Text Generation
GGUF
llama.cpp
pocketpal
on-device
Mixture of Experts
multilingual
quantization
distillation
1-bit
2-bit
3-bit
conversational
Instructions to use RegalInvader/Marco-Nano-Instruct-GGUF 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 RegalInvader/Marco-Nano-Instruct-GGUF 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 RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0 # Run inference directly in the terminal: llama cli -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0 # Run inference directly in the terminal: llama cli -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
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 RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
Use Docker
docker model run hf.co/RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
- Llama new
- LM Studio
- Jan
- vLLM
How to use RegalInvader/Marco-Nano-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RegalInvader/Marco-Nano-Instruct-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RegalInvader/Marco-Nano-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
- Ollama
How to use RegalInvader/Marco-Nano-Instruct-GGUF with Ollama:
ollama run hf.co/RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
- Unsloth Desktop
- Pi
How to use RegalInvader/Marco-Nano-Instruct-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
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": "RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use RegalInvader/Marco-Nano-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
- Lemonade
How to use RegalInvader/Marco-Nano-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
Run and chat with the model
lemonade run user.Marco-Nano-Instruct-GGUF-TQ1_0
List all available models
lemonade list
- Hermes Agent
How to use RegalInvader/Marco-Nano-Instruct-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
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 RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use RegalInvader/Marco-Nano-Instruct-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
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 "RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0" \ --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: 11,132 Bytes
e5235fb | 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 | #!/usr/bin/env python3
"""HumanEval+ (164) and MBPP+ (378) through each model's own chat template (D-59b: the varied-data branches add code,
so the ship rule needs a code test). Thinking off, greedy, EvalPlus's instruct prompt: the problem in a code block,
then the reply opened with EvalPlus's response prefix and a ```python fence (rendered by /apply-template and appended
as plain text, as compare_quality.py does for multiple choice), generated to the closing fence.
The programs run with EvalPlus's own harness (each dataset row's `test`: base and plus inputs with expected outputs and
EvalPlus's float-tolerant assertion) inside one Docker container per model: no network, read-only root, user nobody,
memory and process limits, a time limit per problem. A problem passes if its program passes every test; the score is
pass@1 per benchmark (EvalPlus's "plus" number).
python scripts/code_eval.py --server .../llama-server --data DIR_WITH_THE_PARQUETS --model NAME=PATH.gguf ...
Per-item results in <out>/<name>.code.jsonl (generations reused on a rerun), summary in <out>/<name>.code.summary.json.
"""
from __future__ import annotations
import argparse
import ast
import json
import re
import signal
import subprocess
import sys
import tempfile
import time
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
import pyarrow.parquet as pq
sys.path.insert(0, str(Path(__file__).resolve().parent))
import compare_quality as cq # noqa: E402
REPO = Path(__file__).resolve().parents[1]
INSTRUCTION = "Please provide a self-contained Python script that solves the following problem in a markdown code block:"
RESPONSE_PREFIX = ("Below is a Python script with a self-contained function that solves the problem and passes "
"corresponding tests:\n```python\n")
IMAGE = "ghcr.io/open-webui/open-webui:main" # an image already on gen with python3.11 and numpy (no pull needed)
GEN_TOKENS = 1024
# the closing fence, or top-level test code after the solution (smoke test: Qwen3.5-2B wrote asserts to the token cap;
# no "\nassert" stop: MBPP replies often echo the prompt's docstring, which holds an assert)
STOP = ["```", "\nif __name__", "\nprint(", "\n# Test", "\n# Example"]
