test
#4
by
haijunlv
- opened
- LICENSE.txt +0 -201
- README.md +70 -231
- model-00001-of-00002.safetensors → model-00001-of-00004.safetensors +2 -2
- model-00002-of-00002.safetensors → model-00002-of-00004.safetensors +2 -2
- model-00003-of-00004.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model.safetensors.index.json +436 -436
- modeling_internlm3.py +1 -2
- tokenization_internlm3.py +1 -1
LICENSE.txt
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README.md
CHANGED
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---
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license: apache-2.0
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pipeline_tag: text-generation
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---
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# InternLM
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@@ -23,7 +19,7 @@ pipeline_tag: text-generation
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[](https://github.com/internLM/OpenCompass/)
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[💻Github Repo](https://github.com/InternLM/InternLM) • [
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</div>
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@@ -48,26 +44,25 @@ InternLM3 supports both the deep thinking mode for solving complicated reasoning
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We conducted a comprehensive evaluation of InternLM using the open-source evaluation tool [OpenCompass](https://github.com/internLM/OpenCompass/). The evaluation covered five dimensions of capabilities: disciplinary competence, language competence, knowledge competence, inference competence, and comprehension competence. Here are some of the evaluation results, and you can visit the [OpenCompass leaderboard](https://rank.opencompass.org.cn) for more evaluation results.
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| ------------ | ------------------------------- | --------------------- | ------------------- | -------------------- |
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| General | CMMLU(0-shot) | **83.1** | 75.8 | 53.9 | 66.0
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| | MMLU(0-shot) | 76.6 | **76.8** | 71.8 | 82.7
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| | MMLU-Pro(0-shot) | **57.6** | 56.2 | 48.1 | 64.1
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| Reasoning | GPQA-Diamond(0-shot) | **37.4** | 33.3 | 24.2 | 42.9
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| | DROP(0-shot) | **83.1** | 80.4 | 81.6 | 85.2
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| | HellaSwag(10-shot) | **91.2** | 85.3 | 76.7 | 89.5
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| | KOR-Bench(0-shot) | **56.4** | 44.6 | 47.7 | 58.2
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| MATH | MATH-500(0-shot) | **83.0*** | 72.4 | 48.4 | 74.0
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| | AIME2024(0-shot) | **20.0*** | 16.7 | 6.7 | 13.3
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| Coding | LiveCodeBench(2407-2409 Pass@1) | **17.8** | 16.8 | 12.9 | 21.8
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| | HumanEval(Pass@1) | 82.3 | **85.4** | 72.0 | 86.6
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| Instrunction | IFEval(Prompt-Strict) | **79.3** | 71.7 | 75.2 | 79.7
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| Long Context | RULER(4-128K Average) | 87.9 | 81.4 | **88.5** | 90.7
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| Chat | AlpacaEval 2.0(LC WinRate) | **51.1** | 30.3 | 25.0 | 50.7
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| | WildBench(Raw Score) | **33.1** | 23.3 | 1.5 | 40.3
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| | MT-Bench-101(Score 1-10) | **8.59** | 8.49 | 8.37 | 8.87
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- Values marked in bold indicate the **highest** in open source models
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- The evaluation results were obtained from [OpenCompass](https://github.com/internLM/OpenCompass/) (some data marked with *, which means evaluating with Thinking Mode), and evaluation configuration can be found in the configuration files provided by [OpenCompass](https://github.com/internLM/OpenCompass/).
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- The evaluation data may have numerical differences due to the version iteration of [OpenCompass](https://github.com/internLM/OpenCompass/), so please refer to the latest evaluation results of [OpenCompass](https://github.com/internLM/OpenCompass/).
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@@ -91,7 +86,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
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model_dir = "internlm/internlm3-8b-instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
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# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
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| 94 |
-
model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.
|
| 95 |
# (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
|
| 96 |
# InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
|
| 97 |
# pip install -U bitsandbytes
|
|
@@ -106,7 +101,7 @@ messages = [
|
|
| 106 |
{"role": "system", "content": system_prompt},
|
| 107 |
{"role": "user", "content": "Please tell me five scenic spots in Shanghai"},
|
| 108 |
]
|
| 109 |
-
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
|
| 110 |
|
| 111 |
generated_ids = model.generate(tokenized_chat, max_new_tokens=1024, temperature=1, repetition_penalty=1.005, top_k=40, top_p=0.8)
|
| 112 |
|
|
@@ -115,7 +110,7 @@ generated_ids = [
|
|
| 115 |
]
|
| 116 |
prompt = tokenizer.batch_decode(tokenized_chat)[0]
|
| 117 |
print(prompt)
|
| 118 |
-
response = tokenizer.batch_decode(generated_ids
|
| 119 |
print(response)
|
| 120 |
```
|
| 121 |
|
|
@@ -162,54 +157,15 @@ Find more details in the [LMDeploy documentation](https://lmdeploy.readthedocs.i
|
|
| 162 |
|
| 163 |
#### Ollama inference
|
| 164 |
|
| 165 |
-
|
| 166 |
-
|
| 167 |
-
```python
|
| 168 |
-
# install ollama
|
| 169 |
-
curl -fsSL https://ollama.com/install.sh | sh
|
| 170 |
-
# fetch model
|
| 171 |
-
ollama pull internlm/internlm3-8b-instruct
|
| 172 |
-
# install
|
| 173 |
-
pip install ollama
|
| 174 |
-
```
|
| 175 |
-
|
| 176 |
-
inference code,
|
| 177 |
-
|
| 178 |
-
```python
|
| 179 |
-
import ollama
|
| 180 |
-
|
| 181 |
-
system_prompt = """You are an AI assistant whose name is InternLM (书生·浦语).
