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 /prep_quick.py

HelioAI

LFM3-1.7B inference modules: experimental release

67ebcef verified 2 days ago

Raw History Blame Contribute Delete

1.07 kB

"""Quick screening set (274 dev items, never test): 100 math, 50 code, 70 MMLU-Pro (5/category), 54 BBH (2/task).
Random, NOT selected by the original model's results (that would bias every comparison by regression to the mean)."""
import json, random, collections
rng = random.Random(2024)
dev = [json.loads(l) for l in open("data/train/dev.jsonl", encoding="utf-8")]
gen = [json.loads(l) for l in open("data/train/dev_general.jsonl", encoding="utf-8")]
math = [r for r in dev if r["task"] == "math"]
code = [r for r in dev if r["task"] != "math"]
pick = rng.sample(math, 100) + rng.sample(code, 50)
for task, k in [("mmlupro", 5), ("bbh", 2)]:
    groups = collections.defaultdict(list)
    for r in gen:
        if r["task"] == task:
            groups[r.get("category") or r["id"].split("/")[1]].append(r)
    for g in sorted(groups):
        pick += rng.sample(groups[g], k)
with open("data/quick/quick.jsonl", "w", encoding="utf-8") as f:
    for r in pick:
        f.write(json.dumps(r, ensure_ascii=False) + "\n")
print(collections.Counter(r["task"] for r in pick))

	"""Quick screening set (274 dev items, never test): 100 math, 50 code, 70 MMLU-Pro (5/category), 54 BBH (2/task).
	Random, NOT selected by the original model's results (that would bias every comparison by regression to the mean)."""
	import json, random, collections
	rng = random.Random(2024)
	dev = [json.loads(l) for l in open("data/train/dev.jsonl", encoding="utf-8")]
	gen = [json.loads(l) for l in open("data/train/dev_general.jsonl", encoding="utf-8")]
	math = [r for r in dev if r["task"] == "math"]
	code = [r for r in dev if r["task"] != "math"]
	pick = rng.sample(math, 100) + rng.sample(code, 50)
	for task, k in [("mmlupro", 5), ("bbh", 2)]:
	groups = collections.defaultdict(list)
	for r in gen:
	if r["task"] == task:
	groups[r.get("category") or r["id"].split("/")[1]].append(r)
	for g in sorted(groups):
	pick += rng.sample(groups[g], k)
	with open("data/quick/quick.jsonl", "w", encoding="utf-8") as f:
	for r in pick:
	f.write(json.dumps(r, ensure_ascii=False) + "\n")
	print(collections.Counter(r["task"] for r in pick))