Instructions to use build-small-hackathon/codeflow-qwen-3-finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use build-small-hackathon/codeflow-qwen-3-finetuning with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="build-small-hackathon/codeflow-qwen-3-finetuning", filename="qwen3-coder-codeflow-Q3_K_L.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use build-small-hackathon/codeflow-qwen-3-finetuning 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 build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L # Run inference directly in the terminal: llama cli -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L # Run inference directly in the terminal: llama cli -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L # Run inference directly in the terminal: ./llama-cli -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L # Run inference directly in the terminal: ./build/bin/llama-cli -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
Use Docker
docker model run hf.co/build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
- LM Studio
- Jan
- Ollama
How to use build-small-hackathon/codeflow-qwen-3-finetuning with Ollama:
ollama run hf.co/build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
- Unsloth Studio
How to use build-small-hackathon/codeflow-qwen-3-finetuning with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for build-small-hackathon/codeflow-qwen-3-finetuning to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for build-small-hackathon/codeflow-qwen-3-finetuning to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for build-small-hackathon/codeflow-qwen-3-finetuning to start chatting
- Pi
How to use build-small-hackathon/codeflow-qwen-3-finetuning with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use build-small-hackathon/codeflow-qwen-3-finetuning with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
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 build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use build-small-hackathon/codeflow-qwen-3-finetuning with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
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 "build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L" \ --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"
- Docker Model Runner
How to use build-small-hackathon/codeflow-qwen-3-finetuning with Docker Model Runner:
docker model run hf.co/build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
- Lemonade
How to use build-small-hackathon/codeflow-qwen-3-finetuning with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull build-small-hackathon/codeflow-qwen-3-finetuning:Q3_K_L
Run and chat with the model
lemonade run user.codeflow-qwen-3-finetuning-Q3_K_L
List all available models
lemonade list
File size: 12,454 Bytes
da653f3 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 | """Core synthetic-data engine for the code -> Mermaid flowchart dataset.
The whole design goal is *correctness by construction*. Each example's source
code and its flowchart are emitted from the same builder state, so:
* Every node's `<linemap>` line number is the live 1-based line number that
`CodeBuilder.add()` returned when the corresponding statement was written.
Injecting comments / docstrings / blank lines shifts the line numbers for
free and the map stays correct, because nothing is hard-coded.
* Decision labels are *paraphrased* plain-English questions (per the strict
system-prompt constraint), never raw code/operators/quotes/brackets.
`Mermaid` renders the graph + the `<linemap>` block; `validate_example()` is the
hard gate that every produced example must pass.
"""
from __future__ import annotations
import re
import string
from dataclasses import dataclass, field
from typing import Callable, Optional
# --------------------------------------------------------------------------- #
# Code builder
# --------------------------------------------------------------------------- #
class CodeBuilder:
"""Accumulates source lines and hands back live 1-based line numbers.
Templates call ``add()`` for real statements (and keep the returned line
number to wire into the flowchart) and ``blank()`` / ``comment()`` /
``docstring()`` for filler that deliberately shifts line numbers but maps to
no node.
"""
def __init__(self, indent_unit: str = " ", comment_prefix: str = "# "):
self.lines: list[str] = []
self.iu = indent_unit
self.cpre = comment_prefix
def add(self, text: str, indent: int = 0) -> int:
self.lines.append(self.iu * indent + text)
return len(self.lines)
def blank(self) -> int:
self.lines.append("")
return len(self.lines)
def comment(self, text: str, indent: int = 0) -> int:
return self.add(self.cpre + text, indent)
def maybe_blank(self, rng, p: float = 0.18) -> None:
if rng.random() < p:
self.blank()
def maybe_comment(self, rng, comments, indent: int = 0, p: float = 0.3) -> None:
if rng.random() < p:
self.comment(rng.choice(comments), indent)
def maybe_docstring(self, rng, texts, indent: int = 1, p: float = 0.3) -> None:
"""Python-style one-line docstring (filler that shifts line numbers)."""
if rng.random() < p:
self.add('"""' + rng.choice(texts) + '"""', indent)
def render_numbered(self) -> str:
return "\n".join(f"{i}| {ln}" for i, ln in enumerate(self.lines, 1))
def source(self) -> str:
return "\n".join(self.lines)
@property
def n_lines(self) -> int:
return len(self.lines)
# --------------------------------------------------------------------------- #
# Mermaid builder
# --------------------------------------------------------------------------- #
_LETTERS = string.ascii_uppercase
def _node_id(n: int) -> str:
"""1 -> A, 26 -> Z, 27 -> AA, ..."""
