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 /microbench2.py

HelioAI

LFM3-1.7B inference modules: experimental release

67ebcef verified 2 days ago

Raw History Blame Contribute Delete

1.02 kB

import json, time, urllib.request
from concurrent.futures import ThreadPoolExecutor
base = {"n_predict":384,"ignore_eos":True,"temperature":0.6,"top_k":50,"top_p":0.95,"min_p":0,"samplers":["top_k","top_p","temperature"]}
extra = {"repeat_penalty": 1.0, "presence_penalty": 0.0, "frequency_penalty": 0.0, "dry_multiplier": 0.0, "xtc_probability": 0.0, "typical_p": 1.0, "cache_prompt": False}
variants = {"base": {}, "all_extra": extra, **{k: {k: v} for k, v in extra.items()}}
def one(cfg, i):
    b = {"prompt": "Write a long story about a dragon number %d." % i, "seed": i, **base, **cfg}
    r = json.loads(urllib.request.urlopen(urllib.request.Request("http://localhost:8080/completion", data=json.dumps(b).encode(), headers={"Content-Type": "application/json"})).read())
    return r["tokens_predicted"]
for name, cfg in variants.items():
    t = time.time()
    with ThreadPoolExecutor(48) as ex: n = sum(ex.map(lambda i: one(cfg, i), range(48)))
    print(name, round(n / (time.time() - t)), "tok/s", flush=True)

	import json, time, urllib.request
	from concurrent.futures import ThreadPoolExecutor
	base = {"n_predict":384,"ignore_eos":True,"temperature":0.6,"top_k":50,"top_p":0.95,"min_p":0,"samplers":["top_k","top_p","temperature"]}
	extra = {"repeat_penalty": 1.0, "presence_penalty": 0.0, "frequency_penalty": 0.0, "dry_multiplier": 0.0, "xtc_probability": 0.0, "typical_p": 1.0, "cache_prompt": False}
	variants = {"base": {}, "all_extra": extra, **{k: {k: v} for k, v in extra.items()}}
	def one(cfg, i):
	b = {"prompt": "Write a long story about a dragon number %d." % i, "seed": i, base, cfg}
	r = json.loads(urllib.request.urlopen(urllib.request.Request("http://localhost:8080/completion", data=json.dumps(b).encode(), headers={"Content-Type": "application/json"})).read())
	return r["tokens_predicted"]
	for name, cfg in variants.items():
	t = time.time()
	with ThreadPoolExecutor(48) as ex: n = sum(ex.map(lambda i: one(cfg, i), range(48)))
	print(name, round(n / (time.time() - t)), "tok/s", flush=True)