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---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE
pipeline_tag: text-generation
base_model: Qwen/Qwen3-14B
---
# Qwen3-14B-AWQ
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-14B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 14.8B
- Number of Paramaters (Non-Embedding): 13.2B
- Number of Layers: 40
- Number of Attention Heads (GQA): 40 for Q and 8 for KV
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
- Quantization: AWQ 4-bit
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-14B-AWQ"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-14B-AWQ --reasoning-parser qwen3
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-14B-AWQ --enable-reasoning --reasoning-parser deepseek_r1
```
Also check out our [AWQ documentation](https://qwen.readthedocs.io/en/latest/quantization/awq.html) for more usage guide.
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="Qwen/Qwen3-14B-AWQ"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> [!NOTE]
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-14B-AWQ',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Processing Long Texts
Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.
YaRN is currently supported by several inference frameworks, e.g., `transformers` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:
- Modifying the model files:
In the `config.json` file, add the `rope_scaling` fields:
```json
{
...,
"rope_scaling": {
"rope_type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
}
```
- Passing command line arguments:
For `vllm`, you can use
```shell
vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
```
For `sglang`, you can use
```shell
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
```
> [!IMPORTANT]
> If you encounter the following warning
> ```
> Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}
> ```
> please upgrade `transformers>=4.51.0`.
> [!NOTE]
> All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.**
> We advise adding the `rope_scaling` configuration only when processing long contexts is required.
> It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0.
> [!NOTE]
> The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.
> [!TIP]
> The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed.
## Performance
| Mode | QUANTIZATION TYPE | LiveBench 2024-11-25 | GPQA | MMLU-Redux | AIME24 |
| --- | --- | --- | --- | --- | --- |
| Thinking | bf16 | 71.3 | 64.0 | 88.6 | 79.3 |
| Thinking | AWQ-int4 | 70.0 | 62.1 | 88.5 | 77.0 |
| Non-Thinking | bf16 | 59.6 | 54.8 | 82.0 | - |
| Non-Thinking | AWQ-int4 | 57.4 | 53.8 | 81.5 | - |
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. **We strongly recommend setting this value to 1.5 for quantized models.** However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3technicalreport,
title={Qwen3 Technical Report},
author={Qwen Team},
year={2025},
eprint={2505.09388},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.09388},
}
```

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 5120,
"initializer_range": 0.02,
"intermediate_size": 17408,
"max_position_embeddings": 40960,
"max_window_layers": 40,
"model_type": "qwen3",
"num_attention_heads": 40,
"num_hidden_layers": 40,
"num_key_value_heads": 8,
"quantization_config": {
"bits": 4,
"group_size": 128,
"modules_to_not_convert": null,
"quant_method": "awq",
"version": "gemm",
"zero_point": true
},