|
| 182 |
-
- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.
|
| 183 |
-
- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文."""
|
| 184 |
-
|
| 185 |
-
messages = [
|
| 186 |
-
{
|
| 187 |
-
"role": "system",
|
| 188 |
-
"content": system_prompt,
|
| 189 |
-
},
|
| 190 |
-
{
|
| 191 |
-
"role": "user",
|
| 192 |
-
"content": "Please tell me five scenic spots in Shanghai"
|
| 193 |
-
},
|
| 194 |
-
]
|
| 195 |
-
|
| 196 |
-
stream = ollama.chat(
|
| 197 |
-
model='internlm/internlm3-8b-instruct',
|
| 198 |
-
messages=messages,
|
| 199 |
-
stream=True,
|
| 200 |
-
)
|
| 201 |
-
|
| 202 |
-
for chunk in stream:
|
| 203 |
-
print(chunk['message']['content'], end='', flush=True)
|
| 204 |
-
```
|
| 205 |
-
|
| 206 |
|
| 207 |
#### vLLM inference
|
| 208 |
|
| 209 |
-
|
| 210 |
|
| 211 |
```python
|
| 212 |
-
|
|
|
|
| 213 |
```
|
| 214 |
|
| 215 |
inference code:
|
|
@@ -311,7 +267,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
|
|
| 311 |
model_dir = "internlm/internlm3-8b-instruct"
|
| 312 |
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
| 313 |
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
|
| 314 |
-
model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.
|
| 315 |
# (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
|
| 316 |
# InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
|
| 317 |
# pip install -U bitsandbytes
|
|
@@ -323,7 +279,7 @@ messages = [
|
|
| 323 |
{"role": "system", "content": thinking_system_prompt},
|
| 324 |
{"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 \(a\)."},
|
| 325 |
]
|
| 326 |
-
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
|
| 327 |
|
| 328 |
generated_ids = model.generate(tokenized_chat, max_new_tokens=8192)
|
| 329 |
|
|
@@ -332,7 +288,7 @@ generated_ids = [
|
|
| 332 |
]
|
| 333 |
prompt = tokenizer.batch_decode(tokenized_chat)[0]
|
| 334 |
print(prompt)
|
| 335 |
-
response = tokenizer.batch_decode(generated_ids
|
| 336 |
print(response)
|
| 337 |
```
|
| 338 |
#### LMDeploy inference
|
|
@@ -362,52 +318,14 @@ print(response)
|
|
| 362 |
|
| 363 |
#### Ollama inference
|
| 364 |
|
| 365 |
-
|
| 366 |
-
|
| 367 |
-
```python
|
| 368 |
-
# install ollama
|
| 369 |
-
curl -fsSL https://ollama.com/install.sh | sh
|
| 370 |
-
# fetch model
|
| 371 |
-
ollama pull internlm/internlm3-8b-instruct
|
| 372 |
-
# install
|
| 373 |
-
pip install ollama
|
| 374 |
-
```
|
| 375 |
-
|
| 376 |
-
inference code,
|
| 377 |
-
|
| 378 |
-
```python
|
| 379 |
-
import ollama
|
| 380 |
-
|
| 381 |
-
messages = [
|
| 382 |
-
{
|
| 383 |
-
"role": "system",
|
| 384 |
-
"content": thinking_system_prompt,
|
| 385 |
-
},
|
| 386 |
-
{
|
| 387 |
-
"role": "user",
|
| 388 |
-
"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 \(a\)."
