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Model: srikarkashyap/iol-ai-2026-baseline Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-7B-Instruct
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tags:
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- iol-ai-2026
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---
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# IOL-AI 2026 submission (v3)
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Submission for the [IOL-AI 2026 challenge](https://iolai.org/):
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Qwen2.5-7B-Instruct quantized to 4-bit (bitsandbytes NF4) at load time,
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greedy decoding, with per-item prompting.
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v3: 7B in 4-bit instead of 1.5B (fp16 fallback if bitsandbytes is missing).
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v2 changes over the plain baseline:
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- one generation per numbered item instead of one per problem
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- brief step-by-step reasoning, answer extracted from a `FINAL:` line
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- task-type-specific answer-format instructions
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- a one-shot worked example in the prompt
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- a global time budget that shrinks reasoning space near the 30-min limit
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- `script.py` reads `/tmp/data/test.csv` and writes `submission.csv`
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(columns `id`, `pred`, where `pred` is a JSON list of per-item answers).
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- Model weights are shipped in this repo and loaded from `"."` because the
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eval sandbox has no internet.
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Base model: [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
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(Apache 2.0).
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"model_type": "qwen2",
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"num_hidden_layers": 28,
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"rms_norm_eps": 1e-06,
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}
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"top_k": 20,
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}
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"model.layers.7.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.bias": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
244
script.py
Normal file
244
script.py
Normal file
@@ -0,0 +1,244 @@
|
||||
"""IOL-AI 2026 v3: Qwen2.5-7B-Instruct in 4-bit, per-item prompting + reasoning.
|
||||
|
||||
Runs inside the competition sandbox (no internet). The submission repo is the
|
||||
working directory; model weights are shipped in it and loaded from ".".
|
||||
Reads /tmp/data/test.csv, writes submission.csv (columns: id, pred).
|
||||
|
||||
v3 over v2: Qwen2.5-7B-Instruct instead of 1.5B, quantized to 4-bit (NF4) at
|
||||
load time with bitsandbytes. (AWQ was a dead end: autoawq pins an exact torch
|
||||
version and the sandbox's package index serves no torch wheels at all, so the
|
||||
preinstalled torch must be used as-is.)
|
||||
|
||||
v2 over the baseline:
|
||||
- One generation per numbered item (not one per problem), so a misparsed line
|
||||
can't shift every later answer.
|
||||
- The model reasons first, then emits "FINAL: <answer>"; only that is kept.
|
||||
- Task-type-specific answer-format instructions (exact match is unforgiving).
|
||||
- A one-shot worked example demonstrating the reasoning + FINAL format.
|
||||
- A global time budget that shrinks reasoning space as the deadline nears.
|
||||
"""
|
||||
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
|
||||
START_TIME = time.time()
|
||||
|
||||
# IOL_SKIP_PIP=1 skips dependency install for local testing (where torch etc.
|
||||
# are already set up); the sandbox always installs.
|
||||
if os.environ.get("IOL_SKIP_PIP") != "1":
|
||||
# Do NOT list torch here: the sandbox's package index has no torch wheels
|
||||
# (any resolver attempt to fetch one fails), so the preinstalled torch
|
||||
# must satisfy every requirement as-is.
|
||||
subprocess.run(
|
||||
[sys.executable, "-m", "pip", "install", "-q",
|
||||
"transformers>=4.43", "accelerate>=0.30", "pandas"],
|
||||
check=True,
|
||||
)
|
||||
# bitsandbytes installed separately and non-fatally: if the sandbox index
|
||||
# lacks it, the script falls back to fp16 loading below.
|
||||
subprocess.run(
|
||||
[sys.executable, "-m", "pip", "install", "-q", "bitsandbytes"],
|
||||
check=False,
|
||||
)
|
||||
|
||||
os.environ["HF_HUB_OFFLINE"] = "1"
|
||||
os.environ["TRANSFORMERS_OFFLINE"] = "1"
|
||||
|
||||
# IOL_TEST_CSV / IOL_MODEL_ID are only for local testing; in the sandbox the
|
||||
# defaults are used.
|
||||
TEST_CSV = os.environ.get("IOL_TEST_CSV", "/tmp/data/test.csv")
|
||||
MODEL_ID = os.environ.get("IOL_MODEL_ID", ".")