# Problems whose own reference solution fails in this harness (self-test 28 Oct: 540 of 542 pass): HumanEval/32's
# check unpacks the float find_zero returns; Mbpp/255's reference needs more than the 2 GB per-program limit.
EXCLUDE = {"HumanEval/32", "Mbpp/255"}
RUNNER = r'''
import json, resource, subprocess, sys
from concurrent.futures import ThreadPoolExecutor
n, timeout = int(sys.argv[1]), float(sys.argv[2])
def limits():
resource.setrlimit(resource.RLIMIT_AS, (2 << 30, 2 << 30))
resource.setrlimit(resource.RLIMIT_FSIZE, (1 << 20, 1 << 20))
def run(i):
try:
p = subprocess.run([sys.executable, "-I", f"/work/{i}.py"], capture_output=True, timeout=timeout,
preexec_fn=limits, cwd="/tmp")
return {"i": i, "status": "pass" if p.returncode == 0 else "fail", "err": p.stderr.decode(errors="replace")[-300:]}
except subprocess.TimeoutExpired:
return {"i": i, "status": "timeout", "err": ""}
with ThreadPoolExecutor(8) as ex:
for r in ex.map(run, range(n)):
print(json.dumps(r), flush=True)
'''
def items(data: Path) -> list[dict]:
out = []
for f in sorted(data.glob("*.parquet")):
rows = pq.read_table(f).to_pylist()
for r in rows:
if "entry_point" in r: # HumanEval+
out.append({"task": "humanevalplus", "id": r["task_id"], "code": r["prompt"].strip(),
"prefix": r["prompt"], "entry": r["entry_point"], "imports": [], "test": r["test"]})
else: # MBPP+: EvalPlus's prompt is the statement plus its first assert, as a docstring
defs = re.findall(r"^def (\w+)\(", r["code"], re.M)
called = [d for d in defs if re.search(rf"\b{d}\(", r["test_list"][0])]
out.append({"task": "mbppplus", "id": f"Mbpp/{r['task_id']}",
"code": f'"""\n{r["prompt"]}\n{r["test_list"][0]}\n"""', "prefix": "",
"entry": (called or defs)[-1] if (called or defs) else None,
"imports": list(r["test_imports"]), "test": r["test"]})
return [it for it in out if it["id"] not in EXCLUDE]
def ask(port: int, it: dict) -> dict:
msgs = [{"role": "user", "content": f"{INSTRUCTION}\n```\n{it['code']}\n```\n"}]
try:
prompt = cq.post(port, "/apply-template", {"messages": msgs,
"chat_template_kwargs": {"enable_thinking": False}})["prompt"]
res = cq.post(port, "/completion", {"prompt": prompt + RESPONSE_PREFIX, "n_predict": GEN_TOKENS,
"temperature": 0.0, "stop": STOP})
return {"content": res["content"], "n_out": res.get("tokens_predicted"),
"stopped": bool(res.get("stopped_word") or res.get("stopping_word"))}
except Exception as e: # noqa: BLE001 — scored as a failure
return {"error": str(e)[:300]}
def sanitize(code: str) -> str:
"""EvalPlus-style: the longest prefix that parses (a reply cut at the token cap), then only its imports, classes,
functions and constant assignments; top-level calls, asserts, prints and __main__ blocks are dropped."""
lines = code.splitlines()
for k in range(len(lines), 0, -1):
try:
tree = ast.parse("\n".join(lines[:k]))
break
except SyntaxError:
continue
else:
return code
keep = [n for n in tree.body if isinstance(n, (ast.Import, ast.ImportFrom, ast.FunctionDef, ast.AsyncFunctionDef,
ast.ClassDef))
or (isinstance(n, (ast.Assign, ast.AnnAssign)) and n.value is not None
and not any(isinstance(c, ast.Call) for c in ast.walk(n.value)))]
return "\n\n".join(ast.unparse(n) for n in keep)
def program(it: dict, completion: str) -> str:
code = sanitize(completion)
if it["task"] == "humanevalplus" and not re.search(rf"^def {re.escape(it['entry'])}\(", code, re.M):
code = it["prefix"] + code # the reply continued the function body
head = "\n".join(it["imports"])
tail = f"\n\ncheck({it['entry']})\n" if it["task"] == "humanevalplus" else "\n"
return f"{head}\n{code}\n\n{it['test']}{tail}"
def execute(progs: list[str], timeout: float) -> list[dict]:
with tempfile.TemporaryDirectory(prefix="code_eval_") as d:
w = Path(d)
for i, p in enumerate(progs):
