Safetensors
GGUF
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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: 10,725 Bytes
cd61333 | 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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 | """Grades generations produced by gen.py against the frozen eval suite.
Only the final answer (text after </think>) is graded; a response whose thinking never closed counts as wrong.
Writes results/<tag>/graded.jsonl (one row per sample) and prints per-task accuracy.
usage: grade.py <tag>
"""
import glob
import json
import os
import re
import subprocess
import sandbox # noqa: E402 (same folder)
import sys
import tempfile
import threading
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..")
TMP = os.path.join(tempfile.gettempdir(), "lfm3_codeexec")
os.makedirs(TMP, exist_ok=True)
def final_answer(completion):
if "</think>" not in completion:
return None
return completion.split("</think>", 1)[1].strip()
def last_boxed(text):
idx = text.rfind("\\boxed")
if idx < 0:
idx = text.rfind("\\fbox")
if idx < 0:
return None
i = text.find("{", idx)
if i < 0:
return None
depth = 0
for j in range(i, len(text)):
if text[j] == "{":
depth += 1
elif text[j] == "}":
depth -= 1
if depth == 0:
return text[i + 1: j]
return None
def grade_math(ans_text, gold):
from math_verify import parse, verify
if ans_text is None:
return False
pred = last_boxed(ans_text)
if pred is None:
return False
try:
g = parse("$" + gold + "$", parsing_timeout=None)
p = parse("$" + pred + "$", parsing_timeout=None)
if verify(g, p, timeout_seconds=None):
return True
except Exception:
pass
norm = lambda s: re.sub(r"\\text\{(.*?)\}|\\!|\s|\$|\\left|\\right|\.$", r"\1", s).replace("dfrac", "frac")
return norm(pred) == norm(gold)
def grade_gsm8k(ans_text, gold):
if ans_text is None:
return False
pred = last_boxed(ans_text)
if pred is None:
nums = re.findall(r"-?\d[\d,]*\.?\d*", ans_text)
if not nums:
return False
pred = nums[-1]
pred = re.sub(r"\\text\{.*?\}|\\\$|\$|,|\\%|%|\\!|\s", "", pred)
m = re.search(r"-?\d+(\.\d+)?", pred.replace("{", "").replace("}", ""))
if not m:
return False
try:
return abs(float(m.group(0)) - float(gold)) < 1e-6
except ValueError:
return False
def extract_code(ans_text):
if ans_text is None:
return None
blocks = re.findall(r"```(?:python|py|Python)?\s*\n(.*?)```", ans_text, re.S)
if not blocks:
return None
return max(blocks, key=len) if len(blocks) > 1 and "def " not in blocks[-1] else blocks[-1]
def run_python(src, timeout):
fd, path = tempfile.mkstemp(suffix=".py", dir=TMP)
with os.fdopen(fd, "w", encoding="utf-8") as f:
f.write(src)
try:
r = sandbox.run([sys.executable, "-I", path], capture_output=True, timeout=timeout, cwd=TMP,
creationflags=getattr(subprocess, "CREATE_NO_WINDOW", 0))
return r.returncode == 0
except subprocess.TimeoutExpired:
return False
finally:
try:
os.remove(path)
except OSError:
pass
def grade_humaneval(ans_text, item):
code = extract_code(ans_text)
if code is None:
return False
if f"def {item['entry_point']}" not in code:
code = item["code_prompt"] + code
src = code + "\n\n" + item["test"] + f"\n\ncheck({item['entry_point']})\n"
return run_python(src, 60)
def grade_mbpp(ans_text, item):
code = extract_code(ans_text)
if code is None:
return False
src = "\n".join(item.get("test_imports") or []) + "\n" + code + "\n\n" + item["test"] + "\n"
return run_python(src, 60)
def run_io(code, inp, out, timeout=6):
fd, path = tempfile.mkstemp(suffix=".py", dir=TMP)
with os.fdopen(fd, "w", encoding="utf-8") as f:
f.write(code)
try:
r = sandbox.run([sys.executable, "-I", path], input=inp.encode(), capture_output=True, timeout=timeout,
cwd=TMP, creationflags=getattr(subprocess, "CREATE_NO_WINDOW", 0))
if r.returncode != 0:
return False
return r.stdout.decode(errors="replace").split() == out.split()
except subprocess.TimeoutExpired:
return False
finally:
try:
os.remove(path)
except OSError:
pass
def grade_code_io(ans_text, item):
code = extract_code(ans_text)
if code is None:
return False
return all(run_io(code, i, o) for i, o in zip(item["inputs"], item["outputs"]))
def grade_lcb(ans_text, item):
from lcb_tests import run_lcb
code = extract_code(ans_text)
if code is None:
return False
k, n, _ = run_lcb(code, item, item["hidden"])
return k == n
_IFEVAL_LOCK = threading.Lock()
def grade_ifeval(ans_text, item):
# lm_eval fills empty instruction arguments with random values and langdetect is random too: seed both under a
# lock so that a response always gets the same grade
import random
from langdetect import DetectorFactory
from lm_eval.tasks.ifeval.utils import process_results
doc = {"key": 0, "prompt": item["prompt"], "instruction_id_list": item["instruction_id_list"],
"kwargs": item["kwargs"]}
with _IFEVAL_LOCK:
random.seed(0)
DetectorFactory.seed = 0
res = process_results(doc, [ans_text or ""])
return {"strict": res["prompt_level_strict_acc"], "loose": res["prompt_level_loose_acc"],
"inst_strict": res["inst_level_strict_acc"]}
def grade_mmlupro(ans_text, gold):
if ans_text is None:
return False
m = re.findall(r"answer is:?\s*\(?\**\s*([A-J])\b", ans_text)
if not m:
m = re.findall(r"\\boxed\{\(?([A-J])\)?\}", ans_text)
return bool(m) and m[-1] == gold
def grade_bbh(ans_text, gold, prompt=None):
"""Exact match after extraction. With the prompt (flexible mode) it also accepts answers that state the same
thing in an unambiguous other form: the text of the chosen option instead of its letter, a word list separated by
commas, the whole Dyck sequence (input + completion) instead of the completion, plausible/not plausible for
yes/no. Each rule maps the answer to exactly one gold-comparable form, so it cannot accept a different answer."""
