Instructions to use cpral/Hermes-4-405B-exl3-2bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cpral/Hermes-4-405B-exl3-2bpw with Transformers:

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

pipe = pipeline("text-generation", model="cpral/Hermes-4-405B-exl3-2bpw")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cpral/Hermes-4-405B-exl3-2bpw")
model = AutoModelForCausalLM.from_pretrained("cpral/Hermes-4-405B-exl3-2bpw")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cpral/Hermes-4-405B-exl3-2bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cpral/Hermes-4-405B-exl3-2bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cpral/Hermes-4-405B-exl3-2bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cpral/Hermes-4-405B-exl3-2bpw

SGLang

How to use cpral/Hermes-4-405B-exl3-2bpw 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 "cpral/Hermes-4-405B-exl3-2bpw" \
    --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": "cpral/Hermes-4-405B-exl3-2bpw",
		"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 "cpral/Hermes-4-405B-exl3-2bpw" \
        --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": "cpral/Hermes-4-405B-exl3-2bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cpral/Hermes-4-405B-exl3-2bpw with Docker Model Runner:
```
docker model run hf.co/cpral/Hermes-4-405B-exl3-2bpw
```

Hermes-4-405B-exl3-2bpw

File size: 1,206 Bytes

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{
    "architectures": [
        "LlamaForCausalLM"
    ],
    "attention_bias": false,
    "attention_dropout": 0.0,
    "bos_token_id": 128000,
    "eos_token_id": 128001,
    "head_dim": 128,
    "hidden_act": "silu",
    "hidden_size": 16384,
    "initializer_range": 0.02,
    "intermediate_size": 53248,
    "max_position_embeddings": 131072,
    "mlp_bias": false,
    "model_type": "llama",
    "num_attention_heads": 128,
    "num_hidden_layers": 126,
    "num_key_value_heads": 8,
    "pretraining_tp": 1,
    "rms_norm_eps": 1e-05,
    "rope_scaling": {
        "factor": 8.0,
        "high_freq_factor": 4.0,
        "low_freq_factor": 1.0,
        "original_max_position_embeddings": 8192,
        "rope_type": "llama3"
    },
    "rope_theta": 500000.0,
    "tie_word_embeddings": false,
    "torch_dtype": "bfloat16",
    "transformers_version": "4.52.4",
    "use_cache": true,
    "vocab_size": 128256,
    "quantization_config": {
        "quant_method": "exl3",
        "version": "0.0.28",
        "bits": 2.0,
        "head_bits": 8,
        "calibration": {
            "rows": 250,
            "cols": 2048
        },
        "out_scales": "always",
        "codebook": "mcg"
    }
}