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 / config.json

frontterrain

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

88086d4 verified about 21 hours ago

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{
  "architectures": [
    "HARP1BCUDAModel"
  ],
  "model_type": "harp",
  "vocab_size": 32000,
  "hidden_size": 768,
  "num_hidden_layers": 12,
  "num_attention_heads": 12,
  "num_key_value_heads": 3,
  "intermediate_size": 2048,
  "head_dim": 64,
  "max_position_embeddings": 512,
  "rms_norm_eps": 1e-06,
  "rope_theta": 10000.0,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.45.0",
  "organization": "FrontTerrain",
  "model_name": "HARP-1B",
  "creator": "Rishi Shandilya (@dar2quasar)",
  "ai_lead": "Arka Brahma",
  "step": 52000,
  "loss": 0.2773,
  "tokens_trained": 1400832000,
  "target_tokens": 50000000000
}

	{
	"architectures": [
	"HARP1BCUDAModel"
	],
	"model_type": "harp",
	"vocab_size": 32000,
	"hidden_size": 768,
	"num_hidden_layers": 12,
	"num_attention_heads": 12,
	"num_key_value_heads": 3,
	"intermediate_size": 2048,
	"head_dim": 64,
	"max_position_embeddings": 512,
	"rms_norm_eps": 1e-06,
	"rope_theta": 10000.0,
	"torch_dtype": "bfloat16",
	"transformers_version": "4.45.0",
	"organization": "FrontTerrain",
	"model_name": "HARP-1B",
	"creator": "Rishi Shandilya (@dar2quasar)",
	"ai_lead": "Arka Brahma",
	"step": 52000,
	"loss": 0.2773,
	"tokens_trained": 1400832000,
	"target_tokens": 50000000000
	}