คู่มือ Fine-Tuning LLM สำหรับ Tool-Calling ด้วย XYZ-Aquila-SFT และ Qwen3

import os, sys, subprocess
CFG = dict(
REPO = "XYZAILab/XYZ-Aquila-SFT",
LANG = "en",
N_STREAM = 400,
N_EVAL = 40,
MODEL_ID = "Qwen/Qwen3-0.6B",
MAX_SEQ_LEN = 2048,
LENGTH_POLICY = "truncate",
RUN_TRAINING = True,
MAX_STEPS = 30,
GRAD_ACCUM = 8,
LR = 1e-4,
LORA_R = 16,
RUN_EVAL = True,
N_EVAL_PROBES = 24,
OUT_DIR = "/content/aquila_out",
SEED = 0,
)
os.makedirs(CFG["OUT_DIR"], exist_ok=True)
def pip(*pkgs):
subprocess.run([sys.executable, "-m", "pip", "install", "-q", "-U", *pkgs], check=False)
pip("datasets>=3.0.0", "transformers>=4.51.0", "peft>=0.13.0", "accelerate>=1.0.0")
import json, re, math, random, statistics as stats
from collections import Counter, defaultdict
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
import torch
import matplotlib.pyplot as plt
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, get_cosine_schedule_with_warmup
random.seed(CFG["SEED"]); torch.manual_seed(CFG["SEED"])
DEV = "cuda" if torch.cuda.is_available() else "cpu"
BF16 = DEV == "cuda" and torch.cuda.is_bf16_supported()
print(f"device={DEV} bf16={BF16} torch={torch.__version__}")
print(f"\n[1] streaming {CFG['REPO']}:{CFG['LANG']} ...")
stream = load_dataset(CFG["REPO"], CFG["LANG"], split="train", streaming=True)
RAW: List[Dict[str, Any]] = list(stream.take(CFG["N_STREAM"]))
print(f" pulled {len(RAW)} rows; keys = {list(RAW[0].keys())}")
_r = RAW[0]
print(f" question[:110] : {_r['question'][:110]}...")
print(f" answer : {_r['answer'][:80]}")
print(f" number of tool calls : {_r['number of tool calls']}")
print(f" trajectory len : {len(_r['trajectory'])} msgs")
print(f" role sequence (first8): {[m['role'] for m in _r['trajectory'][:8]]}")

TOOLS_BLOCK_RE = re.compile(r"<tools>\s*(.*?)\s*</tools>", re.S)
THINK_RE = re.compile(r"<think>(.*?)</think>", re.S)
TOOL_RESP_RE = re.compile(r"<tool_response>\s*(.*?)\s*</tool_response>", re.S)
TOOLS_HDR_RE = re.compile(r"\n\n# Tools\n\n")
def iter_json_objects(text: str, limit: int = 1):
"""Nesting-safe JSON scanner. Regex like r'\\{.*?\\}' breaks on nested
`arguments` objects, which every real tool call has."""
