Instructions to use frontterrain/HARP-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use frontterrain/HARP-1B 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 frontterrain/HARP-1B:F16
# Run inference directly in the terminal:
llama cli -hf frontterrain/HARP-1B:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf frontterrain/HARP-1B:F16
# Run inference directly in the terminal:
llama cli -hf frontterrain/HARP-1B:F16

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 frontterrain/HARP-1B:F16
# Run inference directly in the terminal:
./llama-cli -hf frontterrain/HARP-1B:F16

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 frontterrain/HARP-1B:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf frontterrain/HARP-1B:F16

Use Docker

docker model run hf.co/frontterrain/HARP-1B:F16

Llama new
LM Studio
Jan

vLLM

How to use frontterrain/HARP-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "frontterrain/HARP-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "frontterrain/HARP-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/frontterrain/HARP-1B:F16

Ollama
How to use frontterrain/HARP-1B with Ollama:
```
ollama run hf.co/frontterrain/HARP-1B:F16
```
Unsloth Desktop
Docker Model Runner
How to use frontterrain/HARP-1B with Docker Model Runner:
```
docker model run hf.co/frontterrain/HARP-1B:F16
```

Lemonade

How to use frontterrain/HARP-1B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull frontterrain/HARP-1B:F16

Run and chat with the model

lemonade run user.HARP-1B-F16

List all available models

lemonade list

Atomic Chat

HARP-1B / tokenizer_config.json

frontterrain

Upload HARP-1B Pre-trained weights (Step 52,000, 1,400,832,000 tokens trained)

88086d4 verified about 15 hours ago

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508 Bytes

{
  "tokenizer_class": "BPETokenizer",
  "vocab_size": 32000,
  "model_max_length": 512,
  "padding_side": "right",
  "special_tokens": {
    "<|pad|>": 0,
    "<|unk|>": 1,
    "<|im_start|>": 2,
    "<|im_end|>": 3,
    "<|system|>": 4,
    "<|user|>": 5,
    "<|assistant|>": 6
  },
  "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
}

	{
	"tokenizer_class": "BPETokenizer",
	"vocab_size": 32000,
	"model_max_length": 512,
	"padding_side": "right",
	"special_tokens": {
	"<\|pad\|>": 0,
	"<\|unk\|>": 1,
	"<\|im_start\|>": 2,
	"<\|im_end\|>": 3,
	"<\|system\|>": 4,
	"<\|user\|>": 5,
	"<\|assistant\|>": 6
	},
	"chat_template": "{% for message in messages %}{{'<\|im_start\|>' + message['role'] + '\n' + message['content'] + '<\|im_end\|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<\|im_start\|>assistant\n' }}{% endif %}"
	}