Instructions to use zai-org/cogvlm2-llama3-chinese-chat-19B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/cogvlm2-llama3-chinese-chat-19B with Transformers:

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

pipe = pipeline("text-generation", model="zai-org/cogvlm2-llama3-chinese-chat-19B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("zai-org/cogvlm2-llama3-chinese-chat-19B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zai-org/cogvlm2-llama3-chinese-chat-19B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/cogvlm2-llama3-chinese-chat-19B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/cogvlm2-llama3-chinese-chat-19B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/cogvlm2-llama3-chinese-chat-19B

SGLang

How to use zai-org/cogvlm2-llama3-chinese-chat-19B 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 "zai-org/cogvlm2-llama3-chinese-chat-19B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/cogvlm2-llama3-chinese-chat-19B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "zai-org/cogvlm2-llama3-chinese-chat-19B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/cogvlm2-llama3-chinese-chat-19B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/cogvlm2-llama3-chinese-chat-19B with Docker Model Runner:
```
docker model run hf.co/zai-org/cogvlm2-llama3-chinese-chat-19B
```

cogvlm2-llama3-chinese-chat-19B

File size: 885 Bytes

19e72b3

{
  "architectures": [
    "CogVLMForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "configuration_cogvlm.CogVLMConfig",
    "AutoModelForCausalLM": "modeling_cogvlm.CogVLMForCausalLM"
  },
  "vision_config": {
    "dropout_prob": 0.0,
    "hidden_act": "gelu",
    "in_channels": 3,
    "num_hidden_layers": 63,
    "hidden_size": 1792,
    "patch_size": 14,
    "num_heads": 16,
    "intermediate_size": 15360,
    "layer_norm_eps": 1e-06,
    "num_positions": 9217,
    "image_size": 1344
  },
  "hidden_size": 4096,
  "intermediate_size": 14336,
  "num_attention_heads": 32,
  "max_position_embeddings": 8192,
  "rms_norm_eps": 1e-05,
  "template_version": "chat",
  "initializer_range": 0.02,
  "bos_token_id": 128000,
  "eos_token_id": [128001, 128009],
  "pad_token_id": 128002,
  "vocab_size": 128256,
  "num_hidden_layers": 32,
  "hidden_act": "silu",
  "use_cache": true
}