dec, i, n, out = json.JSONDecoder(), 0, len(text), []
while i < n and len(out) < limit:
while i < n and text[i] not in "{[":
i += 1
if i >= n:
break
try:
obj, end = dec.raw_decode(text, i)
except json.JSONDecodeError:
i += 1
continue
out.append(obj); i = end
return out
def parse_tool_calls(content: str) -> List[Dict[str, Any]]:
calls = []
for m in re.finditer(r"<tool_call>", content):
got = iter_json_objects(content[m.end():], limit=1)
if got:
calls.append(got[0])
return calls
@dataclass
class Trajectory:
question: str
answer: str
declared_calls: int
messages: List[Dict[str, str]]
system_core: str = ""
tools: List[Dict[str, Any]] = field(default_factory=list)
tools_suffix: str = ""
calls: List[Dict[str, Any]] = field(default_factory=list)
n_observations: int = 0
n_think: int = 0
@property
def tool_names(self): return [c.get("name", "?") for c in self.calls]
@property
def depth(self): return len(self.messages)
def parse_row(row: Dict[str, Any]) -> Trajectory:
msgs = [{"role": m["role"], "content": m["content"]} for m in row["trajectory"]]
t = Trajectory(row["question"], row["answer"], row["number of tool calls"], msgs)
if msgs and msgs[0]["role"] == "system":
sysmsg = msgs[0]["content"]
split = TOOLS_HDR_RE.search(sysmsg)
if split:
t.system_core = sysmsg[:split.start()]
t.tools_suffix = sysmsg[split.start():]
else:
t.system_core = sysmsg
blk = TOOLS_BLOCK_RE.search(sysmsg)
if blk:
t.tools = iter_json_objects(blk.group(1), limit=64)
for m in msgs:
if m["role"] == "assistant":
t.calls += parse_tool_calls(m["content"])
t.n_think += len(THINK_RE.findall(m["content"]))
else:
t.n_observations += len(TOOL_RESP_RE.findall(m["content"]))
return t
TRAJ = [parse_row(r) for r in RAW]
t0 = TRAJ[0]
print(f"\n[2] parsed {len(TRAJ)} trajectories")
print(f" tool schemas found : {[fn.get('function', fn).get('name') for fn in t0.tools]}")
print(f" parsed calls : {len(t0.calls)} (declared {t0.declared_calls})")
print(f" observations : {t0.n_observations} think blocks: {t0.n_think}")
if t0.calls:
print(f" sample call : {json.dumps(t0.calls[0], ensure_ascii=False)[:200]}")
agree = sum(len(t.calls) == t.declared_calls for t in TRAJ)
print(f" parser vs 'number of tool calls': {agree}/{len(TRAJ)} exact match")
calls_per = [len(t.calls) for t in TRAJ]
depth_per = [t.depth for t in TRAJ]
chars_per = [sum(len(m["content"]) for m in t.messages) for t in TRAJ]
name_freq = Counter(n for t in TRAJ for n in t.tool_names)
argkey_freq = defaultdict(Counter)
for t in TRAJ:
for c in t.calls:
args = c.get("arguments", {})
if isinstance(args, dict):
for k in args: argkey_freq[c.get("name", "?")][k] += 1
def q(xs, p):
xs = sorted(xs); return xs[min(len(xs) - 1, int(p * len(xs)))]
print("\n[3] corpus statistics")
print(f" tool calls / traj : mean {stats.mean(calls_per):.1f} p50 {q(calls_per,.5)} "
f"p90 {q(calls_per,.9)} max {max(calls_per)}")
print(f" messages / traj : mean {stats.mean(depth_per):.1f} p90 {q(depth_per,.9)} max {max(depth_per)}")
print(f" chars / traj : mean {stats.mean(chars_per):,.0f} p90 {q(chars_per,.9):,}")
print(f" tool distribution : {dict(name_freq)}")
for k, v in argkey_freq.items():
print(f" {k:<24} arg keys -> {dict(v.most_common(6))}")
tot = sum(chars_per); top = sum(sorted(chars_per)[-max(1, len(chars_per)//10):])
print(f" top-10% longest trajectories hold {100*top/tot:.1f}% of all characters")