|
||||
OUT_CSV = os.environ.get("IOL_OUT_CSV", "submission.csv")
|
||||
# Stop reasoning generously before the sandbox's 30-minute hard kill; the
|
||||
# budget also has to cover pip install and model load (already elapsed).
|
||||
TIME_BUDGET_S = int(os.environ.get("IOL_TIME_BUDGET_S", 25 * 60))
|
||||
|
||||
import json
|
||||
import re
|
||||
|
||||
# Progress prints contain IPA characters; don't let a non-UTF-8 console
|
||||
# (Windows local testing) crash the run.
|
||||
sys.stdout.reconfigure(encoding="utf-8", errors="replace")
|
||||
|
||||
import pandas as pd
|
||||
import torch
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
|
||||
|
||||
SYSTEM_PROMPT = (
|
||||
"You solve International Linguistics Olympiad problems: given a few "
|
||||
"examples from an unfamiliar language, you deduce its rules and apply "
|
||||
"them. Think step by step, briefly. Then give your answer on a new line "
|
||||
"starting with 'FINAL:'. The FINAL line must contain only the answer "
|
||||
"itself: no explanation, no quotes, no numbering."
|
||||
)
|
||||
|
||||
# One worked example (Hakhun, from the competition page) demonstrating the
|
||||
# reasoning style and the FINAL format.
|
||||
ONE_SHOT_USER = """DATA
|
||||
Here are sentences in Hakhun with their English translations:
|
||||
ŋa ka kɤ ne | Do I go?
|
||||
nɤ ʒip tuʔ ne | Did you sleep?
|
||||
ati ka la | He goes.
|
||||
ŋa ʒip ku | I sleep.
|
||||
|
||||
TASK
|
||||
Translate into English.
|
||||
Answer format: only the translation, nothing else.
|
||||
|
||||
ITEM
|
||||
nɤ ʒip ku ne"""
|
||||
|
||||
ONE_SHOT_ASSISTANT = """From the data: ŋa = I, nɤ = you, ati = he. ka = go, ʒip = sleep. \
|
||||
Sentence-final ne marks a question. The verb suffix differs by subject and tense: \
|
||||
kɤ/ku are present forms (kɤ with ŋa+ka, ku with ŋa+ʒip), tuʔ is past, la is 3rd person present. \
|
||||
Here: nɤ (you) + ʒip (sleep) + ku (present) + ne (question) = present-tense question about you sleeping.
|
||||
FINAL: Do you sleep?"""
|
||||
|
||||
# Answer-format hints per task_type; unknown types get the generic hint.
|
||||
TASK_HINTS = {
|
||||
"translation": "only the translation, nothing else",
|
||||
"fill_blanks": "only the word or words that fill the blank",
|
||||
"match_letters": "only the letter or letters of the matching choice",
|
||||
"text_to_num": "only the number in digits",
|
||||
"num_to_text": "only the number written out in the problem language",
|
||||
}
|
||||
GENERIC_HINT = "only the answer itself, as short as possible"
|
||||
|
||||
# A numbered item looks like "17. ..." or "17) ..." at the start of a line.
|
||||
ITEM_RE = re.compile(r"^\s*\(?(\d+)\s*[.)]\s*", re.MULTILINE)
|
||||
# Models drift to variants like "Final Answer:" or "Answer:"; accept them all.
|
||||
FINAL_RE = re.compile(r"(?:FINAL(?:\s+ANSWER)?|ANSWER)\s*:\s*(.*)", re.IGNORECASE)
|
||||
|
||||
|
||||
def split_query(query: str):
|
||||
"""Split the query into (instruction, [(number, item_text), ...])."""
|
||||
matches = list(ITEM_RE.finditer(query))
|
||||
if not matches:
|
||||
return query.strip(), [("1", query.strip())]
|
||||
instruction = query[: matches[0].start()].strip()
|
||||
items = []
|
||||
for i, m in enumerate(matches):
|
||||
end = matches[i + 1].start() if i + 1 < len(matches) else len(query)
|
||||
items.append((m.group(1), query[m.end(): end].strip()))
|
||||
return instruction, items
|
||||
|
||||
|
||||
QUOTED_RE = re.compile(r"[\"“”'']([^\"“”'']+)[\"“”'']")
|
||||
|
||||
|
||||
def extract_final(text: str) -> str:
|
||||
"""Take the last FINAL: line; fall back to the last non-empty line."""
|
||||
finals = FINAL_RE.findall(text)
|
||||
if finals:
|
||||
return finals[-1].strip().strip('"').strip()
|
||||
lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
|
||||
if not lines:
|
||||
return ""
|
||||
last = lines[-1]
|
||||
# Without a FINAL marker the last line is often prose like
|
||||
# 'The sentence "X" translates to "Y".' — take the last quoted span.
|
||||
quoted = QUOTED_RE.findall(last)
|
||||
if quoted and len(last) > 2 * len(quoted[-1]):
|
||||
return quoted[-1].strip()
|
||||
return last
|
||||
|
||||
|
||||
def main():
|
||||
tok = AutoTokenizer.from_pretrained(MODEL_ID)
