Instructions to use HelioAI/LFM3-1.7B-inference-modules-experiments with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use HelioAI/LFM3-1.7B-inference-modules-experiments 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 HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_K_M

Use Docker

docker model run hf.co/HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_K_M

Llama new
LM Studio
Jan
Ollama
How to use HelioAI/LFM3-1.7B-inference-modules-experiments with Ollama:
```
ollama run hf.co/HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_K_M
```
Unsloth Desktop

How to use HelioAI/LFM3-1.7B-inference-modules-experiments with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_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": "HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use HelioAI/LFM3-1.7B-inference-modules-experiments with Docker Model Runner:
```
docker model run hf.co/HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_K_M
```

Lemonade

How to use HelioAI/LFM3-1.7B-inference-modules-experiments with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_K_M

Run and chat with the model

lemonade run user.LFM3-1.7B-inference-modules-experiments-Q5_K_M

List all available models

lemonade list

Hermes Agent

How to use HelioAI/LFM3-1.7B-inference-modules-experiments with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_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 HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use HelioAI/LFM3-1.7B-inference-modules-experiments with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_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 "HelioAI/LFM3-1.7B-inference-modules-experiments:Q5_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"

LFM3-1.7B-inference-modules-experiments / research /scripts /make_cvec.py

HelioAI

LFM3-1.7B inference modules: experimental release

67ebcef verified 2 days ago

Raw History Blame Contribute Delete

1.05 kB

"""Writes a llama.cpp control-vector GGUF: alpha * unit(direction[k]) * avg_norm[k] added to the output of each layer
in --layers (0-based; layer 0 cannot be steered by llama.cpp). Negative alpha = steer AWAY from the direction.
usage: make_cvec.py results/cvec/hes.pt out.gguf --alpha -0.1 --layers 12 13 14"""
import argparse, torch, gguf
ap = argparse.ArgumentParser()
ap.add_argument("src"); ap.add_argument("out")
ap.add_argument("--alpha", type=float, required=True)
ap.add_argument("--layers", type=int, nargs="+", required=True)
a = ap.parse_args()
s = torch.load(a.src)
d, nrm = s["dir"], s["avg_norm"]
w = gguf.GGUFWriter(a.out, "controlvector")
w.add_string("controlvector.model_hint", "lfm3")
w.add_uint32("controlvector.layer_count", len(a.layers))
for k in a.layers:
    assert k >= 1
    v = a.alpha * nrm[k] * d[k] / d[k].norm()
    w.add_tensor(f"direction.{k}", v.float().numpy())
w.write_header_to_file(); w.write_kv_data_to_file(); w.write_tensors_to_file(); w.close()
print("wrote", a.out, "alpha", a.alpha, "layers", a.layers)

	"""Writes a llama.cpp control-vector GGUF: alpha * unit(direction[k]) * avg_norm[k] added to the output of each layer
	in --layers (0-based; layer 0 cannot be steered by llama.cpp). Negative alpha = steer AWAY from the direction.
	usage: make_cvec.py results/cvec/hes.pt out.gguf --alpha -0.1 --layers 12 13 14"""
	import argparse, torch, gguf
	ap = argparse.ArgumentParser()
	ap.add_argument("src"); ap.add_argument("out")
	ap.add_argument("--alpha", type=float, required=True)
	ap.add_argument("--layers", type=int, nargs="+", required=True)
	a = ap.parse_args()
	s = torch.load(a.src)
	d, nrm = s["dir"], s["avg_norm"]
	w = gguf.GGUFWriter(a.out, "controlvector")
	w.add_string("controlvector.model_hint", "lfm3")
	w.add_uint32("controlvector.layer_count", len(a.layers))
	for k in a.layers:
	assert k >= 1
	v = a.alpha * nrm[k] * d[k] / d[k].norm()
	w.add_tensor(f"direction.{k}", v.float().numpy())
	w.write_header_to_file(); w.write_kv_data_to_file(); w.write_tensors_to_file(); w.close()
	print("wrote", a.out, "alpha", a.alpha, "layers", a.layers)