fig, ax = plt.subplots(1, 3, figsize=(15, 3.6))
ax[0].hist(calls_per, bins=40); ax[0].set_yscale("log"); ax[0].set_title("tool calls / trajectory")
ax[1].hist(depth_per, bins=40); ax[1].set_yscale("log"); ax[1].set_title("messages / trajectory")
ax[2].bar(list(name_freq), list(name_freq.values())); ax[2].set_title("tool usage"); ax[2].tick_params(axis="x", rotation=20)
plt.tight_layout(); plt.show()

QWEN3_TOOLS_TMPL = (
"You are provided with function signatures within <tools></tools> XML tags:\n<tools>\n"
"{lines}\n</tools>\n\nFor each function call, return a json object with function name "
"and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n"
'{{"name": <function-name>, "arguments": <args-json-object>}}\n</tool_call>'
)
def extract_tools(t: Trajectory) -> Dict[str, Any]:
"""message-embedded schemas -> {'messages': [...], 'tools': [...]}"""
msgs = [dict(m) for m in t.messages]
if msgs and msgs[0]["role"] == "system":
msgs[0]["content"] = t.system_core
return {"messages": msgs, "tools": t.tools,
"question": t.question, "answer": t.answer}
def render_tools(rec: Dict[str, Any]) -> List[Dict[str, str]]:
"""inverse: structured tools -> schemas re-embedded in the system message"""
msgs = [dict(m) for m in rec["messages"]]
if rec["tools"] and msgs and msgs[0]["role"] == "system":
lines = "\n".join(json.dumps(x, ensure_ascii=False) for x in rec["tools"])
msgs[0]["content"] = msgs[0]["content"] + QWEN3_TOOLS_TMPL.format(lines=lines)
return msgs
_rt = render_tools(extract_tools(t0))
exact = _rt[0]["content"] == t0.messages[0]["content"]
print(f"\n[4] extract->render byte-exact: {exact}")
if not exact:
print(" template drift detected -> using verbatim tools_suffix for render()")
a, b = t0.messages[0]["content"], _rt[0]["content"]
i = next((i for i in range(min(len(a), len(b))) if a[i] != b[i]), min(len(a), len(b)))
print(f" first divergence @{i}: {a[i:i+70]!r} vs {b[i:i+70]!r}")
tok = AutoTokenizer.from_pretrained(CFG["MODEL_ID"])
if tok.pad_token is None:
tok.pad_token = tok.eos_token
IM_START, IM_END, NL = "<|im_start|>", "<|im_end|>", "\n"
def render_and_mask(t: Trajectory, max_len: int, policy: str):
"""Manual ChatML so we control masking token-exactly.
WHY NOT apply_chat_template(): Qwen3's template deletes <think>...</think>
from every assistant turn except the last. On this dataset that silently
destroys most of the reasoning supervision you are paying to train on.
"""
ids, labels = [], []
for m in t.messages:
head = tok(f"{IM_START}{m['role']}{NL}", add_special_tokens=False).input_ids
body = tok(m["content"], add_special_tokens=False).input_ids
tail = tok(f"{IM_END}{NL}", add_special_tokens=False).input_ids
seg = head + body + tail
if m["role"] == "assistant":
lab = [-100] * len(head) + body + tail
else:
lab = [-100] * len(seg)
ids += seg; labels += lab
if len(ids) > max_len:
if policy == "drop":
return None
ids, labels = ids[:max_len], labels[:max_len]
if all(l == -100 for l in labels):
return None
return {"input_ids": ids, "labels": labels}
_probe = [{"role": "system", "content": "S"}, {"role": "user", "content": "U"},
{"role": "assistant", "content": "A"}]
_mine = "".join(f"{IM_START}{m['role']}{NL}{m['content']}{IM_END}{NL}" for m in _probe)
_theirs = tok.apply_chat_template(_probe, tokenize=False, add_generation_prompt=False)
