Instructions to use lefantom00/Hermes-3-iSMART with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lefantom00/Hermes-3-iSMART with Transformers:

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

pipe = pipeline("text-generation", model="lefantom00/Hermes-3-iSMART")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lefantom00/Hermes-3-iSMART")
model = AutoModelForCausalLM.from_pretrained("lefantom00/Hermes-3-iSMART", device_map="auto")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lefantom00/Hermes-3-iSMART with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lefantom00/Hermes-3-iSMART:F16
# Run inference directly in the terminal:
llama cli -hf lefantom00/Hermes-3-iSMART:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lefantom00/Hermes-3-iSMART:F16
# Run inference directly in the terminal:
llama cli -hf lefantom00/Hermes-3-iSMART:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf lefantom00/Hermes-3-iSMART:F16
# Run inference directly in the terminal:
./llama-cli -hf lefantom00/Hermes-3-iSMART:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf lefantom00/Hermes-3-iSMART:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lefantom00/Hermes-3-iSMART:F16

Use Docker

docker model run hf.co/lefantom00/Hermes-3-iSMART:F16

LM Studio
Jan

vLLM

How to use lefantom00/Hermes-3-iSMART with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lefantom00/Hermes-3-iSMART"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lefantom00/Hermes-3-iSMART",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lefantom00/Hermes-3-iSMART:F16

SGLang

How to use lefantom00/Hermes-3-iSMART 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 "lefantom00/Hermes-3-iSMART" \
    --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": "lefantom00/Hermes-3-iSMART",
		"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 "lefantom00/Hermes-3-iSMART" \
        --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": "lefantom00/Hermes-3-iSMART",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use lefantom00/Hermes-3-iSMART with Ollama:
```
ollama run hf.co/lefantom00/Hermes-3-iSMART:F16
```

Unsloth Studio

How to use lefantom00/Hermes-3-iSMART with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lefantom00/Hermes-3-iSMART to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lefantom00/Hermes-3-iSMART to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lefantom00/Hermes-3-iSMART to start chatting

Docker Model Runner
How to use lefantom00/Hermes-3-iSMART with Docker Model Runner:
```
docker model run hf.co/lefantom00/Hermes-3-iSMART:F16
```

Lemonade

How to use lefantom00/Hermes-3-iSMART with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lefantom00/Hermes-3-iSMART:F16

Run and chat with the model

lemonade run user.Hermes-3-iSMART-F16

List all available models

lemonade list

Atomic Chat
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

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Safetensors

Model size

8B params

Tensor type

F16

Model tree for lefantom00/Hermes-3-iSMART

Quantizations

1 model