[Feature] Add support for InternLM3 (#1829)

* update

* update

* update

* update
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Linchen Xiao 2025-01-16 14:28:27 +08:00 committed by GitHub
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5 changed files with 32 additions and 1 deletions

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@ -17,7 +17,7 @@ jobs:
python-version: '3.10' python-version: '3.10'
- name: Install pre-commit hook - name: Install pre-commit hook
run: | run: |
pip install pre-commit==3.8.0 mmengine pip install pre-commit==3.8.0 mmengine==0.10.5
pre-commit install pre-commit install
- name: Linting - name: Linting
run: pre-commit run --all-files run: pre-commit run --all-files

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@ -57,6 +57,7 @@ Just like a compass guides us on our journey, OpenCompass will guide you through
## 🚀 What's New <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a> ## 🚀 What's New <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a>
- **\[2025.01.16\]** We now support the [InternLM3-8B-Instruct](https://huggingface.co/internlm/internlm3-8b-instruct) model which has enhanced performance on reasoning and knowledge-intensive tasks.
- **\[2024.12.17\]** We have provided the evaluation script for the December [CompassAcademic](configs/eval_academic_leaderboard_202412.py), which allows users to easily reproduce the official evaluation results by configuring it. - **\[2024.12.17\]** We have provided the evaluation script for the December [CompassAcademic](configs/eval_academic_leaderboard_202412.py), which allows users to easily reproduce the official evaluation results by configuring it.
- **\[2024.11.14\]** OpenCompass now offers support for a sophisticated benchmark designed to evaluate complex reasoning skills — [MuSR](https://arxiv.org/pdf/2310.16049). Check out the [demo](configs/eval_musr.py) and give it a spin! 🔥🔥🔥 - **\[2024.11.14\]** OpenCompass now offers support for a sophisticated benchmark designed to evaluate complex reasoning skills — [MuSR](https://arxiv.org/pdf/2310.16049). Check out the [demo](configs/eval_musr.py) and give it a spin! 🔥🔥🔥
- **\[2024.11.14\]** OpenCompass now supports the brand new long-context language model evaluation benchmark — [BABILong](https://arxiv.org/pdf/2406.10149). Have a look at the [demo](configs/eval_babilong.py) and give it a try! 🔥🔥🔥 - **\[2024.11.14\]** OpenCompass now supports the brand new long-context language model evaluation benchmark — [BABILong](https://arxiv.org/pdf/2406.10149). Have a look at the [demo](configs/eval_babilong.py) and give it a try! 🔥🔥🔥

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@ -57,6 +57,7 @@
## 🚀 最新进展 <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a> ## 🚀 最新进展 <a><img width="35" height="20" src="https://user-images.githubusercontent.com/12782558/212848161-5e783dd6-11e8-4fe0-bbba-39ffb77730be.png"></a>
- **\[2025.01.16\]** 我们现已支持 [InternLM3-8B-Instruct](https://huggingface.co/internlm/internlm3-8b-instruct) 模型,该模型在推理、知识类任务上取得同量级最优性能,欢迎尝试。
- **\[2024.12.17\]** 我们提供了12月CompassAcademic学术榜单评估脚本 [CompassAcademic](configs/eval_academic_leaderboard_202412.py),你可以通过简单地配置复现官方评测结果。 - **\[2024.12.17\]** 我们提供了12月CompassAcademic学术榜单评估脚本 [CompassAcademic](configs/eval_academic_leaderboard_202412.py),你可以通过简单地配置复现官方评测结果。
- **\[2024.10.14\]** 现已支持OpenAI多语言问答数据集[MMMLU](https://huggingface.co/datasets/openai/MMMLU),欢迎尝试! 🔥🔥🔥 - **\[2024.10.14\]** 现已支持OpenAI多语言问答数据集[MMMLU](https://huggingface.co/datasets/openai/MMMLU),欢迎尝试! 🔥🔥🔥
- **\[2024.09.19\]** 现已支持[Qwen2.5](https://huggingface.co/Qwen)(0.5B to 72B) ,可以使用多种推理后端(huggingface/vllm/lmdeploy), 欢迎尝试! 🔥🔥🔥 - **\[2024.09.19\]** 现已支持[Qwen2.5](https://huggingface.co/Qwen)(0.5B to 72B) ,可以使用多种推理后端(huggingface/vllm/lmdeploy), 欢迎尝试! 🔥🔥🔥

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@ -0,0 +1,12 @@
from opencompass.models import HuggingFacewithChatTemplate
models = [
dict(
type=HuggingFacewithChatTemplate,
abbr='internlm3-8b-instruct-hf',
path='internlm/internlm3-8b-instruct',
max_out_len=8192,
batch_size=8,
run_cfg=dict(num_gpus=1),
)
]

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@ -0,0 +1,17 @@
from opencompass.models import TurboMindModelwithChatTemplate
models = [
dict(
type=TurboMindModelwithChatTemplate,
abbr='internlm3-8b-instruct-turbomind',
path='internlm/internlm3-8b-instruct',
engine_config=dict(session_len=32768, max_batch_size=16, tp=1),
gen_config=dict(
top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=8192
),
max_seq_len=32768,
max_out_len=8192,
batch_size=16,
run_cfg=dict(num_gpus=1),
)
]