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lfm3
reasoning
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inference-time-scaling
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llama.cpp
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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"
Download research/scripts/blind_pairs.py from HelioAI/LFM3-1.7B-inference-modules-experiments: direct link, hf CLI and curl.
- Browser
- Download file 3.63 kB
-
https://huggingface.co/HelioAI/LFM3-1.7B-inference-modules-experiments/resolve/main/research/scripts/blind_pairs.py
- Command line
-
hf download hf://HelioAI/LFM3-1.7B-inference-modules-experiments/research/scripts/blind_pairs.py
-
curl -L -o blind_pairs.py https://huggingface.co/HelioAI/LFM3-1.7B-inference-modules-experiments/resolve/main/research/scripts/blind_pairs.py
3.63 kB
| """Blind pairwise comparison sheets for visual answers. | |
| make: blind_pairs.py make --a results/vis_base_dm/png --b results/vis_rep/png --ids v01 ... --out results/blind_rep | |
| Each row shows one task: left / right are A and B in a random order (seeded); the key is written to | |
| <out>/key.json and must not be opened before judging. Sheets: <out>/sheet_<k>.png, 8 tasks each. | |
| score: blind_pairs.py score --out results/blind_rep | |
| Reads <out>/judgments.json ({"v01": "L" | "R" | "=" , ...}, written before opening the key) and reports how | |
| often B (the candidate system) won, lost or tied against A. | |
| """ | |
| import argparse | |
| import json | |
| import os | |
| import random | |
| import re | |
| from PIL import Image, ImageDraw | |
| ap = argparse.ArgumentParser() | |
| ap.add_argument("cmd", choices=["make", "score"]) | |
| ap.add_argument("--a") | |
| ap.add_argument("--b") | |
| ap.add_argument("--ids", nargs="+") | |
| ap.add_argument("--out", required=True) | |
| ap.add_argument("--sample", type=int, default=0) | |
| ap.add_argument("--seed", type=int, default=7) | |
| ap.add_argument("--w", type=int, default=420) | |
| ap.add_argument("--per-sheet", type=int, default=8) | |
| ap.add_argument("--tasks", help="task file: the first words of each task are printed over its row") | |
| a = ap.parse_args() | |
| if a.cmd == "make": | |
| heads = {} | |
| if a.tasks: | |
| for l in open(a.tasks, encoding="utf-8"): | |
| t = json.loads(l) | |
| heads[t["id"].split("/")[-1]] = t["prompt"][:70] | |
| os.makedirs(a.out, exist_ok=True) | |
| rng = random.Random(a.seed) | |
| key = {} | |
| w, h, pad = a.w, a.w * 3 // 4, 22 | |
| rows = [a.ids[i:i + a.per_sheet] for i in range(0, len(a.ids), a.per_sheet)] | |
| for k, ids in enumerate(rows): | |
| sheet = Image.new("RGB", (2 * (w + 6) + 6, len(ids) * (h + pad + 6) + 6), "#202124") | |
| d = ImageDraw.Draw(sheet) | |
| for r, tid in enumerate(ids): | |
| flip = rng.random() < 0.5 | |
| key[tid] = "B_left" if flip else "A_left" | |
| sides = [a.b, a.a] if flip else [a.a, a.b] | |
| for c, folder in enumerate(sides): | |
| x, y = 6 + c * (w + 6), 6 + r * (h + pad + 6) | |
| stem = tid if re.search(r"_\d+$", tid) else f"{tid}_{a.sample}" # ids may name the sample: v01_1 | |
| p = os.path.join(folder, f"{stem}.png") | |
| head = heads.get(re.sub(r"_\d+$", "", tid), "") if c == 0 else "" | |
| d.text((x + 4, y + 4), f"{tid} {'L' if c == 0 else 'R'} {head}", fill="#e8eaed") | |
| if os.path.exists(p): | |
| sheet.paste(Image.open(p).convert("RGB").resize((w, h)), (x, y + pad)) | |
| else: | |
| d.rectangle([x, y + pad, x + w, y + pad + h], outline="#5f6368") | |
| d.text((x + w // 2 - 30, y + pad + h // 2), "no output", fill="#9aa0a6") | |
| sheet.save(os.path.join(a.out, f"sheet_{k}.png")) | |
| json.dump(key, open(os.path.join(a.out, "key.json"), "w")) | |
| print(f"{len(rows)} sheets in {a.out}; key written (do not open before judging)") | |
| else: | |
| key = json.load(open(os.path.join(a.out, "key.json"))) | |
| judg = json.load(open(os.path.join(a.out, "judgments.json"))) | |
| win = lose = tie = 0 | |
| for tid, j in judg.items(): | |
| if j == "=": | |
| tie += 1 | |
| continue | |
| b_left = key[tid] == "B_left" | |
| if (j == "L") == b_left: | |
| win += 1 | |
| else: | |
| lose += 1 | |
| n = win + lose + tie | |
| print(f"candidate B vs A over {n} tasks: wins {win}, losses {lose}, ties {tie} " | |
| f"(win rate excluding ties {100 * win / max(1, win + lose):.0f}%)") | |