File size: 1,841 Bytes
e8eb104 | 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 | """Tool-call calibration conversations: NousResearch/hermes-function-calling-v1 (func-calling.json,
rev dae3e1d), parsed into structured tool_calls and rendered through the model's own chat template
with tools=, so the Hessians see the served <tools> system prompt and <tool_call><function=...> turns.
No BFCL data. Import render(tok, path) -> list of rendered strings, in file order."""
import json, re
CALL = re.compile(r"<tool_call>\s*(\{.*?\})\s*</tool_call>", re.S)
RESP = re.compile(r"<tool_response>\s*(.*?)\s*</tool_response>", re.S)
def convert(r):
tools = json.loads(r["tools"]) if isinstance(r["tools"], str) else r["tools"]
msgs = []
for m in r["conversations"]:
v = m["value"]
if m["from"] == "system":
continue # the template writes its own tools system prompt
if m["from"] == "human":
msgs.append({"role": "user", "content": v})
elif m["from"] == "gpt":
calls = [json.loads(c) for c in CALL.findall(v)]
if calls:
msgs.append({"role": "assistant", "content": CALL.sub("", v).strip(),
"tool_calls": [{"type": "function", "function": {"name": c["name"], "arguments": c.get("arguments", {})}} for c in calls]})
else:
msgs.append({"role": "assistant", "content": v})
elif m["from"] == "tool":
for body in RESP.findall(v) or [v]:
msgs.append({"role": "tool", "content": body})
return tools, msgs
def render(tok, path, **kw):
out = []
for r in json.load(open(path)):
try:
tools, msgs = convert(r)
out.append(tok.apply_chat_template(msgs, tools=tools, tokenize=False, **kw))
except (ValueError, KeyError, TypeError, json.JSONDecodeError):
continue
return out
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