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"

WamphyreAI commited on 3 days ago

Commit

3dd4fd1

verified ·

1 Parent(s): 412a372

Add LiquidVamp model logo below Wamphyre AI branding

Browse files

Add the dedicated LiquidVamp-R4-1.7B logo below the publisher logo and refresh release checksums. Model weights are unchanged.

Files changed (4) hide show

.gitattributes +1 -0
README.md +2 -0
SHA256SUMS +2 -1
liquidvamp-logo.png +3 -0

.gitattributes CHANGED Viewed

@@ -36,3 +36,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 LiquidVamp-R4-1.7B-BF16.gguf filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
 wamphyre-ai-logo.png filter=lfs diff=lfs merge=lfs -text

 LiquidVamp-R4-1.7B-BF16.gguf filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
 wamphyre-ai-logo.png filter=lfs diff=lfs merge=lfs -text
+liquidvamp-logo.png filter=lfs diff=lfs merge=lfs -text

README.md CHANGED Viewed

@@ -26,6 +26,8 @@ tags:
 ![Wamphyre AI](wamphyre-ai-logo.png)
 # LiquidVamp-R4-1.7B
 **By Wamphyre AI.**

 ![Wamphyre AI](wamphyre-ai-logo.png)
+![LiquidVamp-R4-1.7B](liquidvamp-logo.png)
 # LiquidVamp-R4-1.7B
 **By Wamphyre AI.**

SHA256SUMS CHANGED Viewed

@@ -1,13 +1,14 @@
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liquidvamp-logo.png ADDED Viewed

Git LFS Details

SHA256: 40e726cc74968ec45c8cb100f52ec6141f4a3adfc529d8f5e453287ae1e33dd0
Pointer size: 131 Bytes
Size of remote file: 275 kB