Instructions to use MonetLLM/monet-vd-850M-100BT-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MonetLLM/monet-vd-850M-100BT-hf with Transformers:

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

pipe = pipeline("text-generation", model="MonetLLM/monet-vd-850M-100BT-hf", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MonetLLM/monet-vd-850M-100BT-hf", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MonetLLM/monet-vd-850M-100BT-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MonetLLM/monet-vd-850M-100BT-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MonetLLM/monet-vd-850M-100BT-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MonetLLM/monet-vd-850M-100BT-hf

SGLang

How to use MonetLLM/monet-vd-850M-100BT-hf 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 "MonetLLM/monet-vd-850M-100BT-hf" \
    --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": "MonetLLM/monet-vd-850M-100BT-hf",
		"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 "MonetLLM/monet-vd-850M-100BT-hf" \
        --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": "MonetLLM/monet-vd-850M-100BT-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MonetLLM/monet-vd-850M-100BT-hf with Docker Model Runner:
```
docker model run hf.co/MonetLLM/monet-vd-850M-100BT-hf
```

monet-vd-850M-100BT-hf

File size: 936 Bytes

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{
  "architectures": ["MonetForCausalLM"],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "auto_map": {
    "AutoConfig": "modeling_monet.MonetConfig",
    "AutoModelForCausalLM": "modeling_monet.MonetForCausalLM"
  },
  "bos_token_id": 1,
  "eos_token_id": 2,
  "hidden_act": "relu2",
  "hidden_size": 1536,
  "initializer_range": 0.02,
  "intermediate_size": null,
  "max_position_embeddings": 2048,
  "mlp_bias": null,
  "model_type": "monet",
  "moe_decompose": "vertical",
  "moe_dim": 12,
  "moe_experts": 512,
  "moe_groups": 4,
  "moe_heads": 6,
  "moe_topk": 8,
  "num_attention_heads": 12,
  "num_hidden_layers": 24,
  "num_key_value_heads": 12,
  "output_router_probs": false,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-6,
  "rope_scaling": null,
  "rope_theta": 10000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.42.3",
  "use_cache": true,
  "vocab_size": 32000
}