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.51.3",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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generation_config.json Normal file
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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"temperature": 0.6,
"top_k": 20,
"top_p": 0.95,
"transformers_version": "4.51.0"
}

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version https://git-lfs.github.com/spec/v1
oid sha256:668eb0f1356638310db286f4819b223c12e3916934123f1a81b2b2c0e148c6a2
size 4988339832

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version https://git-lfs.github.com/spec/v1
oid sha256:c3c1625df80fe01211038bfa520629ebde6adf776556aa80cd49696d986d6657
size 4988350408

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448
script.py Normal file
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import os, sys, subprocess, importlib, importlib.metadata, unicodedata
from pathlib import Path
os.environ.setdefault("HF_HUB_OFFLINE","1"); os.environ.setdefault("TRANSFORMERS_OFFLINE","1")
SCRIPT_DIR = Path(__file__).resolve().parent if "__file__" in globals() else Path.cwd()
WHEELHOUSE = SCRIPT_DIR / "wheelhouse"
if not WHEELHOUSE.is_dir(): WHEELHOUSE = Path("wheelhouse")
RUNTIME_DIR = Path("/tmp/qwen3deps")
def emergency(reason):
try:
import pandas as pd, json as j
try: ids = pd.read_csv("/tmp/data/test.csv", dtype=str).fillna("")["id"].tolist()
except Exception: ids = []
pd.DataFrame([{"id":i,"pred":j.dumps([""]),"explanation":str(reason)[:100]} for i in ids],
columns=["id","pred","explanation"]).to_csv("submission.csv", index=False)
except Exception:
try: open("submission.csv","w").write("id,pred,explanation\n")
except Exception: pass
try:
wheels = [str(WHEELHOUSE / w) for w in os.listdir(WHEELHOUSE) if w.endswith(".whl")]
if not wheels: raise FileNotFoundError(f"no wheels in {WHEELHOUSE}")
RUNTIME_DIR.mkdir(parents=True, exist_ok=True)
subprocess.run([sys.executable,"-m","pip","install","--no-index","--no-deps","--upgrade",
"--target",str(RUNTIME_DIR)] + wheels, check=True, timeout=300)
sys.path.insert(0, str(RUNTIME_DIR)); importlib.invalidate_caches()
try: print("transformers:", importlib.metadata.version("transformers"), flush=True)
except Exception: pass
except Exception as e:
emergency(f"wheel install failed: {e}"); raise
import re, json, time
import pandas as pd, torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import ast as _ast, hashlib as _hash
from fractions import Fraction as _Frac
from collections import OrderedDict as _OD
MODEL_ID="."; TIME_LIMIT=30*60; start=time.time()
def write_csv(rows):
import csv
with open("submission.csv.tmp","w",newline="",encoding="utf-8") as f:
w=csv.DictWriter(f,fieldnames=["id","pred","explanation"]); w.writeheader()
for r in rows: w.writerow(r)
os.replace("submission.csv.tmp","submission.csv")
try:
df = pd.read_csv("/tmp/data/test.csv", dtype=str).fillna("")
write_csv([{"id":i,"pred":json.dumps([""]),"explanation":"placeholder"} for i in df["id"]])
tok = AutoTokenizer.from_pretrained(MODEL_ID, local_files_only=True)
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype=torch.float16,
device_map="auto", local_files_only=True).eval()
print("loaded, quantized:", getattr(model.config,"quantization_config",None) is not None, flush=True)
except Exception as e:
emergency(f"load failed: {e}"); raise
SYS=("You solve International Linguistics Olympiad problems about a language you have never seen. "
"Everything you need is in the examples. Answer every numbered item, in order. "
"Put each answer on its own line, with no numbering and no extra text.")