|
||||
# NF4 4-bit quantization: ~5 GB on the T4, fp16 compute. Well-supported
|
||||
# on T4 (no bf16 needed) and avoids the fp16-overflow garbage seen when
|
||||
# running full-precision Qwen2.5 in fp16.
|
||||
try:
|
||||
quant = BitsAndBytesConfig(
|
||||
load_in_4bit=True,
|
||||
bnb_4bit_quant_type="nf4",
|
||||
bnb_4bit_compute_dtype=torch.float16,
|
||||
bnb_4bit_use_double_quant=True,
|
||||
)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
MODEL_ID, quantization_config=quant, device_map="auto"
|
||||
).eval()
|
||||
print("Loaded 4-bit (bitsandbytes NF4).", flush=True)
|
||||
except Exception as e:
|
||||
# Last resort if bitsandbytes is unavailable: fp16 barely fits the
|
||||
# 16 GB T4 for 7B and risks Qwen2.5 fp16 overflow, but a degraded
|
||||
# run beats no submission.csv at all.
|
||||
print(f"4-bit load failed ({e!r}); falling back to fp16.", flush=True)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
MODEL_ID, torch_dtype=torch.float16, device_map="auto"
|
||||
).eval()
|
||||
|
||||
df = pd.read_csv(TEST_CSV, dtype=str).fillna("")
|
||||
|
||||
# Count items up front so the time budget can be split evenly.
|
||||
parsed = []
|
||||
total_items = 0
|
||||
for _, r in df.iterrows():
|
||||
instruction, items = split_query(r["query"])
|
||||
parsed.append((r, instruction, items))
|
||||
total_items += len(items)
|
||||
|
||||
def generate(messages, max_new_tokens):
|
||||
prompt = tok.apply_chat_template(
|
||||
messages, add_generation_prompt=True, tokenize=False
|
||||
)
|
||||
enc = tok(prompt, return_tensors="pt").to(model.device)
|
||||
with torch.no_grad():
|
||||
out = model.generate(
|
||||
**enc,
|
||||
max_new_tokens=max_new_tokens,
|
||||
do_sample=False,
|
||||
temperature=None,
|
||||
top_p=None,
|
||||
top_k=None,
|
||||
pad_token_id=tok.eos_token_id,
|
||||
)
|
||||
return tok.decode(
|
||||
out[0][enc["input_ids"].shape[-1]:], skip_special_tokens=True
|
||||
).strip()
|
||||
|
||||
rows = []
|
||||
done_items = 0
|
||||
for r, instruction, items in parsed:
|
||||
hint = TASK_HINTS.get(r["task_type"], GENERIC_HINT)
|
||||
answers = []
|
||||
for num, item_text in items:
|
||||
remaining_s = TIME_BUDGET_S - (time.time() - START_TIME)
|
||||
remaining_items = total_items - done_items
|
||||
# Even share of the remaining budget, assuming ~10 tokens/s for
|
||||
# NF4 7B on a T4; clamp to a sane range. If time has essentially
|
||||
# run out, emit a 32-token answer-only guess.
|
||||
share_s = max(remaining_s, 1) / max(remaining_items, 1)
|
||||
max_new = int(min(384, max(32, share_s * 10)))
|
||||
|
||||
user_msg = (
|
||||
f"DATA\n{r['context'].strip()}\n\n"
|
||||
f"TASK\n{instruction or r['query'].strip()}\n"
|
||||
f"Answer format: {hint}.\n\n"
|
||||
f"ITEM\n{item_text}"
|
||||
)
|
||||
messages = [
|
||||
{"role": "system", "content": SYSTEM_PROMPT},
|
||||
{"role": "user", "content": ONE_SHOT_USER},
|
||||
{"role": "assistant", "content": ONE_SHOT_ASSISTANT},
|
||||
{"role": "user", "content": user_msg},
|
||||
]
|
||||
text = generate(messages, max_new)
|
||||
answer = extract_final(text)
|
||||
answers.append(answer)
|
||||
done_items += 1
|
||||
elapsed = time.time() - START_TIME
|
||||
print(f"[{r['id']}] item {num}: {answer!r} "
|
||||
f"(max_new={max_new}, t={elapsed:.0f}s)", flush=True)
|
||||
|
||||
rows.append({"id": r["id"], "pred": json.dumps(answers, ensure_ascii=False)})
|
||||
|
||||
pd.DataFrame(rows).to_csv(OUT_CSV, index=False)
|
||||
print(f"Wrote {len(rows)} rows to {OUT_CSV} "
|
||||
f"in {time.time() - START_TIME:.0f}s")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
303282
tokenizer.json
Normal file
303282
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
207
tokenizer_config.json
Normal file
207
tokenizer_config.json
Normal file
@@ -0,0 +1,207 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user