Instructions to use infohound/cogito-14b-voice-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use infohound/cogito-14b-voice-GGUF 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 infohound/cogito-14b-voice-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf infohound/cogito-14b-voice-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf infohound/cogito-14b-voice-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf infohound/cogito-14b-voice-GGUF: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 infohound/cogito-14b-voice-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf infohound/cogito-14b-voice-GGUF: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 infohound/cogito-14b-voice-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf infohound/cogito-14b-voice-GGUF:Q4_K_M

Use Docker

docker model run hf.co/infohound/cogito-14b-voice-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use infohound/cogito-14b-voice-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "infohound/cogito-14b-voice-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "infohound/cogito-14b-voice-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/infohound/cogito-14b-voice-GGUF:Q4_K_M

Ollama
How to use infohound/cogito-14b-voice-GGUF with Ollama:
```
ollama run hf.co/infohound/cogito-14b-voice-GGUF:Q4_K_M
```
Unsloth Desktop

How to use infohound/cogito-14b-voice-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf infohound/cogito-14b-voice-GGUF: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": "infohound/cogito-14b-voice-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use infohound/cogito-14b-voice-GGUF with Docker Model Runner:
```
docker model run hf.co/infohound/cogito-14b-voice-GGUF:Q4_K_M
```

Lemonade

How to use infohound/cogito-14b-voice-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull infohound/cogito-14b-voice-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.cogito-14b-voice-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use infohound/cogito-14b-voice-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf infohound/cogito-14b-voice-GGUF: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 infohound/cogito-14b-voice-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use infohound/cogito-14b-voice-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf infohound/cogito-14b-voice-GGUF: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 "infohound/cogito-14b-voice-GGUF: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"

infohound commited on Mar 30

Commit

75f2a48

verified ·

1 Parent(s): 6367606

Update recommendations: IQ4_XS is minimum for production, IQ3_XS too aggressive for 50+ tools

Browse files

Files changed (1) hide show

README.md +6 -4

README.md CHANGED Viewed

@@ -34,8 +34,8 @@ The result: **smaller models that maintain tool-calling accuracy** compared to g
 | Filename | Quant | Size | BPW | Tool Accuracy* | Avg Latency** | Use Case |
 |----------|-------|------|-----|---------------|--------------|----------|
-| `cogito-14b-voice-IQ3_XS.gguf` | IQ3_XS | 6.0 GB | 3.3 | 80% (16/20) | 1.1s | **Best size/speed tradeoff** |
-| `cogito-14b-voice-IQ4_XS.gguf` | IQ4_XS | 7.6 GB | 4.25 | 80% (16/20) | 1.3s | Conservative, same accuracy |
 | `cogito-14b-voice-Q3_K_M.gguf` | Q3_K_M | 6.9 GB | ~3.7 | 70% (14/20) | 1.3s | Traditional quant format |
 | `cogito-14b-voice-Q4_K_M.gguf` | Q4_K_M | 8.4 GB | ~4.6 | — | — | Reference (with imatrix) |
 | `cogito-14b-voice-Q4_K_S.gguf` | Q4_K_S | 8.0 GB | ~4.4 | — | — | Reference (with imatrix) |
@@ -44,9 +44,11 @@ The result: **smaller models that maintain tool-calling accuracy** compared to g
 \* Tool accuracy measured with OpenAI-compatible tools API, 20 voice commands, 54 exposed entities, strict matching.
 \** Average latency on NVIDIA Blackwell (DGX Spark GB10). RTX 4080 SUPER latency will differ.
-### Recommended: IQ3_XS (6.0 GB)
-The IQ3_XS quantization is the sweet spot — **33% smaller than a standard Q4_K_M** (9.0 GB → 6.0 GB) while maintaining 80% strict tool-calling accuracy (95%+ functionally correct). It's the fastest of all variants and fits easily in 8 GB VRAM.
 ## Why cogito:14b for Voice

 | Filename | Quant | Size | BPW | Tool Accuracy* | Avg Latency** | Use Case |
 |----------|-------|------|-----|---------------|--------------|----------|
+| `cogito-14b-voice-IQ3_XS.gguf` | IQ3_XS | 6.0 GB | 3.3 | 80% (16/20) | 1.1s | Too aggressive for 50+ tools |
+| `cogito-14b-voice-IQ4_XS.gguf` | IQ4_XS | 7.6 GB | 4.25 | 80% (16/20) | 1.3s | **Recommended for voice** |
 | `cogito-14b-voice-Q3_K_M.gguf` | Q3_K_M | 6.9 GB | ~3.7 | 70% (14/20) | 1.3s | Traditional quant format |
 | `cogito-14b-voice-Q4_K_M.gguf` | Q4_K_M | 8.4 GB | ~4.6 | — | — | Reference (with imatrix) |
 | `cogito-14b-voice-Q4_K_S.gguf` | Q4_K_S | 8.0 GB | ~4.4 | — | — | Reference (with imatrix) |
 \* Tool accuracy measured with OpenAI-compatible tools API, 20 voice commands, 54 exposed entities, strict matching.
 \** Average latency on NVIDIA Blackwell (DGX Spark GB10). RTX 4080 SUPER latency will differ.
+### Recommended: IQ4_XS (7.6 GB)
+The IQ4_XS quantization is the recommended choice — **16% smaller than a standard Q4_K_M** (9.0 GB → 7.6 GB) while maintaining reliable tool-calling accuracy in production with 50+ concurrent tool definitions.
+> **Important:** IQ3_XS (6.0 GB) passes synthetic benchmarks but is **too aggressive for production** use with 50+ exposed entities. In real-world Home Assistant testing, IQ3_XS produced wrong tool calls (e.g., calling HassVacuumStart instead of HassTurnOff) and empty responses. Use IQ4_XS or higher for reliable voice assistant operation.
 ## Why cogito:14b for Voice