Instructions to use void0x14/echo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use void0x14/echo 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 void0x14/echo:Q4_K_M # Run inference directly in the terminal: llama cli -hf void0x14/echo:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf void0x14/echo:Q4_K_M # Run inference directly in the terminal: llama cli -hf void0x14/echo:Q4_K_M
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 void0x14/echo:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf void0x14/echo:Q4_K_M
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 void0x14/echo:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf void0x14/echo:Q4_K_M
Use Docker
docker model run hf.co/void0x14/echo:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use void0x14/echo with Ollama:
ollama run hf.co/void0x14/echo:Q4_K_M
- Unsloth Desktop
- Pi
How to use void0x14/echo with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf void0x14/echo:Q4_K_M
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": "void0x14/echo:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use void0x14/echo with Docker Model Runner:
docker model run hf.co/void0x14/echo:Q4_K_M
- Lemonade
How to use void0x14/echo with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull void0x14/echo:Q4_K_M
Run and chat with the model
lemonade run user.echo-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use void0x14/echo with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf void0x14/echo:Q4_K_M
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 void0x14/echo:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use void0x14/echo with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf void0x14/echo:Q4_K_M
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 "void0x14/echo:Q4_K_M" \ --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"
void0x14 commited on
fix: remove mtp fields from text config
Browse files
MVP/qwen35_prune.py
CHANGED
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@@ -154,6 +154,8 @@ def build_text_config(full_config: dict, layer_count: int) -> dict:
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source["architectures"] = ["Qwen3_5ForCausalLM"]
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source.pop("vision_config", None)
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source.pop("mtp_config", None)
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return source
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source["architectures"] = ["Qwen3_5ForCausalLM"]
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source.pop("vision_config", None)
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source.pop("mtp_config", None)
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source.pop("mtp_num_hidden_layers", None)
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source.pop("mtp_use_dedicated_embeddings", None)
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return source
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MVP/tests/test_pruning_contract.py
CHANGED
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@@ -83,6 +83,8 @@ def test_text_config_is_standalone_and_keeps_live_attention_fields():
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"linear_value_head_dim": 128,
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"linear_conv_kernel_dim": 4,
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"vocab_size": 248320,
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},
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}
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@@ -94,6 +96,7 @@ def test_text_config_is_standalone_and_keeps_live_attention_fields():
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assert text["linear_num_value_heads"] == 16
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assert text["tie_word_embeddings"] is True
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assert "vision_config" not in text
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def test_validator_rejects_vision_mtp_and_duplicate_tied_head():
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"linear_value_head_dim": 128,
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"linear_conv_kernel_dim": 4,
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"vocab_size": 248320,
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"mtp_num_hidden_layers": 1,
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"mtp_use_dedicated_embeddings": False,
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},
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}
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assert text["linear_num_value_heads"] == 16
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assert text["tie_word_embeddings"] is True
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assert "vision_config" not in text
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assert not any(key.startswith("mtp_") for key in text)
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def test_validator_rejects_vision_mtp_and_duplicate_tied_head():
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