if n <= 26:
return _LETTERS[n - 1]
first = (n - 1) // 26 - 1
second = (n - 1) % 26
return _LETTERS[first] + _LETTERS[second]
# Allowed characters inside a rendered label. Deliberately excludes every
# operator, quote, paren and bracket the system prompt bans.
_LABEL_RE = re.compile(r"^[A-Za-z0-9 ?:_,./\-]+$")
class Mermaid:
SHAPES = {
"rect": ("[", "]"),
"decision": ("{", "}"),
"stadium": ("([", "])"),
"round": ("(", ")"),
}
def __init__(self, header: str = "graph TD"):
self.header = header
self._n = 0
# node id -> (shape, label)
self.nodes: list[tuple[str, str, str]] = []
self.edges: list[tuple[str, Optional[str], str]] = []
self.lines: dict[str, str] = {}
self.loop_count = 0
def add(self, label: str, shape: str = "rect", line=None) -> str:
self._n += 1
nid = _node_id(self._n)
self.nodes.append((nid, shape, label))
if line is not None:
self.lines[nid] = str(line)
return nid
def edge(self, a: str, b: str, label: Optional[str] = None, loop: bool = False) -> None:
self.edges.append((a, label, b))
if loop:
self.loop_count += 1
# rendering -------------------------------------------------------------- #
def _render_node(self, nid: str, shape: str, label: str) -> str:
open_b, close_b = self.SHAPES[shape]
return f" {nid}{open_b}{label}{close_b}"
def render_graph(self) -> str:
out = [self.header]
for nid, shape, label in self.nodes:
out.append(self._render_node(nid, shape, label))
for a, label, b in self.edges:
if label is None:
out.append(f" {a} --> {b}")
else:
out.append(f" {a} -- {label} --> {b}")
return "\n".join(out)
def render_linemap(self) -> Optional[str]:
rows = [f"{nid}: {self.lines[nid]}" for nid, _, _ in self.nodes if nid in self.lines]
if not rows:
return None
return "<linemap>\n" + "\n".join(rows) + "\n</linemap>"
# --------------------------------------------------------------------------- #
# Example container
# --------------------------------------------------------------------------- #
_TERMINAL_PREFIXES = ("Return", "Raise", "Throw", "Print", "Yield", "Error", "End", "Exit", "Break")
def _is_terminal(label: str) -> bool:
return label.startswith(_TERMINAL_PREFIXES)
@dataclass
class Example:
language: str
template: str
code: str # line-numbered source (the user turn)
output: str # thinking + mermaid + linemap (the assistant turn)
source: str # raw source without line prefixes (for syntax checking)
n_nodes: int
def _pluralize(n: int, singular: str) -> str:
if n == 1:
return f"{n} {singular}"
suffix = "es" if singular.endswith(("ch", "sh", "s", "x", "z")) else "s"
return f"{n} {singular}{suffix}"
def build_thinking(rng, m: Mermaid) -> str:
decisions = [n for n in m.nodes if n[1] == "decision"]
terminals = [n for n in m.nodes if _is_terminal(n[2])]
pieces = []
if decisions:
pieces.append(_pluralize(len(decisions), "decision point"))
if m.loop_count:
pieces.append(_pluralize(m.loop_count, "loop"))
pieces.append(_pluralize(len(terminals) or 1, "terminal branch"))
lead1 = rng.choice(["Control structures:", "Structural parse:", "Control flow detected:"])
p1 = f"1. {lead1} " + ", ".join(pieces) + "."
nodelist = ", ".join(f"{nid} {label}" for nid, _, label in m.nodes)
lead2 = rng.choice(["Nodes mapped chronologically:", "Execution nodes in order:", "Node sequence:"])
p2 = f"2. {lead2} {nodelist}."
mapped = [(nid, m.lines[nid]) for nid, _, _ in m.nodes if nid in m.lines]
shown = mapped[:6]
verb = rng.choice(["maps to line", "is line", "at line"])
linestr = ", ".join(f"{nid} {verb} {ln}" for nid, ln in shown)
if len(mapped) > len(shown):
linestr += ", and so on"
p3 = f"3. Source lines: {linestr}."
