Instructions to use internlm/internlm-chat-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/internlm-chat-7b with Transformers:

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

pipe = pipeline("text-generation", model="internlm/internlm-chat-7b", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use internlm/internlm-chat-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/internlm-chat-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/internlm-chat-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/internlm/internlm-chat-7b

SGLang

How to use internlm/internlm-chat-7b 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/internlm-chat-7b" \
    --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/internlm-chat-7b",
		"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/internlm-chat-7b" \
        --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/internlm-chat-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use internlm/internlm-chat-7b with Docker Model Runner:
```
docker model run hf.co/internlm/internlm-chat-7b
```

x54-729 commited on Jul 3, 2024

Commit

4e2d2e1

1 Parent(s): 110c998

update opencompass url

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -35,7 +35,7 @@ InternLM has open-sourced a 7 billion parameter base model and a chat model tail
 ### Performance Evaluation
-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/leaderboard-llm) for more evaluation results.
 | Datasets\Models           |  **InternLM-Chat-7B** |  **InternLM-7B**  |  LLaMA-7B | Baichuan-7B | ChatGLM2-6B | Alpaca-7B | Vicuna-7B |
 | -------------------- | --------------------- | ---------------- | --------- |  --------- | ------------ | --------- | ---------- |
@@ -110,7 +110,7 @@ InternLM ，即书生·浦语大模型，包含面向实用场景的70亿参数
 ### 性能评测
-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/leaderboard-llm)获取更多的评测结果。
 | 数据集\模型           |  **InternLM-Chat-7B** |  **InternLM-7B**  |  LLaMA-7B | Baichuan-7B | ChatGLM2-6B | Alpaca-7B | Vicuna-7B |
 | -------------------- | --------------------- | ---------------- | --------- |  --------- | ------------ | --------- | ---------- |

 ### Performance Evaluation
+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.
 | Datasets\Models           |  **InternLM-Chat-7B** |  **InternLM-7B**  |  LLaMA-7B | Baichuan-7B | ChatGLM2-6B | Alpaca-7B | Vicuna-7B |
 | -------------------- | --------------------- | ---------------- | --------- |  --------- | ------------ | --------- | ---------- |
 ### 性能评测
+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。
 | 数据集\模型           |  **InternLM-Chat-7B** |  **InternLM-7B**  |  LLaMA-7B | Baichuan-7B | ChatGLM2-6B | Alpaca-7B | Vicuna-7B |
 | -------------------- | --------------------- | ---------------- | --------- |  --------- | ------------ | --------- | ---------- |