Instructions to use internlm/internlm2-wqx-20b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/internlm2-wqx-20b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="internlm/internlm2-wqx-20b", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-wqx-20b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use internlm/internlm2-wqx-20b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/internlm2-wqx-20b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/internlm2-wqx-20b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/internlm/internlm2-wqx-20b

SGLang

How to use internlm/internlm2-wqx-20b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "internlm/internlm2-wqx-20b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/internlm2-wqx-20b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "internlm/internlm2-wqx-20b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/internlm2-wqx-20b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use internlm/internlm2-wqx-20b with Docker Model Runner:
```
docker model run hf.co/internlm/internlm2-wqx-20b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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Check out the documentation for more information.

InternLM2-WQX-20B

InternLM2-WQX ^HOT

InternLM2-WQX-20B 🤗 ｜ InternLM2-WQX-VL-20B 🤗

Introduction

InternLM2-WQX与InternLM2-WQX-VL是InternLM团队于2024年高考前夕最新推出的文曲星系列模型。

高考覆盖各类学科及题型，同时因其开考前的“绝密性”，被视作中国最具权威的考试之一，成为评估考生综合能力的“试金石”。这一面向人类设计的高难度综合性测试，目前普遍被研究者用于考察大模型的智能水平。InternLM2-WQX系列模型在2024年高考评测集GAOKAO-Eval上取得了优异的成绩，综合表现与GPT-4o相当，且超越了国内外一系列开源大模型，体现了InternLM2-WQX系列模型优秀的性能。

我们即将更新关于文曲星系列模型数据准备的相关说明，敬请期待。

MD5 Check

md5sum ./*
5209adfd6ef7d1724848ff0372362568  ./model-00001-of-00004.safetensors
e37ee2eafecfed543d10dca75998204e  ./model-00002-of-00004.safetensors
ea3da8035b0c2a31c369dd463adf9b52  ./model-00003-of-00004.safetensors
f1ff218f801c69fd4c12c534b64e1b60  ./model-00004-of-00004.safetensors

Citation

@misc{2024internlm2wqx,
    title={https://github.com/InternLM/InternLM-WQX},
    author={InternLM Team},
    howpublished = {\url{https://github.com/InternLM/InternLM-WQX}},
    year={2024}
}

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