def n_expected(query):
items=re.findall(r"(?m)^\s*(\d+)\s*[.\)]", query)
if items: return len(items)
rng=re.search(r"\(\s*(\d+)\s*[-–—]\s*(\d+)\s*\)", query)
if rng:
lo,hi=int(rng.group(1)),int(rng.group(2))
if 0<hi-lo<100: return hi-lo+1
return None
def norm_generic(s):
s = unicodedata.normalize("NFC", s).strip()
s = re.sub(r"^\s*\(?\d+\)?\s*[.):\-]\s+", "", s)
s = re.sub(r"^\s*[-*•·]\s+", "", s)
s = re.sub(r"(?i)^\s*(answer|translation|output)\s*\d*\s*[:.\-]\s+", "", s)
s = s.strip("* ")
s = re.sub(r"\s{2,}", " ", s)
return s.strip()
def norm_number(s):
g = norm_generic(s)
m = re.search(r"-?\d[\d,\. ]*\d|\d", g)
if not m: return g
digits = re.sub(r"[^\d-]", "", m.group(0))
return digits if digits else g
def normalize(task_type, s):
if task_type == "text_to_num":
return norm_number(s)
return norm_generic(s)
def gen(context, query):
msgs=[{"role":"system","content":SYS},
{"role":"user","content":f"{context.strip()}\n\n{query.strip()}"}]
try:
enc=tok.apply_chat_template(msgs,add_generation_prompt=True,enable_thinking=False,
return_tensors="pt",return_dict=True).to(model.device)
ilen=enc["input_ids"].shape[-1]
with torch.no_grad(): out=model.generate(**enc,max_new_tokens=512,do_sample=False)
except Exception:
ids=tok.apply_chat_template(msgs,add_generation_prompt=True,enable_thinking=False,
return_tensors="pt").to(model.device)
ilen=ids.shape[-1]
with torch.no_grad(): out=model.generate(ids,max_new_tokens=512,do_sample=False)
return tok.decode(out[0][ilen:],skip_special_tokens=True).strip()
rows=[]; done=set()
try:
for _,r in df.iterrows():
try:
task_type = r.get("task_type","")
text=gen(r["context"],r["query"])
ans=[normalize(task_type, ln) for ln in text.splitlines() if ln.strip()]
n=n_expected(r["query"])
if n:
if len(ans)<n: ans=ans+[ans[-1] if ans else ""]*(n-len(ans))
elif len(ans)>n: ans=ans[:n]
if not ans: ans=[""]
expl=re.sub(r"\s+"," ",text[:300]).strip() or "derived from the examples"
rows.append({"id":r["id"],"pred":json.dumps(ans,ensure_ascii=False),"explanation":expl})
except Exception as e:
n=n_expected(r["query"]) or 1
rows.append({"id":r["id"],"pred":json.dumps([""]*n,ensure_ascii=False),"explanation":"fallback"})
print("row error",r["id"],e,flush=True)
done.add(r["id"]); write_csv(rows)
print(f"{len(rows)}/{len(df)} t={time.time()-start:.0f}s",flush=True)
if time.time()-start>TIME_LIMIT-60:
print("time up, stopping",flush=True); break
for _,r in df.iterrows():
if r["id"] in done: continue
n=n_expected(r["query"]) or 1
rows.append({"id":r["id"],"pred":json.dumps([""]*n,ensure_ascii=False),"explanation":"fallback"})
# ==========================================================================
# PASS 2: Grammar induction consensus.
# Appended after the proven 0.121 baseline completes. The submission.csv
# already has valid answers at this point. Pass 2 only IMPROVES rows where
# two independent grammar inductions agree exactly -- never empties them.
# ==========================================================================
_P2_INDUCTION_SYS = (
"You study an International Linguistics Olympiad problem. "
"From the examples only, write a RULE SHEET: with 3-8 bullet points "
"covering the grammar: word meanings, word order, morphology, numeral "
"composition, and exact-form constraints. "
"Do not answer the queries. List only rules verifiable from examples."
)
_P2_APPLICATION_SYS = (
"Apply the rule sheet to the IOL queries. "
"Use only the rule sheet and examples. "
"Follow the exact output format. No alternatives, no explanations."
