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
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1e1d266 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | 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,
)
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