print(f"\n[5] manual ChatML == chat_template on tool-free probe: {_mine == _theirs}")
if _mine != _theirs:
print(f" mine : {_mine!r}\n theirs: {_theirs!r} (informational only)")
ENC = [e for e in (render_and_mask(t, CFG["MAX_SEQ_LEN"], CFG["LENGTH_POLICY"]) for t in TRAJ) if e]
sup = [sum(1 for x in e["labels"] if x != -100) / len(e["labels"]) for e in ENC]
print(f" encoded {len(ENC)}/{len(TRAJ)} examples")
print(f" supervised-token ratio: mean {stats.mean(sup):.3f} p10 {q(sup,.1):.3f} p90 {q(sup,.9):.3f}")
over = sum(1 for t in TRAJ if sum(len(tok(m['content'], add_special_tokens=False).input_ids)
for m in t.messages[:3]) > CFG["MAX_SEQ_LEN"])
print(f" trajectories whose first 3 msgs alone exceed MAX_SEQ_LEN: {over}")
SPLIT = len(ENC) - min(CFG["N_EVAL"], len(ENC)//5)
TRAIN_ENC, EVAL_TRAJ = ENC[:SPLIT], TRAJ[SPLIT:]
class SFTSet(torch.utils.data.Dataset):
def __init__(self, rows): self.rows = rows
def __len__(self): return len(self.rows)
def __getitem__(self, i): return self.rows[i]
def collate(batch):
L = max(len(b["input_ids"]) for b in batch)
pad = tok.pad_token_id
return {
"input_ids": torch.tensor([b["input_ids"] + [pad]*(L-len(b["input_ids"])) for b in batch]),
"labels": torch.tensor([b["labels"] + [-100]*(L-len(b["labels"])) for b in batch]),
"attention_mask": torch.tensor([[1]*len(b["input_ids"]) + [0]*(L-len(b["input_ids"])) for b in batch]),
}
loader = torch.utils.data.DataLoader(SFTSet(TRAIN_ENC), batch_size=1, shuffle=True, collate_fn=collate)
print(f"\n[6] train={len(TRAIN_ENC)} eval_trajectories={len(EVAL_TRAJ)}")

def build_probes(trajs, n):
"""Teacher-forced probes: cut the trajectory right before an assistant turn
that issues a tool call; the gold label is that call."""
probes = []
for t in trajs:
for i, m in enumerate(t.messages):
if m["role"] != "assistant":
continue
gold = parse_tool_calls(m["content"])
if not gold:
continue
prefix = "".join(f"{IM_START}x['role']{NL}" for x in [])
prefix = "".join(f"{IM_START}{p['role']}{NL}{p['content']}{IM_END}{NL}"
for p in t.messages[:i]) + f"{IM_START}assistant{NL}"
if len(tok(prefix, add_special_tokens=False).input_ids) > CFG["MAX_SEQ_LEN"] - 160:
continue
probes.append({"prefix": prefix, "gold": gold[0]})
break
if len(probes) >= n:
break
return probes
@torch.no_grad()
def eval_tool_calls(model, probes, tag):
model.eval()
name_hit = arg_f1 = parsed = 0
for p in probes:
enc = tok(p["prefix"], return_tensors="pt", add_special_tokens=False).to(model.device)
out = model.generate(**enc, max_new_tokens=160, do_sample=False,
pad_token_id=tok.pad_token_id)
gen = tok.decode(out[0][enc.input_ids.shape[1]:], skip_special_tokens=True)
pred = (parse_tool_calls(gen) or iter_json_objects(gen, limit=1) or [None])[0]
if not isinstance(pred, dict):
continue
parsed += 1
g = p["gold"]
name_hit += int(pred.get("name") == g.get("name"))
pk = set((pred.get("arguments") or {}).keys()) if isinstance(pred.get("arguments"), dict) else set()
gk = set((g.get("arguments") or {}).keys()) if isinstance(g.get("arguments"), dict) else set()
if pk or gk:
inter = len(pk & gk)
arg_f1 += 0.0 if inter == 0 else 2*inter/(len(pk)+len(gk))
n = max(1, len(probes))
print(f" [{tag}] parseable {parsed}/{n} | tool-name acc {name_hit/n:.3f} | arg-key F1 {arg_f1/n:.3f}")
return dict(parsed=parsed/n, name_acc=name_hit/n, arg_f1=arg_f1/n)
