Safetensors
GGUF
English
Russian
lfm3
reasoning
test-time-compute
inference-time-scaling
code-generation
llama.cpp
negative-results
conversational
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
- llama.cpp
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
- Pi
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"
File size: 4,253 Bytes
67ebcef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | """Fresh held-out visual tasks (4 per category), written and frozen BEFORE the mentor training and never used for the
curriculum or the advice. The curriculum was designed after seeing the student's weak skills on the 32 benchmark
tasks; this set checks that what it learned carries over to subjects nobody looked at.
"""
import json
from make_mentor_tasks import HTML_TAIL, LIB_NONE, LIB_THREE, SVG_TAIL # noqa: E402 (also rewrites mentor_tasks.jsonl)
SVG = [
"Draw a cartoon penguin standing on ice: a black back and head, a white belly, an orange beak and feet, two flippers and two small eyes.",
"Draw a radial menu: a ring divided into six equal segments with a label in each (Home, Mail, Music, Photos, Maps, Settings) and a round 'Menu' button in the centre.",
"Draw a pyramid diagram: a triangle split into four horizontal layers labelled from bottom to top Basics, Practice, Projects, Mastery, each layer in a different colour.",
"Draw a side view of a steam locomotive on rails: a long boiler, a cab with a window, a chimney with a puff of smoke, and three wheels joined by a connecting rod.",
]
PAGES = [
"Build a 'Contact us' page with two columns: a contact form (name, email, subject, message, send button) on the left and the office address, phone, email and opening hours on the right.",
"Build a social media profile header: a cover image placeholder (a gradient), a round avatar overlapping its bottom edge, the name, a short bio, follower and following counts and a Follow button.",
"Build an invoice page: a company header, a bill-to address, a table of four line items with quantity, unit price and amount, the subtotal, tax and total, and payment terms at the bottom.",
"Build a weekly habit tracker: a table of five habits by seven days with cells that are checked by clicking, and a completion percentage for each habit that updates.",
]
CANVAS = [
("Make a constellation animation on a canvas: 80 slowly drifting points, joined by lines when they are closer than 120 pixels, with the line opacity fading with distance.", True),
("Make an aquarium animation on a canvas: fish of different colours swimming left and right at different speeds and depths, rising bubbles and swaying seaweed.", True),
("Make a tic-tac-toe game on a canvas: clicking places X and O in turn, a win or a draw is detected and shown, and a restart button starts a new game.", False),
("Make a kaleidoscope animation on a canvas: a pattern of shapes mirrored into 12 symmetric segments around the centre, slowly rotating and changing colour.", True),
]
THREE = [
("Make a three.js carousel: a round platform with a cone roof on a central pole and six horses represented by colourful boxes that move up and down while the carousel rotates.", True),
("Make a three.js bowling scene: a wooden lane, ten white pins arranged in a triangle and a ball rolling towards them.", True),
("Make a three.js lava lamp: a transparent glass capsule on a dark base with several orange blobs (spheres) slowly rising and sinking inside it.", True),
("Make a three.js Ferris wheel: a large ring on two supports with eight gondolas (boxes) hanging from it that stay upright while the wheel turns.", True),
]
tasks = [{"id": f"fresh/s{i}", "cat": "svg", "kind": "svg", "animated": False, "prompt": p + SVG_TAIL}
for i, p in enumerate(SVG, 1)]
tasks += [{"id": f"fresh/p{i}", "cat": "html", "kind": "html", "animated": False,
"prompt": p + HTML_TAIL.format(lib=LIB_NONE)} for i, p in enumerate(PAGES, 1)]
tasks += [{"id": f"fresh/a{i}", "cat": "canvas", "kind": "html", "animated": an,
"prompt": p + HTML_TAIL.format(lib=LIB_NONE)} for i, (p, an) in enumerate(CANVAS, 1)]
tasks += [{"id": f"fresh/d{i}", "cat": "three", "kind": "html", "animated": an,
"prompt": p + HTML_TAIL.format(lib=LIB_THREE)} for i, (p, an) in enumerate(THREE, 1)]
with open("data/visual/tasks_fresh.jsonl", "w", encoding="utf-8") as f:
for t in tasks:
f.write(json.dumps({"id": t["id"], "task": "visual", **{k: v for k, v in t.items() if k != "id"}},
ensure_ascii=False) + "\n")
print(len(tasks), "fresh held-out tasks -> data/visual/tasks_fresh.jsonl")
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