Instructions to use OpenModel-Labs/Comet-1-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenModel-Labs/Comet-1-Preview with Transformers:

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

pipe = pipeline("text-generation", model="OpenModel-Labs/Comet-1-Preview", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OpenModel-Labs/Comet-1-Preview", trust_remote_code=True, device_map="auto")

COMET

How to use OpenModel-Labs/Comet-1-Preview with COMET:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenModel-Labs/Comet-1-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenModel-Labs/Comet-1-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenModel-Labs/Comet-1-Preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenModel-Labs/Comet-1-Preview

SGLang

How to use OpenModel-Labs/Comet-1-Preview 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 "OpenModel-Labs/Comet-1-Preview" \
    --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": "OpenModel-Labs/Comet-1-Preview",
		"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 "OpenModel-Labs/Comet-1-Preview" \
        --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": "OpenModel-Labs/Comet-1-Preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenModel-Labs/Comet-1-Preview with Docker Model Runner:
```
docker model run hf.co/OpenModel-Labs/Comet-1-Preview
```

Comet-1-Preview / config.json

Bc-AI

Add Transformers support (modeling + config code), tokenizer/generation configs and model card

9df61c9 verified 1 day ago

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982 Bytes

{
  "architectures": [
    "CometForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "configuration_comet.CometConfig",
    "AutoModel": "modeling_comet.CometModel",
    "AutoModelForCausalLM": "modeling_comet.CometForCausalLM"
  },
  "model_type": "comet",
  "variant": "7.0b",
  "vocab_size": 50277,
  "hidden_size": 4096,
  "num_hidden_layers": 36,
  "num_attention_heads": 32,
  "num_key_value_heads": 8,
  "intermediate_size": 12288,
  "max_position_embeddings": 8192,
  "rope_theta": 1000000.0,
  "memory_width": 2048,
  "memory_kernel_size": 7,
  "memory_dilations": [1, 2, 4, 8],
  "layer_pattern": ["global", "global", "global", "recall"],
  "layer_scale_init": 0.5,
  "qk_norm": true,
  "rms_norm_eps": 1e-06,
  "fused_projections": true,
  "dropout": 0.0,
  "initializer_range": 0.02,
  "tie_word_embeddings": true,
  "use_cache": true,
  "bos_token_id": 0,
  "eos_token_id": 0,
  "pad_token_id": 1,
  "tokenizer_class": "PreTrainedTokenizerFast",
  "dtype": "float32"
}

	{
	"architectures": [
	"CometForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration_comet.CometConfig",
	"AutoModel": "modeling_comet.CometModel",
	"AutoModelForCausalLM": "modeling_comet.CometForCausalLM"
	},
	"model_type": "comet",
	"variant": "7.0b",
	"vocab_size": 50277,
	"hidden_size": 4096,
	"num_hidden_layers": 36,
	"num_attention_heads": 32,
	"num_key_value_heads": 8,
	"intermediate_size": 12288,
	"max_position_embeddings": 8192,
	"rope_theta": 1000000.0,
	"memory_width": 2048,
	"memory_kernel_size": 7,
	"memory_dilations": [1, 2, 4, 8],
	"layer_pattern": ["global", "global", "global", "recall"],
	"layer_scale_init": 0.5,
	"qk_norm": true,
	"rms_norm_eps": 1e-06,
	"fused_projections": true,
	"dropout": 0.0,
	"initializer_range": 0.02,
	"tie_word_embeddings": true,
	"use_cache": true,
	"bos_token_id": 0,
	"eos_token_id": 0,
	"pad_token_id": 1,
	"tokenizer_class": "PreTrainedTokenizerFast",
	"dtype": "float32"
	}