Zhang and Wenwei Zhang and Xingcheng Zhang and Xinyue Zhang and Hui Zhao and Qian Zhao and Xiaomeng Zhao and Fengzhe Zhou and Zaida Zhou and Jingming Zhuo and Yicheng Zou and Xipeng Qiu and Yu Qiao and Dahua Lin},
      year={2024},
      eprint={2403.17297},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

⚠️ 重要提示

儘管在訓練過程中我們非常注重模型的安全性，盡力促使模型輸出符合倫理和法律要求的文本，但受限於模型大小以及概率生成範式，模型可能會產生各種不符合預期的輸出，例如回覆內容包含偏見、歧視等有害內容，請勿傳播這些內容。由於傳播不良信息導致的任何後果，本項目不承擔責任。