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: 2,590 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 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | """Review sheets for the mentor: per task one row with the student's attempts side by side and the browser report.
usage: mentor_sheet.py --tag mtr_dm --tasks data/visual/mentor_tasks.jsonl --out results/mentor/review --per 5
Rows: '<id> <first words of the task>' and under each screenshot 'ok' or the problems found (blank, errors, static).
"""
import argparse
import json
import os
from PIL import Image, ImageDraw
ap = argparse.ArgumentParser()
ap.add_argument("--tag", required=True)
ap.add_argument("--tasks", default="data/visual/mentor_tasks.jsonl")
ap.add_argument("--out", required=True)
ap.add_argument("--per", type=int, default=5)
ap.add_argument("--samples", type=int, default=2)
ap.add_argument("--ids", nargs="*")
ap.add_argument("--w", type=int, default=400)
a = ap.parse_args()
tasks = [json.loads(l) for l in open(a.tasks, encoding="utf-8")]
if a.ids:
tasks = [t for t in tasks if t["id"].split("/")[-1] in a.ids]
rep = {}
for r in json.load(open(os.path.join("results", a.tag, "visual.json"))):
rep[(r["id"].split("/")[-1], r["sample"])] = r
def flags(r, task):
if r is None:
return "no answer"
out = []
if r.get("parse_error"):
out.append("bad XML")
if r["errors"]:
out.append("JS: " + r["errors"][0][:60])
if r["failed"]:
out.append("load failed")
if r["blank"]:
out.append("blank")
if task["animated"] and not r["animated"] and not r["blank"]:
out.append("static")
return "ok" if not out else "; ".join(out)
os.makedirs(a.out, exist_ok=True)
w, h, head, foot = a.w, a.w * 3 // 4, 20, 18
for k in range(0, len(tasks), a.per):
rows = tasks[k:k + a.per]
sheet = Image.new("RGB", (a.samples * (w + 6) + 6, len(rows) * (h + head + foot + 8) + 6), "#202124")
d = ImageDraw.Draw(sheet)
for i, t in enumerate(rows):
tid = t["id"].split("/")[-1]
y = 6 + i * (h + head + foot + 8)
d.text((8, y + 3), f"{tid} {t['prompt'][:95]}", fill="#e8eaed")
for s in range(a.samples):
x = 6 + s * (w + 6)
p = os.path.join("results", a.tag, "png", f"{tid}_{s}.png")
if os.path.exists(p):
sheet.paste(Image.open(p).convert("RGB").resize((w, h)), (x, y + head))
else:
d.rectangle([x, y + head, x + w, y + head + h], outline="#5f6368")
d.text((x + 2, y + head + h + 3), f"s{s}: {flags(rep.get((tid, s)), t)}", fill="#fdd663")
sheet.save(os.path.join(a.out, f"sheet_{k // a.per:02d}.png"))
print(f"{(len(tasks) + a.per - 1) // a.per} sheets in {a.out}")
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