|
| 389 |
-
},
|
| 390 |
-
]
|
| 391 |
-
|
| 392 |
-
stream = ollama.chat(
|
| 393 |
-
model='internlm/internlm3-8b-instruct',
|
| 394 |
-
messages=messages,
|
| 395 |
-
stream=True,
|
| 396 |
-
)
|
| 397 |
-
|
| 398 |
-
for chunk in stream:
|
| 399 |
-
print(chunk['message']['content'], end='', flush=True)
|
| 400 |
-
```
|
| 401 |
-
|
| 402 |
-
|
| 403 |
-
####
|
| 404 |
|
| 405 |
#### vLLM inference
|
| 406 |
|
| 407 |
-
|
| 408 |
-
|
| 409 |
```python
|
| 410 |
-
|
|
|
|
| 411 |
```
|
| 412 |
|
| 413 |
inference code
|
|
@@ -471,26 +389,25 @@ InternLM3支持通过长思维链求解复杂推理任务的深度思考模式
|
|
| 471 |
|
| 472 |
我们使用开源评测工具 [OpenCompass](https://github.com/internLM/OpenCompass/) 从学科综合能力、语言能力、知识能力、推理能力、理解能力五大能力维度对InternLM开展全面评测，部分评测结果如下表所示，欢迎访问[ OpenCompass 榜单 ](https://rank.opencompass.org.cn)获取更多的评测结果。
|
| 473 |
|
| 474 |
-
|
|
| 475 |
-
| ------------ | ------------------------------- | --------------------- | ------------------- | -------------------- |
|
| 476 |
-
| General | CMMLU(0-shot) | **83.1** | 75.8 | 53.9 | 66.0
|
| 477 |
-
| | MMLU(0-shot) | 76.6 | **76.8** | 71.8 | 82.7
|
| 478 |
-
| | MMLU-Pro(0-shot) | **57.6** | 56.2 | 48.1 | 64.1
|
| 479 |
-
| Reasoning | GPQA-Diamond(0-shot) | **37.4** | 33.3 | 24.2 | 42.9
|
| 480 |
-
| | DROP(0-shot) | **83.1** | 80.4 | 81.6 | 85.2
|
| 481 |
-
| | HellaSwag(10-shot) | **91.2** | 85.3 | 76.7 | 89.5
|
| 482 |
-
| | KOR-Bench(0-shot) | **56.4** | 44.6 | 47.7 | 58.2
|
| 483 |
-
| MATH | MATH-500(0-shot) | **83.0*** | 72.4 | 48.4 | 74.0
|
| 484 |
-
| | AIME2024(0-shot) | **20.0*** | 16.7 | 6.7 | 13.3
|
| 485 |
-
| Coding | LiveCodeBench(2407-2409 Pass@1) | **17.8** | 16.8 | 12.9 | 21.8
|
| 486 |
-
| | HumanEval(Pass@1) | 82.3 | **85.4** | 72.0 | 86.6
|
| 487 |
-
| Instrunction | IFEval(Prompt-Strict) | **79.3** | 71.7 | 75.2 | 79.7
|
| 488 |
-
| LongContext | RULER(4-128K Average) | 87.9 | 81.4 | **88.5** | 90.7
|
| 489 |
-
| Chat | AlpacaEval 2.0(LC WinRate) | **51.1** | 30.3 | 25.0 | 50.7
|
| 490 |
-
| | WildBench(Raw Score) | **33.1** | 23.3 | 1.5 | 40.3
|
| 491 |
-
| | MT-Bench-101(Score 1-10) | **8.59** | 8.49 | 8.37 | 8.87
|
| 492 |
-
|
| 493 |
-
- 表中标粗的数值表示在对比的开源模型中的最高值。
|
| 494 |
- 以上评测结果基于 [OpenCompass](https://github.com/internLM/OpenCompass/) 获得(部分数据标注`*`代表使用深度思考模式进行评测)，具体测试细节可参见 [OpenCompass](https://github.com/internLM/OpenCompass/) 中提供的配置文件。
|
| 495 |
- 评测数据会因 [OpenCompass](https://github.com/internLM/OpenCompass/) 的版本迭代而存在数值差异，请以 [OpenCompass](https://github.com/internLM/OpenCompass/) 最新版的评测结果为主。
|
| 496 |
|
|
@@ -518,7 +435,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
|
|
| 518 |
model_dir = "internlm/internlm3-8b-instruct"
|
| 519 |
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
| 520 |
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
|
| 521 |
-
model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.
|
| 522 |
# (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
|
| 523 |
# InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
|
| 524 |
# pip install -U bitsandbytes
|
|
@@ -533,7 +450,7 @@ messages = [
|
|
| 533 |
{"role": "system", "content": system_prompt},
|
| 534 |
{"role": "user", "content": "Please tell me five scenic spots in Shanghai"},
|
| 535 |
]
|
| 536 |
-
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
|
| 537 |
|
| 538 |
generated_ids = model.generate(tokenized_chat, max_new_tokens=1024, temperature=1, repetition_penalty=1.005, top_k=40, top_p=0.8)
|
| 539 |
|
|
@@ -542,7 +459,7 @@ generated_ids = [
|
|
| 542 |
]
|
| 543 |
prompt = tokenizer.batch_decode(tokenized_chat)[0]
|
| 544 |
print(prompt)
|
| 545 |
-
response = tokenizer.batch_decode(generated_ids
|
| 546 |
print(response)
|
| 547 |
```
|
| 548 |
|
|
@@ -590,56 +507,15 @@ curl http://localhost:23333/v1/chat/completions \
|
|
| 590 |
|
| 591 |
##### Ollama 推理
|
| 592 |
|
| 593 |
-
|
| 594 |
-
|
| 595 |
-
```python
|
| 596 |
-
# install ollama
|
| 597 |
-
curl -fsSL https://ollama.com/install.sh | sh
|
| 598 |
-
# fetch 模型
|
| 599 |
-
ollama pull internlm/internlm3-8b-instruct
|
| 600 |
-
# install python库
|
| 601 |
-
pip install ollama
|
| 602 |
-
```
|
| 603 |
-
|
| 604 |
-
推理代码
|
| 605 |
-
|
| 606 |
-
```python
|
| 607 |
-
import ollama
|
| 608 |
-
|
| 609 |
-
system_prompt = """You are an AI assistant whose name is InternLM (书生·浦语).
