Instructions to use bfuzzy1/TinyGuide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use bfuzzy1/TinyGuide with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("bfuzzy1/TinyGuide") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use bfuzzy1/TinyGuide with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "bfuzzy1/TinyGuide"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "bfuzzy1/TinyGuide" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bfuzzy1/TinyGuide with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "bfuzzy1/TinyGuide"
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 bfuzzy1/TinyGuide
Run Hermes
hermes
- OpenClaw new
How to use bfuzzy1/TinyGuide with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "bfuzzy1/TinyGuide"
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 "bfuzzy1/TinyGuide" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- MLX LM
How to use bfuzzy1/TinyGuide with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "bfuzzy1/TinyGuide"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "bfuzzy1/TinyGuide" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bfuzzy1/TinyGuide", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 9,306 Bytes
78d2164 | 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 | """Build TinyGuide dataset from RAW Claude Code transcripts (not skeletons).
Raw transcripts keep full tool inputs AND results — tracebacks, grep output,
test pass/fail, error text — which the skeleton dropped. That lets every rule
fire with real signal. One {prompt, completion} row per tool call.
Usage:
python build_from_raw.py [glob ...]
(default sources: ~/.claude/projects/**/*.jsonl and /tmp/cc*/**/*.jsonl)
"""
import json, re, sys, glob, os, collections, random
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent))
from label_rules import choose_label, HINTS
from format_prompt import format_prompt
random.seed(0)
ROOT = Path(__file__).resolve().parents[1]
OUT = ROOT / "data"
TEST_RE = re.compile(r"\b(pytest|npm test|pnpm test|yarn test|cargo test|go test|bun test|unittest|jest|vitest|tox)\b")
PY_TB_RE = re.compile(r'File "([^"]+\.\w+)", line \d+')
PYTEST_PATH_RE = re.compile(r"\b([\w./-]+\.\w+):\d+\b")
TEST_FAIL_RE = re.compile(r"\b(FAILED|failed|AssertionError|Error|Traceback|\d+ failed|exit code [1-9])", re.I)
TEST_PASS_RE = re.compile(r"\b(\d+ passed|all tests passed|PASSED|\bOK\b|0 failed)\b")
ENV_RE = re.compile(r"command not found|ModuleNotFoundError|No module named|is not recognized|ENOENT|cannot find module|: not found", re.I)
NOTREAD_RE = re.compile(r"not been read yet|Read it first", re.I)
SYMBOL_RE = re.compile(r"(?:NameError|AttributeError|ImportError|cannot import name).*?['\"`](\w+)['\"`]")
def result_text(content):
if isinstance(content, str):
return content
if isinstance(content, list):
return " ".join(x.get("text", "") for x in content if isinstance(x, dict))
return str(content or "")
def iter_pairs(path):
"""Yield (tool_name, input_dict, result_text, is_error) in order."""
pending = {}
try:
rows = [json.loads(l) for l in open(path) if l.strip()]
except Exception:
return
goal = ""
for r in rows:
msg = r.get("message", {}) or {}
content = msg.get("content")
if r.get("type") == "user" and isinstance(content, str) and not goal:
goal = content.strip()
if not isinstance(content, list):
continue
for c in content:
if not isinstance(c, dict):
continue
if c.get("type") == "text" and not goal and r.get("type") == "user":
goal = c.get("text", "").strip()
if c.get("type") == "tool_use":
pending[c.get("id")] = (c.get("name"), c.get("input", {}) or {})
if c.get("type") == "tool_result":
tid = c.get("tool_use_id")
if tid in pending:
name, inp = pending.pop(tid)
yield goal, name, inp, result_text(c.get("content")), bool(c.get("is_error"))
def first_path(text):
m = PY_TB_RE.search(text) or PYTEST_PATH_RE.search(text)
return m.group(1) if m else None
def session_rows(path):
state = {
"observed_files": [], "edited_files": [], "traceback_paths": [],
"command_history": [], "last_test_status": "unknown",
"code_changed_since_last_test": False, "traceback_symbols": [],
"last_failure_type": None, "last_error_signature": None,
"previous_error_signature": None, "last_search_result_class": None,
"last_test_failed_same_error_after_edit": False,
}
traj, rows, goal_final = [], [], "Continue the task."
