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 /budget_view.py

HelioAI

LFM3-1.7B inference modules: experimental release

67ebcef verified 2 days ago

Raw History Blame Contribute Delete

1.34 kB

"""Exactly paired budget curve: cuts long generations (run with gen.py --save-tokens) to a smaller token budget.
Generation is autoregressive with a fixed seed, so the first B tokens of a 32k run are what a B-token run would
have produced; a record that finished within B tokens is unchanged, a longer one becomes a truncated record of B.
usage: budget_view.py --src results/m180_32k/mini.jsonl --budget 16384 --out results/m180_16k/mini.jsonl
"""
import argparse
import json
import os

from transformers import AutoTokenizer

ap = argparse.ArgumentParser()
ap.add_argument("--src", required=True)
ap.add_argument("--budget", type=int, required=True)
ap.add_argument("--out", required=True)
ap.add_argument("--tokenizer", default="models/LFM3-1.7B")
a = ap.parse_args()
tok = AutoTokenizer.from_pretrained(a.tokenizer)
os.makedirs(os.path.dirname(a.out), exist_ok=True)
cut = 0
with open(a.src, encoding="utf-8") as f, open(a.out, "w", encoding="utf-8") as out:
    for l in f:
        r = json.loads(l)
        if r["n_tokens"] > a.budget:
            ids = r["tokens"][: a.budget]
            r.update(completion=tok.decode(ids), tokens=ids, n_tokens=a.budget, truncated=True, stop_type="limit")
            cut += 1
        out.write(json.dumps(r, ensure_ascii=False) + "\n")
print(f"{a.src} -> budget {a.budget}: {cut} records cut")

	"""Exactly paired budget curve: cuts long generations (run with gen.py --save-tokens) to a smaller token budget.
	Generation is autoregressive with a fixed seed, so the first B tokens of a 32k run are what a B-token run would
	have produced; a record that finished within B tokens is unchanged, a longer one becomes a truncated record of B.
	usage: budget_view.py --src results/m180_32k/mini.jsonl --budget 16384 --out results/m180_16k/mini.jsonl
	"""
	import argparse
	import json
	import os

	from transformers import AutoTokenizer

	ap = argparse.ArgumentParser()
	ap.add_argument("--src", required=True)
	ap.add_argument("--budget", type=int, required=True)
	ap.add_argument("--out", required=True)
	ap.add_argument("--tokenizer", default="models/LFM3-1.7B")
	a = ap.parse_args()
	tok = AutoTokenizer.from_pretrained(a.tokenizer)
	os.makedirs(os.path.dirname(a.out), exist_ok=True)
	cut = 0
	with open(a.src, encoding="utf-8") as f, open(a.out, "w", encoding="utf-8") as out:
	for l in f:
	r = json.loads(l)
	if r["n_tokens"] > a.budget:
	ids = r["tokens"][: a.budget]
	r.update(completion=tok.decode(ids), tokens=ids, n_tokens=a.budget, truncated=True, stop_type="limit")
	cut += 1
	out.write(json.dumps(r, ensure_ascii=False) + "\n")
	print(f"{a.src} -> budget {a.budget}: {cut} records cut")