In this tutorial, we implement an end-to-end supervised fine-tuning pipeline for the XYZ-Aquila-SFT dataset, Hugging Face Transformers, PyTorch, and PEFT. We stream and inspect the dataset, parse multi-turn tool-use trajectories, extract structured tool calls, analyze corpus characteristics, and preserve embedded reasoning and observation patterns. We then convert tool schemas between message-embedded and structured formats, render Qwen-compatible ChatML with assistant-only loss masking, prepare a custom PyTorch dataset and collator, and fine-tune Qwen3-0.6B with LoRA. Finally, we evaluate tool-call prediction before and after training and export both the transformed dataset and corpus statistics for further experimentation.
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]]}”)
We configure the dataset, model, training parameters, output directory, and reproducibility settings for the complete workflow. We install the required Hugging Face, PEFT, Accelerate, and PyTorch-related dependencies and detect whether a CUDA GPU and BF16 support are available. We then stream a limited number of XYZ-Aquila-SFT examples, inspect the dataset schema, and examine the structure of the first tool-use trajectory.
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()
We define nesting-safe utilities for extracting JSON tool calls, reasoning blocks, observations, and embedded tool schemas from each conversation. We convert every raw dataset row into a structured trajectory object and verify that the parsed tool-call counts match the values declared by the dataset. We then calculate corpus-level statistics and visualize the distributions of tool calls, message depth, trajectory size, and tool usage frequency.
“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)}”)
We extract embedded tool definitions into a structured format and reconstruct them to test whether the conversion preserves the original system message. We manually render each trajectory in ChatML format to retain all reasoning content and apply loss only to assistant-generated tokens. We also tokenize the examples, enforce the selected sequence-length policy, create the training and evaluation split, and prepare a padded PyTorch DataLoader.
“””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.)”)
We build teacher-forced evaluation probes by cutting trajectories immediately before assistant turns that contain tool calls. We load Qwen3-0.6B, measure its baseline tool-call performance, attach LoRA adapters, and fine-tune the model using gradient accumulation, mixed precision, checkpointing, clipping, and cosine learning-rate scheduling. We then evaluate the adapted model, compare its metrics with the baseline, and save the trained LoRA adapter and tokenizer.
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.”)
We export every parsed trajectory as a structured JSONL record containing messages, tool schemas, questions, and answers. We also save a JSON report containing corpus size, tool frequencies, trajectory statistics, supervised-token ratios, and available evaluation results. We finish the workflow with reusable dataset artifacts, analytical outputs, and model files stored in the configured output directory.
In conclusion, we completed a practical pipeline for analyzing, transforming, fine-tuning, and evaluating complex tool-use trajectories from the XYZ-Aquila-SFT dataset. We preserved the original conversational structure, applied token-level supervision only to assistant responses, and used LoRA to adapt Qwen3-0.6B efficiently on a Colab-compatible GPU. We also compared baseline and post-training tool-call performance through teacher-forced evaluation and exported reusable structured records, model adapters, and analytical statistics. This workflow gives us a strong foundation for scaling tool-aware supervised fine-tuning, testing alternative sequence-length policies, and training more capable agentic language models.
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Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.