return "<thinking>\n" + "\n".join([p1, p2, p3]) + "\n</thinking>"
def assemble_output(rng, m: Mermaid) -> str:
thinking = build_thinking(rng, m)
graph = m.render_graph()
linemap = m.render_linemap()
parts = [thinking, graph]
if linemap is not None:
parts.append(linemap)
return "\n".join(parts)
# --------------------------------------------------------------------------- #
# Validation (the hard gate)
# --------------------------------------------------------------------------- #
class ValidationError(Exception):
pass
_NODE_DEF_RE = re.compile(
r"^([A-Za-z][A-Za-z0-9]*)"
r"(?:\(\[(?P<stadium>[^\]]*)\]\)"
r"|\{(?P<decision>[^{}]*)\}"
r"|\[(?P<rect>[^\]]*)\]"
r"|\((?P<round>[^()]*)\))$"
)
_EDGE_RE = re.compile(
r"^([A-Za-z][A-Za-z0-9]*)\s*(?:--\s*(?P<label>[^>]*?)\s*-->|-->)\s*([A-Za-z][A-Za-z0-9]*)$"
)
def _check_label(label: str) -> None:
if not label.strip():
raise ValidationError("empty label")
if not _LABEL_RE.match(label):
bad = sorted(set(c for c in label if not _LABEL_RE.match(c)))
raise ValidationError(f"label has banned chars {bad!r}: {label!r}")
def validate_mermaid_block(graph_text: str) -> set[str]:
"""Parse a rendered graph; return the set of defined node ids. Raises on any
malformed node/edge, banned label char, or edge referencing an unknown node.
"""
lines = [ln.rstrip() for ln in graph_text.splitlines() if ln.strip()]
if not lines:
raise ValidationError("empty graph")
if not re.match(r"^(graph|flowchart)\s+(TD|LR|TB|RL|BT)$", lines[0]):
raise ValidationError(f"bad header: {lines[0]!r}")
defined: set[str] = set()
referenced: set[str] = set()
for raw in lines[1:]:
body = raw.strip()
m_edge = _EDGE_RE.match(body)
if m_edge:
referenced.add(m_edge.group(1))
referenced.add(m_edge.group(3))
lbl = m_edge.groupdict().get("label")
if lbl:
_check_label(lbl)
continue
m_node = _NODE_DEF_RE.match(body)
if m_node:
nid = m_node.group(1)
label = next(v for k, v in m_node.groupdict().items() if k and v is not None)
_check_label(label)
if nid in defined:
raise ValidationError(f"duplicate node id {nid}")
defined.add(nid)
continue
raise ValidationError(f"unparseable mermaid line: {body!r}")
missing = referenced - defined
if missing:
raise ValidationError(f"edges reference undefined nodes: {sorted(missing)}")
if len(defined) < 1:
raise ValidationError("no nodes defined")
return defined
def validate_example(ex: Example) -> None:
"""Full structural validation of one assembled example."""
out = ex.output
if out.count("<thinking>") != 1 or out.count("</thinking>") != 1:
raise ValidationError("thinking block malformed")
if "```" in out:
raise ValidationError("markdown fence leaked")
for banned in ("Here is", "As requested", "Explanation:", "Note:"):
# only check the post-thinking region to avoid false positives
post = out.split("</thinking>", 1)[1]
if banned in post:
raise ValidationError(f"banned phrase leaked: {banned!r}")
after = out.split("</thinking>", 1)[1].lstrip("\n")
if not after.startswith(("graph ", "flowchart ")):
raise ValidationError("diagram does not start with graph/flowchart after thinking")
# split graph vs linemap
if "<linemap>" in after:
graph_text, _, rest = after.partition("<linemap>")
if not rest.rstrip().endswith("</linemap>"):
raise ValidationError("linemap not closed")
linemap_body = rest.split("</linemap>", 1)[0].strip("\n")
else:
graph_text, linemap_body = after, ""
defined = validate_mermaid_block(graph_text)
# n_lines of the user code
code_lines = ex.code.splitlines()
n_src = len(code_lines)
if linemap_body:
seen_ids = set()
for row in linemap_body.splitlines():
row = row.strip()
if not row:
continue
mrow = re.match(r"^([A-Za-z][A-Za-z0-9]*):\s*(\d+)(?:-(\d+))?$", row)
if not mrow:
raise ValidationError(f"bad linemap row: {row!r}")
nid, lo, hi = mrow.group(1), int(mrow.group(2)), mrow.group(3)
if nid not in defined:
raise ValidationError(f"linemap references unknown node {nid}")
if nid in seen_ids:
raise ValidationError(f"duplicate linemap entry for {nid}")
seen_ids.add(nid)
if lo < 1 or lo > n_src:
raise ValidationError(f"linemap line {lo} out of range 1..{n_src}")
if hi is not None and (int(hi) < lo or int(hi) > n_src):
raise ValidationError(f"linemap range {lo}-{hi} invalid (max {n_src})")
# the user code lines must all carry the "N| " prefix
for i, ln in enumerate(code_lines, 1):
if not ln.startswith(f"{i}| ") and ln != f"{i}|":
raise ValidationError(f"line {i} not properly prefixed: {ln!r}")
|