PROBES = build_probes(EVAL_TRAJ, CFG["N_EVAL_PROBES"])
print(f" built {len(PROBES)} teacher-forced probes")
results = {}
if CFG["RUN_TRAINING"]:
from peft import LoraConfig, get_peft_model
dtype = torch.bfloat16 if BF16 else torch.float32
model = AutoModelForCausalLM.from_pretrained(
CFG["MODEL_ID"], torch_dtype=dtype, attn_implementation="sdpa").to(DEV)
model.config.use_cache = False
model.gradient_checkpointing_enable()
model.enable_input_require_grads()
if CFG["RUN_EVAL"] and PROBES and DEV == "cuda":
print("\n[8] baseline eval")
results["before"] = eval_tool_calls(model, PROBES, "base")
model = get_peft_model(model, LoraConfig(
r=CFG["LORA_R"], lora_alpha=2*CFG["LORA_R"], lora_dropout=0.05,
bias="none", task_type="CAUSAL_LM",
))
model.print_trainable_parameters()
opt = torch.optim.AdamW([p for p in model.parameters() if p.requires_grad],
lr=CFG["LR"], weight_decay=0.0, betas=(0.9, 0.95))
sched = get_cosine_schedule_with_warmup(opt, 5, CFG["MAX_STEPS"])
scaler = torch.amp.GradScaler("cuda", enabled=(DEV == "cuda" and not BF16))
amp_dt = torch.bfloat16 if BF16 else torch.float16
print(f"\n[7] training {CFG['MAX_STEPS']} steps "
f"(bs1 x accum{CFG['GRAD_ACCUM']} = {CFG['GRAD_ACCUM']} traj/step)")
model.train(); step = 0; run = None; it = iter(loader)
while step < CFG["MAX_STEPS"]:
opt.zero_grad(set_to_none=True); acc = 0.0
for _ in range(CFG["GRAD_ACCUM"]):
try: batch = next(it)
except StopIteration:
it = iter(loader); batch = next(it)
batch = {k: v.to(DEV) for k, v in batch.items()}
with torch.autocast(DEV, dtype=amp_dt, enabled=(DEV == "cuda")):
loss = model(**batch).loss / CFG["GRAD_ACCUM"]
scaler.scale(loss).backward() if scaler.is_enabled() else loss.backward()
acc += loss.item()
if scaler.is_enabled():
scaler.unscale_(opt)
(scaler.step(opt), scaler.update()) if scaler.is_enabled() else opt.step()
sched.step(); step += 1
run = acc if run is None else 0.9*run + 0.1*acc
if step % 5 == 0 or step == 1:
print(f" step {step:>3}/{CFG['MAX_STEPS']} loss {acc:.4f} ema {run:.4f} "
f"lr {sched.get_last_lr()[0]:.2e} ppl {math.exp(min(20, acc)):.1f}")
model.save_pretrained(f"{CFG['OUT_DIR']}/lora_adapter"); tok.save_pretrained(f"{CFG['OUT_DIR']}/lora_adapter")
print(f" adapter -> {CFG['OUT_DIR']}/lora_adapter")
if CFG["RUN_EVAL"] and PROBES and DEV == "cuda":
print("\n[8] post-training eval")
model.config.use_cache = True
results["after"] = eval_tool_calls(model, PROBES, "lora")
model.config.use_cache = False
if "before" in results and "after" in results:
print("\n delta:", {k: round(results['after'][k] - results['before'][k], 3)
for k in results['after']})
print(" (30 steps on ~350 trajectories is a smoke test, not a result — "
"expect noise, and scale N_STREAM/MAX_STEPS for anything real.)")

struct_path = f"{CFG['OUT_DIR']}/aquila_{CFG['LANG']}_structured_tools.jsonl"
with open(struct_path, "w", encoding="utf-8") as f:
for t in TRAJ:
f.write(json.dumps(extract_tools(t), ensure_ascii=False) + "\n")
stats_path = f"{CFG['OUT_DIR']}/corpus_stats.json"
with open(stats_path, "w") as f:
json.dump({"n": len(TRAJ), "tool_freq": dict(name_freq),
"calls_mean": stats.mean(calls_per), "calls_max": max(calls_per),
"depth_p90": q(depth_per, .9), "encoded": len(ENC),
"supervised_ratio_mean": stats.mean(sup), "eval": results}, f, indent=2)
print(f"\n[9] wrote:\n {struct_path}\n {stats_path}")
print("done.")