|
| 610 |
-
- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.
|
| 611 |
-
- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文."""
|
| 612 |
-
|
| 613 |
-
messages = [
|
| 614 |
-
{
|
| 615 |
-
"role": "system",
|
| 616 |
-
"content": system_prompt,
|
| 617 |
-
},
|
| 618 |
-
{
|
| 619 |
-
"role": "user",
|
| 620 |
-
"content": "Please tell me five scenic spots in Shanghai"
|
| 621 |
-
},
|
| 622 |
-
]
|
| 623 |
-
|
| 624 |
-
stream = ollama.chat(
|
| 625 |
-
model='internlm/internlm3-8b-instruct',
|
| 626 |
-
messages=messages,
|
| 627 |
-
stream=True,
|
| 628 |
-
)
|
| 629 |
-
|
| 630 |
-
for chunk in stream:
|
| 631 |
-
print(chunk['message']['content'], end='', flush=True)
|
| 632 |
-
```
|
| 633 |
-
|
| 634 |
-
|
| 635 |
-
####
|
| 636 |
|
| 637 |
##### vLLM 推理
|
| 638 |
|
| 639 |
-
|
| 640 |
|
| 641 |
-
```
|
| 642 |
-
|
|
|
|
| 643 |
```
|
| 644 |
|
| 645 |
推理代码
|
|
@@ -740,7 +616,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
|
|
| 740 |
model_dir = "internlm/internlm3-8b-instruct"
|
| 741 |
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
| 742 |
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
|
| 743 |
-
model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.
|
| 744 |
# (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
|
| 745 |
# InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
|
| 746 |
# pip install -U bitsandbytes
|
|
@@ -752,7 +628,7 @@ messages = [
|
|
| 752 |
{"role": "system", "content": thinking_system_prompt},
|
| 753 |
{"role": "user", "content": "已知函数\(f(x)=\mathrm{e}^{x}-ax - a^{3}\)。\n（1）当\(a = 1\)时，求曲线\(y = f(x)\)在点\((1,f(1))\)处的切线方程；\n（2）若\(f(x)\)有极小值，且极小值小于\(0\)，求\(a\)的取值范围。"},
|
| 754 |
]
|
| 755 |
-
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
|
| 756 |
|
| 757 |
generated_ids = model.generate(tokenized_chat, max_new_tokens=8192)
|
| 758 |
|
|
@@ -761,7 +637,7 @@ generated_ids = [
|
|
| 761 |
]
|
| 762 |
prompt = tokenizer.batch_decode(tokenized_chat)[0]
|
| 763 |
print(prompt)
|
| 764 |
-
response = tokenizer.batch_decode(generated_ids
|
| 765 |
print(response)
|
| 766 |
```
|
| 767 |
##### LMDeploy 推理
|
|
@@ -791,52 +667,15 @@ print(response)
|
|
| 791 |
|
| 792 |
##### Ollama 推理
|
| 793 |
|
| 794 |
-
|
| 795 |
-
|
| 796 |
-
```python
|
| 797 |
-
# install ollama
|
| 798 |
-
curl -fsSL https://ollama.com/install.sh | sh
|
| 799 |
-
# fetch 模型
|
| 800 |
-
ollama pull internlm/internlm3-8b-instruct
|
| 801 |
-
# install python库
|
| 802 |
-
pip install ollama
|
| 803 |
-
```
|
| 804 |
-
|
| 805 |
-
inference code,
|
| 806 |
-
|
| 807 |
-
```python
|
| 808 |
-
import ollama
|
| 809 |
-
|
| 810 |
-
messages = [
|
| 811 |
-
{
|
| 812 |
-
"role": "system",
|
| 813 |
-
"content": thinking_system_prompt,
|
| 814 |
-
},
|
| 815 |
-
{
|
| 816 |
-
"role": "user",
|
| 817 |
-
"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 \(a\)."