)
_P2_SOFT_DEADLINE = 1620
_P2_RULE_CAP = 90
_P2_APPLY_CAP = 110
_P2_MIN_T = 25
_P2_SAMPLES = 2
_THINK_RE2 = re.compile(r"</?think\b", re.I)
_FINAL_RE2 = re.compile(r"^FINAL\s+ANSWERS\s*:\s*$", re.I)
_CHKRE2 = re.compile(r"^ARITHMETIC\s+CHECKS\s*:\s*$", re.I)
_BEGIN_RE2 = re.compile(r"^BEGIN\s+(ROW_[1-9]\d*)\s*$", re.I)
_END_RE2 = re.compile(r"^END\s+(ROW_[1-9]\d*)\s*$", re.I)
_JUNK2 = re.compile(r"^(?:note|explanation|reason(?:ing)?|answers?|here\s+(?:are|is))\s*:", re.I)
_FMT2 = re.compile(r"^(?:(?:\d{1,3}[.)]|\(\d{1,3}\))(?:\s+|(?=[^\d]))|[-*•]\s+)")
_SAFE_NUM2 = re.compile(r"^[0-9\s.,;+\-*/^=()\[\]{}×÷·\u2212]+$")
_OPT_RE2 = re.compile(r"(?m)^\s*([A-Za-z])[.)]\s")
def _p2_elapsed():
return time.time() - start
def _p2_field(r, k):
v = r.get(k, ""); return "" if v is None else str(v).strip()
def _p2_classify(r):
d = _p2_field(r,"task_type").lower().replace("-","_")
if d in {"translation","fill_blanks","match_letters","text_to_num","num_to_text"}: return d
q = _p2_field(r,"query").lower()
if "fill" in q and "blank" in q: return "fill_blanks"
if "correspondence" in q or ("match" in q and "letter" in q): return "match_letters"
if re.search(r"\bwrite\s+(?:in|as)\s+digits?\b", q): return "text_to_num"
if re.search(r"\bwrite\s+out\b", q): return "num_to_text"
return "translation"
def _p2_n(r):
return n_expected(_p2_field(r,"query"))
def _p2_opt_labels(r):
if _p2_classify(r) != "match_letters": return None
for src in (_p2_field(r,"query"), _p2_field(r,"context")):
labels = _OPT_RE2.findall(src)
numbered = len(re.findall(r"(?m)^\s*\d+[.)]\s+", src))
if len(labels)>=2 and len(set(labels))==len(labels) and numbered==len(labels):
return tuple(labels)
return None
def _p2_safe_eval(expr):
expr = (expr.replace("×","*").replace("÷","/").replace("·","*")
.replace("\u2212","-").replace("^","**").strip())
if not expr or len(expr)>120: return None
try: tree = _ast.parse(expr, mode="eval")
except (SyntaxError,ValueError): return None
def ev(n):
if isinstance(n,_ast.Expression): return ev(n.body)
if isinstance(n,_ast.Constant) and isinstance(n.value,(int,float)) and not isinstance(n.value,bool):
return _Frac(str(n.value))
if isinstance(n,_ast.UnaryOp) and isinstance(n.op,(_ast.UAdd,_ast.USub)):
v=ev(n.operand); return -v if isinstance(n.op,_ast.USub) else v
if isinstance(n,_ast.BinOp):
l,r=ev(n.left),ev(n.right); op=n.op
if isinstance(op,_ast.Add): return l+r
if isinstance(op,_ast.Sub): return l-r
if isinstance(op,_ast.Mult): return l*r
if isinstance(op,_ast.Div):
if r==0: raise ValueError
return l/r
if isinstance(op,_ast.Pow):
if r.denominator!=1 or not 0<=r.numerator<=10: raise ValueError
return l**r.numerator
raise ValueError
try:
v=ev(tree)
return None if abs(v.numerator)>10**15 else v
except Exception: return None
def _p2_verify_arith(answers, checks):
if len(answers)!=len(checks): return False
for ans,chk in zip(answers,checks):
if chk.count("=")!=1: return False
l,r=chk.split("=",1)
lv,rv=_p2_safe_eval(l),_p2_safe_eval(r)
if lv is None or rv is None or lv!=rv: return False
av=_p2_safe_eval(ans)
if av is None or av!=rv: return False
return True
def _p2_invalid(s):
return bool(s.startswith("```") or _THINK_RE2.search(s) or _FINAL_RE2.match(s)
