Instructions to use WamphyreAI/LiquidVamp-R4-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WamphyreAI/LiquidVamp-R4-1.7B with Transformers:

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

pipe = pipeline("text-generation", model="WamphyreAI/LiquidVamp-R4-1.7B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WamphyreAI/LiquidVamp-R4-1.7B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("WamphyreAI/LiquidVamp-R4-1.7B", trust_remote_code=True, 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use WamphyreAI/LiquidVamp-R4-1.7B 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 WamphyreAI/LiquidVamp-R4-1.7B:BF16
# Run inference directly in the terminal:
llama cli -hf WamphyreAI/LiquidVamp-R4-1.7B:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf WamphyreAI/LiquidVamp-R4-1.7B:BF16
# Run inference directly in the terminal:
llama cli -hf WamphyreAI/LiquidVamp-R4-1.7B:BF16

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf WamphyreAI/LiquidVamp-R4-1.7B:BF16
# Run inference directly in the terminal:
./build/bin/llama cli -hf WamphyreAI/LiquidVamp-R4-1.7B:BF16

Use Docker

docker model run hf.co/WamphyreAI/LiquidVamp-R4-1.7B:BF16

Llama new
LM Studio
Jan

vLLM

How to use WamphyreAI/LiquidVamp-R4-1.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WamphyreAI/LiquidVamp-R4-1.7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WamphyreAI/LiquidVamp-R4-1.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WamphyreAI/LiquidVamp-R4-1.7B:BF16

SGLang

How to use WamphyreAI/LiquidVamp-R4-1.7B 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 "WamphyreAI/LiquidVamp-R4-1.7B" \
    --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": "WamphyreAI/LiquidVamp-R4-1.7B",
		"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 "WamphyreAI/LiquidVamp-R4-1.7B" \
        --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": "WamphyreAI/LiquidVamp-R4-1.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use WamphyreAI/LiquidVamp-R4-1.7B with Ollama:
```
ollama run hf.co/WamphyreAI/LiquidVamp-R4-1.7B:BF16
```
Unsloth Desktop

How to use WamphyreAI/LiquidVamp-R4-1.7B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WamphyreAI/LiquidVamp-R4-1.7B:BF16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "WamphyreAI/LiquidVamp-R4-1.7B:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use WamphyreAI/LiquidVamp-R4-1.7B with Docker Model Runner:
```
docker model run hf.co/WamphyreAI/LiquidVamp-R4-1.7B:BF16
```

Lemonade

How to use WamphyreAI/LiquidVamp-R4-1.7B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WamphyreAI/LiquidVamp-R4-1.7B:BF16

Run and chat with the model

lemonade run user.LiquidVamp-R4-1.7B-BF16

List all available models

lemonade list

Hermes Agent

How to use WamphyreAI/LiquidVamp-R4-1.7B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WamphyreAI/LiquidVamp-R4-1.7B:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default WamphyreAI/LiquidVamp-R4-1.7B:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use WamphyreAI/LiquidVamp-R4-1.7B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WamphyreAI/LiquidVamp-R4-1.7B:BF16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "WamphyreAI/LiquidVamp-R4-1.7B:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

LiquidVamp-R4-1.7B

File size: 1,858 Bytes

412a372

{
  "audio_out_token": "<|audio_out|>",
  "audio_token": "<|audio|>",
  "backend": "tokenizers",
  "bos_token": "<|startoftext|>",
  "eos_token": "<|im_end|>",
  "extra_special_tokens": [],
  "image_token": "<|image|>",
  "is_local": true,
  "local_files_only": true,
  "model_max_length": 1000000000000000019884624838656,
  "model_specific_special_tokens": {
    "audio_out_token": "<|audio_out|>",
    "audio_token": "<|audio|>",
    "image_token": "<|image|>",
    "video_token": "<|video|>",
    "vision_end_token": "<|vision_end|>",
    "vision_start_token": "<|vision_start|>"
  },
  "pad_token": "<|pad|>",
  "response_template": {
    "defaults": {
      "role": "assistant"
    },
    "fields": {
      "content": {
        "close_pattern": "<\\|im_end\\|>\\s*",
        "content": "text"
      },
      "reasoning_content": {
        "close_pattern": "</think>\\s*",
        "content": "text",
        "open_pattern": "<think>\\n?"
      },
      "tool_calls": {
        "close_pattern": "</tool_call>\\s*",
        "content": "xml-inline",
        "content_args": {
          "tag_pattern": "<parameter=(?P<key>[^>\\n]+)>\\n?(?P<value>.*?)\\n?</parameter>(?=\\s*(?:<parameter=|</function>\\s*\\Z|\\Z))",
          "value_parser": {
            "args": {
              "strip": false
            },
            "name": "text"
          }
        },
        "open_pattern": "<tool_call>\\s*<function=(?P<name>[^\\n>]+)>",
        "repeats": true,
        "transform": {
          "function": {
            "arguments": "{content}",
            "name": "{name}"
          },
          "type": "function"
        }
      }
    },
    "start_anchor_pattern": "<\\|im_start\\|>assistant\\n"
  },
  "tokenizer_class": "TokenizersBackend",
  "video_token": "<|video|>",
  "vision_end_token": "<|vision_end|>",
  "vision_start_token": "<|vision_start|>"
}