|
| 818 |
-
},
|
| 819 |
-
]
|
| 820 |
-
|
| 821 |
-
stream = ollama.chat(
|
| 822 |
-
model='internlm/internlm3-8b-instruct',
|
| 823 |
-
messages=messages,
|
| 824 |
-
stream=True,
|
| 825 |
-
)
|
| 826 |
-
|
| 827 |
-
for chunk in stream:
|
| 828 |
-
print(chunk['message']['content'], end='', flush=True)
|
| 829 |
-
```
|
| 830 |
-
|
| 831 |
-
|
| 832 |
-
####
|
| 833 |
|
| 834 |
##### vLLM 推理
|
| 835 |
|
| 836 |
-
|
| 837 |
|
| 838 |
-
```
|
| 839 |
-
|
|
|
|
| 840 |
```
|
| 841 |
|
| 842 |
推理代码
|
|
@@ -886,4 +725,4 @@ print(outputs)
|
|
| 886 |
archivePrefix={arXiv},
|
| 887 |
primaryClass={cs.CL}
|
| 888 |
}
|
| 889 |
-
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
# InternLM
|
| 2 |
|
| 3 |
|
|
|
|
| 19 |
|
| 20 |
[](https://github.com/internLM/OpenCompass/)
|
| 21 |
|
| 22 |
+
[💻Github Repo](https://github.com/InternLM/InternLM) • [🤔Reporting Issues](https://github.com/InternLM/InternLM/issues/new) • [📜Technical Report](https://arxiv.org/abs/2403.17297)
|
| 23 |
|
| 24 |
</div>
|
| 25 |
|
|
|
|
| 44 |
|
| 45 |
We conducted a comprehensive evaluation of InternLM using the open-source evaluation tool [OpenCompass](https://github.com/internLM/OpenCompass/). The evaluation covered five dimensions of capabilities: disciplinary competence, language competence, knowledge competence, inference competence, and comprehension competence. Here are some of the evaluation results, and you can visit the [OpenCompass leaderboard](https://rank.opencompass.org.cn) for more evaluation results.
|
| 46 |
|
| 47 |
+
| Benchmark | | InternLM3-8B-Instruct | Qwen2.5-7B-Instruct | Llama3.1-8B-Instruct | GPT-4o-mini(close source) |
|
| 48 |
+
| ------------ | ------------------------------- | --------------------- | ------------------- | -------------------- | ------------------------- |
|
| 49 |
+
| General | CMMLU(0-shot) | **83.1** | 75.8 | 53.9 | 66.0 |
|
| 50 |
+
| | MMLU(0-shot) | 76.6 | **76.8** | 71.8 | 82.7 |
|
| 51 |
+
| | MMLU-Pro(0-shot) | **57.6** | 56.2 | 48.1 | 64.1 |
|
| 52 |
+
| Reasoning | GPQA-Diamond(0-shot) | **37.4** | 33.3 | 24.2 | 42.9 |
|
| 53 |
+
| | DROP(0-shot) | **83.1** | 80.4 | 81.6 | 85.2 |
|
| 54 |
+
| | HellaSwag(10-shot) | **91.2** | 85.3 | 76.7 | 89.5 |
|
| 55 |
+
| | KOR-Bench(0-shot) | **56.4** | 44.6 | 47.7 | 58.2 |
|
| 56 |
+
| MATH | MATH-500(0-shot) | **83.0*** | 72.4 | 48.4 | 74.0 |
|
| 57 |
+
| | AIME2024(0-shot) | **20.0*** | 16.7 | 6.7 | 13.3 |
|
| 58 |
+
| Coding | LiveCodeBench(2407-2409 Pass@1) | **17.8** | 16.8 | 12.9 | 21.8 |
|
| 59 |
+
| | HumanEval(Pass@1) | 82.3 | **85.4** | 72.0 | 86.6 |
|
| 60 |
+
| Instrunction | IFEval(Prompt-Strict) | **79.3** | 71.7 | 75.2 | 79.7 |
|
| 61 |
+
| Long Context | RULER(4-128K Average) | 87.9 | 81.4 | **88.5** | 90.7 |
|
| 62 |
+
| Chat | AlpacaEval 2.0(LC WinRate) | **51.1** | 30.3 | 25.0 | 50.7 |
|
| 63 |
+
| | WildBench(Raw Score) | **33.1** | 23.3 | 1.5 | 40.3 |
|
| 64 |
+
| | MT-Bench-101(Score 1-10) | **8.59** | 8.49 | 8.37 | 8.87 |
|
| 65 |
+
|
|
|
|
| 66 |
- The evaluation results were obtained from [OpenCompass](https://github.com/internLM/OpenCompass/) (some data marked with *, which means evaluating with Thinking Mode), and evaluation configuration can be found in the configuration files provided by [OpenCompass](https://github.com/internLM/OpenCompass/).
|
| 67 |
- The evaluation data may have numerical differences due to the version iteration of [OpenCompass](https://github.com/internLM/OpenCompass/), so please refer to the latest evaluation results of [OpenCompass](https://github.com/internLM/OpenCompass/).