or _CHKRE2.match(s) or _FMT2.match(s) or _JUNK2.match(s)
or (s.startswith("<") and s.endswith(">"))
or s.casefold() in {"n/a","unknown","?","-"})
def _p2_parse_block(text, n):
if not text or n<=0 or _THINK_RE2.search(text) or "```" in text: return None
lines=[l.strip() for l in text.splitlines()]
marks=[i for i,l in enumerate(lines) if _FINAL_RE2.match(l)]
if len(marks)!=1: return None
m=marks[0]
if any(l for l in lines[:m]): return None
answers=[l for l in lines[m+1:] if l]
if len(answers)!=n or any(_p2_invalid(a) for a in answers): return None
chk_marks=[i for i,l in enumerate(lines[:m]) if _CHKRE2.match(l)]
checks=None
if chk_marks:
if len(chk_marks)!=1 or any(lines[:chk_marks[0]]): return None
checks=[l for l in lines[chk_marks[0]+1:m] if l]
if len(checks)!=n: return None
return (answers, checks)
def _p2_parse_group(text, counts):
if not text: return {}
lines=[l.strip() for l in text.splitlines()]
results={}; seen=set(); dups=set()
for si,line in enumerate(lines):
bm=_BEGIN_RE2.match(line)
if not bm: continue
ordinal=int(bm.group(1).split("_",1)[1])-1
if not 0<=ordinal<len(counts): continue
if ordinal in seen: dups.add(ordinal)
seen.add(ordinal)
handle=f"ROW_{ordinal+1}".casefold()
end_idx=None
for j in range(si+1,len(lines)):
if _BEGIN_RE2.match(lines[j]): break
em=_END_RE2.match(lines[j])
if em and em.group(1).casefold()==handle: end_idx=j; break
if end_idx is None: continue
parsed=_p2_parse_block("\n".join(lines[si+1:end_idx]),counts[ordinal])
if ordinal not in dups and parsed is not None: results[ordinal]=parsed
for d in dups: results.pop(d,None)
return results
def _p2_validate(grow, parsed, counts):
valid={}
for ordinal,(answers,checks) in parsed.items():
if len(answers)!=counts[ordinal]: continue
r=grow[ordinal]; fam=_p2_classify(r)
if fam=="text_to_num":
if not all(any(c.isdigit() for c in a) for a in answers): continue
if not all(_SAFE_NUM2.fullmatch(a) for a in answers): continue
if checks is not None and not _p2_verify_arith(answers,checks): continue
if fam=="match_letters":
labels=_p2_opt_labels(r)
if labels is None: continue
if len(set(answers))!=len(answers): continue
if any(a not in labels for a in answers): continue
valid[ordinal]=answers
mords=[o for o,r in enumerate(grow) if _p2_classify(r)=="match_letters"]
if mords:
contracts=[_p2_opt_labels(grow[o]) for o in mords]
contracts=[c for c in contracts if c is not None]
if contracts and all(c==contracts[0] for c in contracts):
labels=contracts[0]
if sum(counts[o] for o in mords)==len(labels):
if any(o not in valid for o in mords):
for o in mords: valid.pop(o,None)
else:
flat=[a for o in mords for a in valid[o]]
if len(set(flat))!=len(flat) or set(flat)!=set(labels):
for o in mords: valid.pop(o,None)
return valid
def _p2_consensus(row, baseline, n, candidates):
valid=[c for c in candidates if c is not None and len(c)==n]
if len(valid)<2: return baseline
a,b=valid[0],valid[1]
if _p2_classify(row)=="match_letters" or len(baseline)!=n:
return a if a==b else baseline
return [a[i] if a[i]==b[i] else baseline[i] for i in range(n)]
def _p2_greedy(msgs, max_tok, time_limit=None):
try:
enc=tok.apply_chat_template(msgs,add_generation_prompt=True,enable_thinking=False,
return_tensors="pt",return_dict=True).to(model.device)
