Instructions to use cpiuk/htech_compliance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use cpiuk/htech_compliance with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use cpiuk/htech_compliance 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 cpiuk/htech_compliance:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cpiuk/htech_compliance:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cpiuk/htech_compliance:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cpiuk/htech_compliance: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 cpiuk/htech_compliance:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cpiuk/htech_compliance: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 cpiuk/htech_compliance:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cpiuk/htech_compliance:Q4_K_M

Use Docker

docker model run hf.co/cpiuk/htech_compliance:Q4_K_M

LM Studio
Jan

vLLM

How to use cpiuk/htech_compliance with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/cpiuk/htech_compliance:Q4_K_M

Ollama
How to use cpiuk/htech_compliance with Ollama:
```
ollama run hf.co/cpiuk/htech_compliance:Q4_K_M
```
Unsloth Desktop

How to use cpiuk/htech_compliance with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cpiuk/htech_compliance: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": "cpiuk/htech_compliance:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use cpiuk/htech_compliance with Docker Model Runner:
```
docker model run hf.co/cpiuk/htech_compliance:Q4_K_M
```

Lemonade

How to use cpiuk/htech_compliance with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cpiuk/htech_compliance:Q4_K_M

Run and chat with the model

lemonade run user.htech_compliance-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use cpiuk/htech_compliance with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cpiuk/htech_compliance: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 cpiuk/htech_compliance:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use cpiuk/htech_compliance with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cpiuk/htech_compliance: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 "cpiuk/htech_compliance: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"

cpiuk commited on Apr 14

Commit

202aaad

verified ·

1 Parent(s): ceb0f4e

Upload runpod/control_server.py with huggingface_hub

Browse files

Files changed (1) hide show

runpod/control_server.py +10 -0

runpod/control_server.py CHANGED Viewed

@@ -143,6 +143,16 @@ def create_app(control_state, api_key: str):
             )
         uploaded = control_state.get_uploaded_files()
         if body.train_file not in uploaded:
             raise HTTPException(
                 status_code=400,

             )
         uploaded = control_state.get_uploaded_files()
+        # Auto-register data files that exist on disk (e.g., pre-downloaded at boot)
+        data_dir = Path(control_state.data_dir)
+        for fname in [body.train_file, body.val_file]:
+            if fname and fname not in uploaded and (data_dir / fname).exists():
+                local_path = str(data_dir / fname)
+                control_state.register_upload(fname, local_path)
+                uploaded[fname] = local_path
+                logger.info(f"Auto-registered pre-existing data file: {fname} → {local_path}")
         if body.train_file not in uploaded:
             raise HTTPException(
                 status_code=400,