|
| 68 |
|
|
|
|
| 86 |
model_dir = "internlm/internlm3-8b-instruct"
|
| 87 |
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
| 88 |
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
|
| 89 |
+
model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.float16)
|
| 90 |
# (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
|
| 91 |
# InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
|
| 92 |
# pip install -U bitsandbytes
|
|
|
|
| 101 |
{"role": "system", "content": system_prompt},
|
| 102 |
{"role": "user", "content": "Please tell me five scenic spots in Shanghai"},
|
| 103 |
]
|
| 104 |
+
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
|
| 105 |
|
| 106 |
generated_ids = model.generate(tokenized_chat, max_new_tokens=1024, temperature=1, repetition_penalty=1.005, top_k=40, top_p=0.8)
|
| 107 |
|
|
|
|
| 110 |
]
|
| 111 |
prompt = tokenizer.batch_decode(tokenized_chat)[0]
|
| 112 |
print(prompt)
|
| 113 |
+
response = tokenizer.batch_decode(generated_ids)[0]
|
| 114 |
print(response)
|
| 115 |
```
|
| 116 |
|
|
|
|
| 157 |
|
| 158 |
#### Ollama inference
|
| 159 |
|
| 160 |
+
TODO
|
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|
| 161 |
|
| 162 |
#### vLLM inference
|
| 163 |
|
| 164 |
+
We are still working on merging the PR(https://github.com/vllm-project/vllm/pull/12037) into vLLM. In the meantime, please use the following PR link to install it manually.
|
| 165 |
|
| 166 |
```python
|
| 167 |
+
git clone -b support-internlm3 https://github.com/RunningLeon/vllm.git
|
| 168 |
+
pip install -e .
|
| 169 |
```
|
| 170 |
|
| 171 |
inference code:
|
|
|
|
| 267 |
model_dir = "internlm/internlm3-8b-instruct"
|
| 268 |
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
| 269 |
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
|
| 270 |
+
model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.float16)
|
| 271 |
# (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
|
| 272 |
# InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
|
| 273 |
# pip install -U bitsandbytes
|
|
|
|
| 279 |
{"role": "system", "content": thinking_system_prompt},
|
| 280 |
{"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 \(a\)."},
|
| 281 |
]
|
| 282 |
+
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
|
| 283 |
|
| 284 |
generated_ids = model.generate(tokenized_chat, max_new_tokens=8192)
|
| 285 |
|
|
|
|
| 288 |
]
|
| 289 |
prompt = tokenizer.batch_decode(tokenized_chat)[0]
|
| 290 |
print(prompt)
|
| 291 |
+
response = tokenizer.batch_decode(generated_ids)[0]
|
| 292 |
print(response)
|
| 293 |
```
|
| 294 |
#### LMDeploy inference
|
|
|
|
| 318 |
|
| 319 |
#### Ollama inference
|
| 320 |
|
| 321 |
+
TODO
|
|
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|
|
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|
|
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|
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|
| 322 |
|
| 323 |
#### vLLM inference
|
| 324 |
|
| 325 |
+
We are still working on merging the PR(https://github.com/vllm-project/vllm/pull/12037) into vLLM. In the meantime, please use the following PR link to install it manually.
|
|
|
|
| 326 |
```python
|
| 327 |
+
git clone https://github.com/RunningLeon/vllm.git
|
| 328 |
+
pip install -e .
|
| 329 |
```
|
| 330 |
|
| 331 |
inference code
|
|
|
|
| 389 |
|
| 390 |
我们使用开源评测工具 [OpenCompass](https://github.com/internLM/OpenCompass/) 从学科综合能力、语言能力、知识能力、推理能力、理解能力五大能力维度对InternLM开展全面评测，部分评测结果如下表所示，欢迎访问[ OpenCompass 榜单 ](https://rank.opencompass.org.cn)获取更多的评测结果。
|
| 391 |
|
| 392 |
+
| 评测集\模型 | | InternLM3-8B-Instruct | Qwen2.5-7B-Instruct | Llama3.1-8B-Instruct | GPT-4o-mini(close source) |
|
| 393 |
+
| ------------ | ------------------------------- | --------------------- | ------------------- | -------------------- | ------------------------- |
|
| 394 |
+
| General | CMMLU(0-shot) | **83.1** | 75.8 | 53.9 | 66.0 |
|
| 395 |
+
| | MMLU(0-shot) | 76.6 | **76.8** | 71.8 | 82.7 |
|
| 396 |
+
| | MMLU-Pro(0-shot) | **57.6** | 56.2 | 48.1 | 64.1 |
|
| 397 |
+
| Reasoning | GPQA-Diamond(0-shot) | **37.4** | 33.3 | 24.2 | 42.9 |
|
| 398 |
+
| | DROP(0-shot) | **83.1** | 80.4 | 81.6 | 85.2 |