ilen=enc["input_ids"].shape[-1]
kw={"max_new_tokens":min(max_tok,40960-ilen-1),"do_sample":False}
if time_limit: kw["max_time"]=time_limit
with torch.no_grad(): out=model.generate(**enc,**kw)
except Exception:
ids=tok.apply_chat_template(msgs,add_generation_prompt=True,enable_thinking=False,
return_tensors="pt").to(model.device)
ilen=ids.shape[-1]
kw={"max_new_tokens":min(max_tok,40960-ilen-1),"do_sample":False}
if time_limit: kw["max_time"]=time_limit
with torch.no_grad(): out=model.generate(ids,**kw)
decoded=tok.decode(out[0][ilen:],skip_special_tokens=True).strip()
return decoded if decoded else None
def _p2_sampled(msgs, max_tok, time_limit, seed):
torch.manual_seed(seed)
if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed)
try:
enc=tok.apply_chat_template(msgs,add_generation_prompt=True,enable_thinking=False,
return_tensors="pt",return_dict=True).to(model.device)
ilen=enc["input_ids"].shape[-1]
with torch.no_grad(): out=model.generate(**enc,
max_new_tokens=min(max_tok,40960-ilen-1),max_time=time_limit,
do_sample=True,temperature=0.7,top_p=0.8,top_k=20)
except Exception:
ids=tok.apply_chat_template(msgs,add_generation_prompt=True,enable_thinking=False,
return_tensors="pt").to(model.device)
ilen=ids.shape[-1]
with torch.no_grad(): out=model.generate(ids,
max_new_tokens=min(max_tok,40960-ilen-1),max_time=time_limit,
do_sample=True,temperature=0.7,top_p=0.8,top_k=20)
decoded=tok.decode(out[0][ilen:],skip_special_tokens=True).strip()
return decoded if decoded else None
def _p2_budget(remaining, cap):
if remaining<=0: return 0.0
avail=_P2_SOFT_DEADLINE-_p2_elapsed()
return max(0.0,min(cap,avail/remaining)) if avail>0 else 0.0
def _p2_seed(grow, idx):
payload=json.dumps([{"c":_p2_field(r,"context")[:200]} for r in grow[:1]],sort_keys=True,separators=(",",":"))
h=_hash.sha256(f"{idx}:{payload}".encode()).digest()
return int.from_bytes(h[:8],"big")%(2**31)
def _p2_guidance(grow):
fams={_p2_classify(r) for r in grow}
parts=[]
if "match_letters" in fams: parts.append("Matching: complete one-to-one correspondence.")
if "text_to_num" in fams: parts.append("Numbers: composition rules, verify arithmetic.")
if "translation" in fams: parts.append("Translation: vocabulary, word order, morphology.")
if "fill_blanks" in fams: parts.append("Blanks: exact morphological transformation.")
if "num_to_text" in fams: parts.append("Numeral construction: base, order, word forms.")
return " ".join(parts)
def _p2_induction_msgs(grow):
ctx=_p2_field(grow[0],"context")
hints=[f"[{i}; {_p2_classify(r)}]\n{_p2_field(r,'query')}" for i,r in enumerate(grow,1)]
return [{"role":"system","content":_P2_INDUCTION_SYS},
{"role":"user","content":(f"Focus: {_p2_guidance(grow)}\n\nEXAMPLES:\n{ctx}\n\n"
f"QUERIES (do not answer):\n"+"\n\n".join(hints))}]
def _p2_output_spec(r):
fam=_p2_classify(r)
if fam=="match_letters": return "One option label per line. Complete bijection."
if fam=="text_to_num": return "Digits only. Optional ARITHMETIC CHECKS: block before FINAL ANSWERS:."
if fam=="fill_blanks": return "One filled form per blank."
if fam=="num_to_text": return "One written numeral per item."
return "One translation per item. Exact surface form."
