Instructions to use zai-org/cogvlm2-llama3-chat-19B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/cogvlm2-llama3-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-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-chat-19B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use zai-org/cogvlm2-llama3-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-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-chat-19B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/zai-org/cogvlm2-llama3-chat-19B
- SGLang
How to use zai-org/cogvlm2-llama3-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-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-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-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-chat-19B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use zai-org/cogvlm2-llama3-chat-19B with Docker Model Runner:
docker model run hf.co/zai-org/cogvlm2-llama3-chat-19B
| from typing import Literal | |
| from transformers import PretrainedConfig | |
| class CogVLMConfig(PretrainedConfig): | |
| _auto_class = "AutoConfig" | |
| def __init__( | |
| self, | |
| vocab_size=128256, | |
| hidden_size=4096, | |
| intermediate_size=14336, | |
| num_hidden_layers=32, | |
| num_attention_heads=32, | |
| num_multi_query_heads=8, | |
| hidden_act='silu', | |
| max_position_embeddings=8192, | |
| initializer_range=0.02, | |
| rms_norm_eps=1e-05, | |
| template_version: Literal["base", "chat"] = "chat", | |
| bos_token_id=128000, | |
| eos_token_id=128001, | |
| tie_word_embeddings=False, | |
| use_cache=True, | |
| **kwargs, | |
| ): | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_attention_heads = num_attention_heads | |
| self.num_multi_query_heads = num_multi_query_heads | |
| self.max_position_embeddings = max_position_embeddings | |
| self.rms_norm_eps = rms_norm_eps | |
| self.initializer_range = initializer_range | |
| self.vocab_size = vocab_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.hidden_act = hidden_act | |
| self.template_version = template_version | |
| self.use_cache = use_cache | |
| super().__init__( | |
| bos_token_id=bos_token_id, | |
| eos_token_id=eos_token_id, | |
| tie_word_embeddings=tie_word_embeddings, | |
| **kwargs, | |
| ) | |