|
| 399 |
+
| | HellaSwag(10-shot) | **91.2** | 85.3 | 76.7 | 89.5 |
|
| 400 |
+
| | KOR-Bench(0-shot) | **56.4** | 44.6 | 47.7 | 58.2 |
|
| 401 |
+
| MATH | MATH-500(0-shot) | **83.0*** | 72.4 | 48.4 | 74.0 |
|
| 402 |
+
| | AIME2024(0-shot) | **20.0*** | 16.7 | 6.7 | 13.3 |
|
| 403 |
+
| Coding | LiveCodeBench(2407-2409 Pass@1) | **17.8** | 16.8 | 12.9 | 21.8 |
|
| 404 |
+
| | HumanEval(Pass@1) | 82.3 | **85.4** | 72.0 | 86.6 |
|
| 405 |
+
| Instrunction | IFEval(Prompt-Strict) | **79.3** | 71.7 | 75.2 | 79.7 |
|
| 406 |
+
| LongContext | RULER(4-128K Average) | 87.9 | 81.4 | **88.5** | 90.7 |
|
| 407 |
+
| Chat | AlpacaEval 2.0(LC WinRate) | **51.1** | 30.3 | 25.0 | 50.7 |
|
| 408 |
+
| | WildBench(Raw Score) | **33.1** | 23.3 | 1.5 | 40.3 |
|
| 409 |
+
| | MT-Bench-101(Score 1-10) | **8.59** | 8.49 | 8.37 | 8.87 |
|
| 410 |
+
|
|
|
|
| 411 |
- 以上评测结果基于 [OpenCompass](https://github.com/internLM/OpenCompass/) 获得(部分数据标注`*`代表使用深度思考模式进行评测)，具体测试细节可参见 [OpenCompass](https://github.com/internLM/OpenCompass/) 中提供的配置文件。
|
| 412 |
- 评测数据会因 [OpenCompass](https://github.com/internLM/OpenCompass/) 的版本迭代而存在数值差异，请以 [OpenCompass](https://github.com/internLM/OpenCompass/) 最新版的评测结果为主。
|
| 413 |
|
|
|
|
| 435 |
model_dir = "internlm/internlm3-8b-instruct"
|
| 436 |
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
| 437 |
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
|
| 438 |
+
model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.float16)
|
| 439 |
# (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
|
| 440 |
# InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
|
| 441 |
# pip install -U bitsandbytes
|
|
|
|
| 450 |
{"role": "system", "content": system_prompt},
|
| 451 |
{"role": "user", "content": "Please tell me five scenic spots in Shanghai"},
|
| 452 |
]
|
| 453 |
+
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
|
| 454 |
|
| 455 |
generated_ids = model.generate(tokenized_chat, max_new_tokens=1024, temperature=1, repetition_penalty=1.005, top_k=40, top_p=0.8)
|
| 456 |
|
|
|
|
| 459 |
]
|
| 460 |
prompt = tokenizer.batch_decode(tokenized_chat)[0]
|
| 461 |
print(prompt)
|
| 462 |
+
response = tokenizer.batch_decode(generated_ids)[0]
|
| 463 |
print(response)
|
| 464 |
```
|
| 465 |
|
|
|
|
| 507 |
|
| 508 |
##### Ollama 推理
|
| 509 |
|
| 510 |
+
TODO
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
| 511 |
|
| 512 |
##### vLLM 推理
|
| 513 |
|
| 514 |
+
我们还在推动PR(https://github.com/vllm-project/vllm/pull/12037) 合入vllm，现在请使用以下PR链接手动安装
|
| 515 |
|
| 516 |
+
```python
|
| 517 |
+
git clone https://github.com/RunningLeon/vllm.git
|
| 518 |
+
pip install -e .
|
| 519 |
```
|
| 520 |
|
| 521 |
推理代码
|
|
|
|
| 616 |
model_dir = "internlm/internlm3-8b-instruct"
|
| 617 |
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
| 618 |
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
|
| 619 |
+
model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.float16)
|
| 620 |
# (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
|
| 621 |
# InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
|
| 622 |
# pip install -U bitsandbytes
|
|
|
|
| 628 |
{"role": "system", "content": thinking_system_prompt},
|
| 629 |
{"role": "user", "content": "已知函数\(f(x)=\mathrm{e}^{x}-ax - a^{3}\)。\n（1）当\(a = 1\)时，求曲线\(y = f(x)\)在点\((1,f(1))\)处的切线方程；\n（2）若\(f(x)\)有极小值，且极小值小于\(0\)，求\(a\)的取值范围。"},
|
| 630 |
]
|
| 631 |
+
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
|
| 632 |
|
| 633 |
generated_ids = model.generate(tokenized_chat, max_new_tokens=8192)
|
| 634 |
|
|
|
|
| 637 |
]
|
| 638 |
prompt = tokenizer.batch_decode(tokenized_chat)[0]
|
| 639 |
print(prompt)
|
| 640 |
+
response = tokenizer.batch_decode(generated_ids)[0]
|
| 641 |
print(response)
|
| 642 |
```
|
| 643 |
##### LMDeploy 推理
|
|
|
|
| 667 |
|
| 668 |
##### Ollama 推理
|
| 669 |
|
| 670 |
+
TODO
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
| 671 |
|
| 672 |
##### vLLM 推理
|
| 673 |
|
| 674 |
+
我们还在推动PR(https://github.com/vllm-project/vllm/pull/12037) 合入vllm，现在请使用以下PR链接手动安装
|
| 675 |
|
| 676 |
+
```python
|
| 677 |
+
git clone https://github.com/RunningLeon/vllm.git
|
| 678 |
+
pip install -e .
