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: 4,767 Bytes

412a372

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  "model": "LiquidVamp-R4-1.7B",
  "author": "Wamphyre AI",
  "base_model": "orcarouter/LFM3-1.7B-abliterated",
  "teacher": "GLM-5.3-Flash",
  "modifications": [
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    "Supervised LoRA post-training, merged into weights"
  ],
  "training": {
    "teacher_unique_examples": 2163,
    "train_unique_examples": 1362,
    "validation_examples": 413,
    "heldout_examples": 388,
    "balanced_epoch_rows": 1611,
    "trainable_lora_parameters": 9999872,
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    "precision": "float32",
    "hardware": "NVIDIA Tesla T4",
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    "context": 8192,
    "temperature": 0,
    "top_k": 1,
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    "thinking": false,
    "cases_per_model": 436,
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        "passed": 357,
        "total": 388
      },
      "heldout_python": {
        "passed": 44,
        "total": 58
      },
      "heldout_reasoning": {
        "passed": 112,
        "total": 129
      },
      "heldout_simulated_tools": {
        "passed": 201,
        "total": 201
      },
      "historical_regressions": {
        "passed": 12,
        "total": 16
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    },
    "generation_retries": 0,
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      "Fresh transfer score is 13/32",
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    ],
    "comparison_baseline": {
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    "numeric_punctuation_grader_corrected": true,
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      "upstream_stock_model": false,
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      "heldout_python": {
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      "heldout_reasoning": {
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      },
      "historical_regressions": {
        "passed": 11,
        "total": 16
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      "fresh_transfer": {
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        "total": 32
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        "passed": 343,
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    },
    "upstream_base_comparison": {
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      "scores": null
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    "paired_improvements_vs_modified_R3": 56,
    "paired_regressions_vs_modified_R3": 17
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  "weights": {
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    "checkpoint": "Full merged model; supports further training without the original predecessor",
    "architecture": "lfm3",
    "quantization": "BF16 GGUF with inherited F32 tensors preserved"
  },
  "deployment": {
    "abliterated_by_lineage": true,
    "abliterated_upstream": "orcarouter/LFM3-1.7B-abliterated",
    "context_window_tokens": 131072,
    "context_window_scope": "Shared input, history, optional reasoning and output",
    "configured_output_budget_tokens": 16384,
    "architecture_max_position_setting": 262144,
    "backend": "llama.cpp b12179",
    "build": "669204ee2",
    "acceleration": "Vulkan/RADV",
    "os": "Ubuntu",
    "gpu": "AMD Radeon RX 6600",
    "gpu_memory_gb": 8,
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    "speculative_decoder": "ngram-mod",
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    "ngram_min_proposal_tokens": 8,
    "ngram_max_proposal_tokens": 16,
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    "parallel_slots": 1,
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    "full_128K_quality_benchmark": false
  }
}