for goal, name, inp, res, is_err in iter_pairs(path):
goal_final = goal or goal_final
prop = {"name": name, "args": inp}
# NOTE: no back-to-back cooldown here. Cooldown is a RUNTIME hook
# concern; baking it into labels gives identical signal patterns two
# different labels (VERIFY vs NO_HINT) -> ambiguous supervision ->
# model collapses to NO_HINT on real prompts. Label the true rule.
key = choose_label(state, prop)
prompt = format_prompt(goal_final, traj, state, prop)
rows.append({"key": key, "prompt": prompt, "completion": HINTS[key]})
_advance(state, traj, name, inp, res, is_err)
return rows
def _advance(state, traj, name, inp, res, is_err):
fp = inp.get("file_path")
cmd = inp.get("command", "") or inp.get("pattern", "")
short = (fp or cmd or json.dumps(inp))[:60]
traj.append({"tool": name, "arg": short,
"result": ("ERR " + res[:70]) if is_err else (res[:70] or "ok")})
if len(traj) > 14:
del traj[0]
if name == "Read" and fp and not is_err:
state["observed_files"].append(fp)
if name in {"Edit", "Write", "MultiEdit", "NotebookEdit"} and fp:
if NOTREAD_RE.search(res):
return # failed edit; nothing changed, state already triggered the hint
state["edited_files"].append(fp)
if fp not in state["observed_files"]:
state["observed_files"].append(fp)
state["code_changed_since_last_test"] = True
if name in {"Grep", "Glob"}:
n = len(re.findall(r"\n", res))
state["last_search_result_class"] = ("search_no_results" if not res.strip()
else "search_many_results" if n > 30 else "search_few")
if name == "Bash":
state["command_history"].append(cmd)
sig = (res.strip().splitlines() or [""])[-1][:80]
# failure type reflects only THIS command's result (not sticky)
state["last_failure_type"] = "missing_package" if ENV_RE.search(res) else None
if TEST_RE.search(cmd):
if TEST_FAIL_RE.search(res) and not TEST_PASS_RE.search(res):
state["last_test_status"] = "failed"
tb = first_path(res)
if tb:
state["traceback_paths"].append(tb)
sym = SYMBOL_RE.search(res)
if sym:
state["traceback_symbols"].append(sym.group(1))
state["last_test_failed_same_error_after_edit"] = (sig == state.get("last_error_signature"))
else:
state["last_test_status"] = "passed"
state["last_failure_type"] = None
state["code_changed_since_last_test"] = False
if is_err or TEST_FAIL_RE.search(res):
state["previous_error_signature"] = state.get("last_error_signature")
state["last_error_signature"] = sig
def main():
pats = sys.argv[1:] or [
os.path.expanduser("~/.claude/projects/**/*.jsonl"),
"/tmp/cc*/**/*.jsonl", "/tmp/cc*/*.jsonl",
"/tmp/mimo/**/*.jsonl",
]
files = sorted({f for p in pats for f in glob.glob(p, recursive=True)})
print(f"transcripts found: {len(files)}")
sessions = []
for f in files:
rs = session_rows(f)
if rs:
sessions.append((Path(f).stem, rs))
print(f"sessions with tool calls: {len(sessions)}")
random.shuffle(sessions)
split = int(len(sessions) * 0.9)
def emit(sess, path, balance):
allrows = [r for _sid, rs in sess for r in rs]
# drop consecutive duplicate hints within nothing -> dedup globally per session done below
hints = [r for r in allrows if r["key"] != "NO_HINT"]
nohint = [r for r in allrows if r["key"] == "NO_HINT"]
random.shuffle(hints); random.shuffle(nohint)
if balance and hints:
# cap any single hint class to 35% of hints to avoid one rule dominating
cap = max(50, int(len(hints) * 0.35))
seen = collections.Counter(); kept = []
for r in hints:
if seen[r["key"]] < cap:
kept.append(r); seen[r["key"]] += 1
hints = kept
# target ~75% NO_HINT
keep_no = min(len(nohint), int(len(hints) / 0.25 * 0.75))
nohint = nohint[:keep_no]
out = hints + nohint
random.shuffle(out)
with open(path, "w") as fh:
for r in out:
fh.write(json.dumps({"prompt": r["prompt"], "completion": r["completion"]}) + "\n")
return out
OUT.mkdir(exist_ok=True)
tr = emit(sessions[:split], OUT / "train.jsonl", balance=True)
va = emit(sessions[split:], OUT / "valid.jsonl", balance=True)
# dump ALL rows (unbalanced, with key), session-split preserved, for build_scaled.py
def dump_all(sess, path):
with open(path, "w") as fh:
for sid, rs in sess:
for r in rs:
fh.write(json.dumps({"key": r["key"], "prompt": r["prompt"],
"completion": r["completion"],
"source": "real", "session_id": sid}) + "\n")
dump_all(sessions[:split], OUT / "all_train.jsonl")
dump_all(sessions[split:], OUT / "all_valid.jsonl")
def dist(rows):
c = collections.Counter("NO_HINT" if r["completion"] == "NO_HINT" else "HINT" for r in rows)
return dict(c)
print(f"train rows: {len(tr)} {dist(tr)}")
print(f"valid rows: {len(va)} {dist(va)}")
spread = collections.Counter(r["completion"][:34] for _sid, rs in sessions for r in rs)
print("label spread (all sessions, pre-balance):")
for k, n in spread.most_common():
print(f" {n:6d} {k}")
if __name__ == "__main__":
main()
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