|
| 679 |
```
|
| 680 |
|
| 681 |
推理代码
|
|
|
|
| 725 |
archivePrefix={arXiv},
|
| 726 |
primaryClass={cs.CL}
|
| 727 |
}
|
| 728 |
+
```
|
model-00001-of-00002.safetensors → model-00001-of-00004.safetensors
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f8a1b8c2aecbe72356241a5b5e861ba029f4e61189c4c0a9ca9821e66679f6f5
|
| 3 |
+
size 9999626944
|
model-00002-of-00002.safetensors → model-00002-of-00004.safetensors
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7eefc1671b07fe3aefb5011f381eb4524c27595cab06e56fbeac256ebe24b18d
|
| 3 |
+
size 9857121648
|
model-00003-of-00004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:536775814f5fd4327c11ae5dcdab5f537ab733aae90df9ef425ea06984802fe5
|
| 3 |
+
size 9857121632
|
model-00004-of-00004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e6b47cfd723371ff07d585cc48355c16f15b03a48281f7933ecf339a299d64b3
|
| 3 |
+
size 5503145608
|
model.safetensors.index.json
CHANGED
|
@@ -1,442 +1,442 @@
|
|
| 1 |
{
|
| 2 |
"metadata": {
|
| 3 |
-
"total_size":
|
| 4 |
},
|
| 5 |
"weight_map": {
|
| 6 |
-
"lm_head.weight": "model-
|
| 7 |
-
"model.embed_tokens.weight": "model-00001-of-
|
| 8 |
-
"model.layers.0.input_layernorm.weight": "model-00001-of-
|
| 9 |
-
"model.layers.0.mlp.down_proj.weight": "model-00001-of-
|
| 10 |
-
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-
|
| 11 |
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| 411 |
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| 412 |
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|
| 413 |
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| 419 |
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| 420 |
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| 421 |
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| 422 |
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| 430 |
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| 431 |
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| 432 |
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| 433 |
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| 434 |
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|
| 435 |
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|
| 436 |
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|
| 437 |
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|
| 438 |
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|
| 439 |
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|
| 440 |
+
"model.norm.weight": "model-00004-of-00004.safetensors"
|
| 441 |
}
|
| 442 |
}
|
modeling_internlm3.py
CHANGED
|
@@ -793,7 +793,7 @@ class InternLM3Model(InternLM3PreTrainedModel):
|
|
| 793 |
Args:
|
| 794 |
config: InternLM3Config
|
| 795 |
"""
|
| 796 |
-
|
| 797 |
def __init__(self, config: InternLM3Config):
|
| 798 |
super().__init__(config)
|
| 799 |
self.padding_idx = config.pad_token_id
|
|
@@ -1070,7 +1070,6 @@ class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
|
|
| 1070 |
|
| 1071 |
|
| 1072 |
class InternLM3ForCausalLM(InternLM3PreTrainedModel, GenerationMixin):
|
| 1073 |
-
_auto_class = "AutoModelForCausalLM"
|
| 1074 |
_tied_weights_keys = ["lm_head.weight"]
|
| 1075 |
_tp_plan = {"lm_head": "colwise_rep"}
|
| 1076 |
|
|
|
|
| 793 |
Args:
|
| 794 |
config: InternLM3Config
|
| 795 |
"""
|
| 796 |
+
|
| 797 |
def __init__(self, config: InternLM3Config):
|
| 798 |
super().__init__(config)
|
| 799 |
self.padding_idx = config.pad_token_id
|
|
|
|
| 1070 |
|
| 1071 |
|
| 1072 |
class InternLM3ForCausalLM(InternLM3PreTrainedModel, GenerationMixin):
|
|
|
|
| 1073 |
_tied_weights_keys = ["lm_head.weight"]
|
| 1074 |
_tp_plan = {"lm_head": "colwise_rep"}
|
| 1075 |
|
tokenization_internlm3.py
CHANGED
|
@@ -67,7 +67,7 @@ class InternLM3Tokenizer(PreTrainedTokenizer):
|
|
| 67 |
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
|
| 68 |
other word. Again, this should be set with `from_slow=True` to make sure it's taken into account.
|
| 69 |
"""
|
| 70 |
-
|
| 71 |
vocab_files_names = VOCAB_FILES_NAMES
|
| 72 |
model_input_names = ["input_ids", "attention_mask"]
|
| 73 |
|
|
|
|
| 67 |
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
|
| 68 |
other word. Again, this should be set with `from_slow=True` to make sure it's taken into account.
|
| 69 |
"""
|
| 70 |
+
|
| 71 |
vocab_files_names = VOCAB_FILES_NAMES
|
| 72 |
model_input_names = ["input_ids", "attention_mask"]
|
| 73 |
|