(w / f"{i}.py").write_text(p)
(w / "runner.py").write_text(RUNNER)
w.chmod(0o755)
for f in w.iterdir():
f.chmod(0o644)
cmd = ["docker", "run", "--rm", "--network", "none", "--read-only", "--tmpfs", "/tmp:size=256m,mode=1777",
"--user", "65534:65534", "--memory", "8g", "--memory-swap", "8g", "--cpus", "8", "--pids-limit", "256",
"--security-opt", "no-new-privileges", "--cap-drop", "ALL", "-v", f"{w}:/work:ro",
"-e", "OPENBLAS_NUM_THREADS=1", "-e", "OMP_NUM_THREADS=1", "-e", "MKL_NUM_THREADS=1",
"--entrypoint", "python3", IMAGE, "-I", "/work/runner.py", str(len(progs)), str(timeout)]
p = subprocess.run(cmd, capture_output=True, timeout=timeout * len(progs) / 4 + 600)
res = [json.loads(l) for l in p.stdout.decode().splitlines() if l.startswith("{")]
if len(res) != len(progs):
raise RuntimeError(f"sandbox returned {len(res)} of {len(progs)} results: {p.stderr.decode()[-500:]}")
return sorted(res, key=lambda r: r["i"])
def main() -> None:
signal.signal(signal.SIGTERM, lambda *_: sys.exit(143))
ap = argparse.ArgumentParser()
ap.add_argument("--server", required=True)
ap.add_argument("--data", type=Path, default=Path("/path/to/data/evalsets/evalplus_dl/data"))
ap.add_argument("--model", action="append", required=True, help="NAME=PATH.gguf")
ap.add_argument("--out", type=Path, default=REPO / "reports" / "compare")
ap.add_argument("--port", type=int, default=18190)
ap.add_argument("--ngl", default="99")
ap.add_argument("--workers", type=int, default=8)
ap.add_argument("--timeout", type=float, default=60.0, help="seconds per problem (all its tests)")
ap.add_argument("--limit", type=int, default=0, help="first N problems per benchmark (smoke test)")
a = ap.parse_args()
its = items(a.data)
if a.limit:
its = [it for t in ("humanevalplus", "mbppplus") for it in [x for x in its if x["task"] == t][:a.limit]]
print(f"{len(its)} problems", flush=True)
for spec in a.model:
name, gguf = spec.split("=", 1)
path = a.out / f"{name}.code.jsonl"
done = {(r["task"], r["id"]): r for r in map(json.loads, path.open())} if path.exists() else {}
todo = [it for it in its if (it["task"], it["id"]) not in done]
if todo:
t0 = time.time()
p = cq.serve(a.server, gguf, a.port, a.ngl, a.out / f"{name}.code.server.log")
try:
with ThreadPoolExecutor(a.workers) as ex:
answers = list(ex.map(lambda it: ask(a.port, it), todo))
finally:
p.terminate()
p.wait(60)
print(f"{name}: generated {len(todo)} in {time.time() - t0:.0f}s", flush=True)
progs = [program(it, ans.get("content", "")) for it, ans in zip(todo, answers)]
runs = execute(progs, a.timeout)
with path.open("a") as f:
for it, ans, r in zip(todo, answers, runs):
row = {"task": it["task"], "id": it["id"], "correct": "error" not in ans and r["status"] == "pass",
"status": "error" if "error" in ans else r["status"], "n_out": ans.get("n_out"),
"stopped": ans.get("stopped"), "completion": ans.get("content", ans.get("error", ""))[-1500:],
"err": r["err"]}
done[(it["task"], it["id"])] = row
f.write(json.dumps(row, ensure_ascii=False) + "\n")
rows = [done[(it["task"], it["id"])] for it in its]
tasks = {}
for t in ("humanevalplus", "mbppplus"):
rs = [r for r in rows if r["task"] == t]
if rs:
tasks[t] = sum(r["correct"] for r in rs) / len(rs)
st = {}
for r in rows:
st[r["status"]] = st.get(r["status"], 0) + 1
s = {"model": name, "groups": {"HumanEval+": tasks.get("humanevalplus"), "MBPP+": tasks.get("mbppplus")},
"tasks": tasks, "status": st, "n": len(rows), "gguf": gguf, "bytes": Path(gguf).stat().st_size}
(a.out / f"{name}.code.summary.json").write_text(json.dumps(s, indent=1))
print(json.dumps({"model": name, "suite": "code", **{k: round(v, 4) for k, v in tasks.items()}, "status": st}),
flush=True)
if __name__ == "__main__":
main()
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