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

Libraries

How to use internlm/internlm-chat-20b with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("internlm/internlm-chat-20b", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use internlm/internlm-chat-20b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/internlm-chat-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/internlm-chat-20b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/internlm/internlm-chat-20b

SGLang

How to use internlm/internlm-chat-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/internlm-chat-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/internlm-chat-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/internlm-chat-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/internlm-chat-20b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use internlm/internlm-chat-20b with Docker Model Runner:
```
docker model run hf.co/internlm/internlm-chat-20b
```

x54-729 commited on Sep 20, 2023

Commit

d85414b

1 Parent(s): 98802ac

Update README.md

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README.md +4 -4

README.md CHANGED Viewed

@@ -76,8 +76,8 @@ Overall, InternLM-20B comprehensively outperforms open-source models in the 13B
 To load the InternLM 7B Chat model using Transformers, use the following code:
 ```python
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM
->>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-20b-chat", trust_remote_code=True)
->>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-20b-chat", trust_remote_code=True).cuda()
 >>> model = model.eval()
 >>> inputs = tokenizer(["Coming to the beautiful nature, we found"], return_tensors="pt")
 >>> for k,v in inputs.items():
@@ -146,8 +146,8 @@ InternLM 20B 在模型结构上选择了深结构，层数设定为60层，超
 通过以下的代码加载 InternLM 20B 模型
 ```python
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM
->>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-20b-chat", trust_remote_code=True)
->>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-20b-chat", trust_remote_code=True).cuda()
 >>> model = model.eval()
 >>> inputs = tokenizer(["来到美丽的大自然，我们发现"], return_tensors="pt")
 >>> for k,v in inputs.items():

 To load the InternLM 7B Chat model using Transformers, use the following code:
 ```python
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+>>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-20b", trust_remote_code=True)
+>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-20b", trust_remote_code=True).cuda()
 >>> model = model.eval()
 >>> inputs = tokenizer(["Coming to the beautiful nature, we found"], return_tensors="pt")
 >>> for k,v in inputs.items():
 通过以下的代码加载 InternLM 20B 模型
 ```python
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+>>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-20b", trust_remote_code=True)
+>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-20b", trust_remote_code=True).cuda()
 >>> model = model.eval()
 >>> inputs = tokenizer(["来到美丽的大自然，我们发现"], return_tensors="pt")
 >>> for k,v in inputs.items():