if ans_text is None:
return False
m = re.findall(r"answer is:?\s*(.+)", ans_text, re.I)
pred = m[-1] if m else (last_boxed(ans_text) or "")
pred = re.sub(r"\\boxed\{(.*)\}", r"\1", pred).strip().strip("*").strip().rstrip(".").strip()
gold = gold.strip()
norm = lambda s: re.sub(r"\s+", " ", s.lower().replace("`", "").replace("*", "")).strip().rstrip(".").strip()
if re.fullmatch(r"\([A-R]\)", gold):
mm = re.search(r"\(?([A-R])\)", pred) or re.fullmatch(r"([A-R])", pred)
if mm or prompt is None:
return bool(mm) and mm.group(1) == gold[1]
opts = {norm(t): k for k, t in re.findall(r"^\(([A-R])\)\s*(.+?)\s*$", prompt, re.M)}
return opts.get(norm(pred)) == gold[1]
if norm(pred) == norm(gold):
return True
if prompt is None:
return False
if "Sort the following words alphabetically" in prompt:
return re.findall(r"[^\s,]+", norm(pred)) == norm(gold).split()
if "parentheses are closed properly" in prompt:
inp = re.search(r"Input:\s*(.+)", prompt).group(1)
sp = lambda s: re.sub(r"\s+", "", norm(s))
return sp(pred) == sp(inp) + sp(gold)
yn = {"plausible": "yes", "not plausible": "no", "implausible": "no"}
return norm(gold) in ("yes", "no") and yn.get(norm(pred)) == norm(gold)
def load_suite(pattern=os.path.join(ROOT, "data", "eval", "*.jsonl")):
items = {}
for path in glob.glob(pattern):
with open(path, encoding="utf-8") as f:
for l in f:
r = json.loads(l)
items[r["id"]] = r
return items
def grade_one(rec, item):
a = final_answer(rec["completion"])
t = item["task"]
extra = {}
if t in ("math500", "aime"):
ok = grade_math(a, item["answer"])
elif t == "gsm8k":
ok = grade_gsm8k(a, item["answer"])
elif t == "humaneval":
ok = grade_humaneval(a, item)
elif t in ("mbpp", "code_fn"):
ok = grade_mbpp(a, item)
elif t == "code_io":
ok = grade_code_io(a, item)
elif t == "lcb":
ok = grade_lcb(a, item)
elif t == "math":
ok = grade_math(a, item["answer"])
elif t == "ifeval":
extra = grade_ifeval(a, item)
ok = extra["strict"]
elif t == "mmlupro":
ok = grade_mmlupro(a, item["answer"])
elif t == "bbh":
ok = grade_bbh(a, item["answer"], item["prompt"])
extra = {"strict": grade_bbh(a, item["answer"])}
else:
raise ValueError(t)
return {"id": rec["id"], "task": t, "sample": rec["sample"], "correct": bool(ok), "n_tokens": rec["n_tokens"],
"truncated": rec["truncated"], "closed_think": a is not None,
"subtask": item.get("subtask") or item.get("category") or item.get("level"), **extra}
def main(tag, pattern=None):
items = load_suite(pattern) if pattern else load_suite()
recs = []
for path in glob.glob(os.path.join(ROOT, "results", tag, "*.jsonl")):
if path.endswith("graded.jsonl"):
continue
with open(path, encoding="utf-8") as f:
recs += [json.loads(l) for l in f]
with ThreadPoolExecutor(12) as ex:
graded = list(ex.map(lambda r: grade_one(r, items[r["id"]]), recs))
with open(os.path.join(ROOT, "results", tag, "graded.jsonl"), "w", encoding="utf-8") as f:
for g in graded:
f.write(json.dumps(g) + "\n")
agg = defaultdict(list)
for g in graded:
agg[g["task"]].append(g)
print(f"{'task':10s} {'n':>5s} {'acc':>7s} {'trunc':>6s} {'no</think>':>10s} {'avg_tok':>8s}")
for t in sorted(agg):
rows = agg[t]
n = len(rows)
print(f"{t:10s} {n:5d} {100 * sum(r['correct'] for r in rows) / n:7.2f} "
f"{100 * sum(r['truncated'] for r in rows) / n:6.1f} {100 * sum(not r['closed_think'] for r in rows) / n:10.1f} "
f"{sum(r['n_tokens'] for r in rows) / n:8.0f}")
if t == "ifeval":
print(f"{' loose':10s} {'':5s} {100 * sum(r['loose'] for r in rows) / n:7.2f} inst_strict "
f"{100 * sum(sum(r['inst_strict']) for r in rows) / sum(len(r['inst_strict']) for r in rows):.2f}")
if __name__ == "__main__":
main(*sys.argv[1:])
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