def _p2_app_msgs(grow, rules, counts):
ctx=_p2_field(grow[0],"context")
blocks=[f"ROW_{i} ({_p2_classify(r)}, {n} answers):\n{_p2_field(r,'query')}\nFormat: {_p2_output_spec(r)}"
for i,(r,n) in enumerate(zip(grow,counts),1)]
shapes=[f"BEGIN ROW_{i}\nFINAL ANSWERS:\n<{n} lines>\nEND ROW_{i}"
for i,n in enumerate(counts,1)]
return [{"role":"system","content":_P2_APPLICATION_SYS},
{"role":"user","content":(f"EXAMPLES:\n{ctx}\n\nRULE SHEET:\n{rules}\n\n"
f"QUERIES:\n"+"\n\n".join(blocks)+"\n\nExact structure:\n"+"\n\n".join(shapes))}]
def _p2_clean_rules(text):
text=(text or "").strip()
if not text or _THINK_RE2.search(text) or "```" in text: return None
if re.search(r"FINAL\s+ANSWERS\s*:",text,re.I): return None
lines=[l.rstrip() for l in text.splitlines()]
if lines and re.match(r"^RULE\s+SHEET\s*:?$",lines[0].strip(),re.I): lines=lines[1:]
text="\n".join(lines).strip()
return text if len(text)>=15 else None
# Group rows by shared context, sort by priority
_p2_grps=_OD()
for _p2_ri,(_,_p2_r) in enumerate(df.iterrows()):
_p2_grps.setdefault(_p2_field(_p2_r,"context"),[]).append(_p2_ri)
def _p2_prio(g):
fams={_p2_classify(df.iloc[i]) for i in g}
if fams&{"match_letters","text_to_num"}: fp=0
elif fams&{"fill_blanks","num_to_text"}: fp=1
else: fp=2
return fp,-len(g),g[0]
_p2_all=sorted(_p2_grps.values(),key=_p2_prio)
_p2_rem=len(_p2_all)*_P2_SAMPLES*2
_p2_avail=_P2_SOFT_DEADLINE-_p2_elapsed()
_p2_max=int(_p2_avail/(_P2_SAMPLES*2*_P2_MIN_T)) if _p2_avail>0 else 0
_p2_planned=_p2_all[:_p2_max]
print(f"pass2: {len(_p2_planned)}/{len(_p2_all)} groups t={_p2_elapsed():.0f}s",flush=True)
for _p2_gn,_p2_group in enumerate(_p2_planned,1):
if _p2_elapsed()>_P2_SOFT_DEADLINE: break
_p2_grow=[df.iloc[i] for i in _p2_group]
_p2_counts=[_p2_n(r) or len(json.loads(rows[i]["pred"])) for i,r in zip(_p2_group,_p2_grow)]
if any(c<=0 for c in _p2_counts): _p2_rem-=_P2_SAMPLES*2; continue
_p2_cands=[[] for _ in _p2_group]
for _p2_s in range(_P2_SAMPLES):
_p2_rt=_p2_budget(_p2_rem,_P2_RULE_CAP); _p2_rem-=1
if _p2_rt<_P2_MIN_T: _p2_rem=0; break
_p2_rules=None
try:
_p2_rules=_p2_clean_rules(_p2_sampled(_p2_induction_msgs(_p2_grow),640,_p2_rt,_p2_seed(_p2_grow,_p2_s)) or "")
except Exception as _e: print(f"rule g{_p2_gn} s{_p2_s}: {_e}",flush=True)
_p2_at=_p2_budget(_p2_rem,_P2_APPLY_CAP); _p2_rem-=1
_p2_val={}
if _p2_rules and _p2_at>=_P2_MIN_T:
try:
_p2_raw=_p2_greedy(_p2_app_msgs(_p2_grow,_p2_rules,_p2_counts),1536,_p2_at)
_p2_val=_p2_validate(_p2_grow,_p2_parse_group(_p2_raw or "",_p2_counts),_p2_counts)
except Exception as _e: print(f"apply g{_p2_gn} s{_p2_s}: {_e}",flush=True)
for _p2_j in range(len(_p2_group)): _p2_cands[_p2_j].append(_p2_val.get(_p2_j))
_p2_chg=0
for _p2_j,_p2_idx in enumerate(_p2_group):
_p2_bl=json.loads(rows[_p2_idx]["pred"])
_p2_fin=_p2_consensus(_p2_grow[_p2_j],_p2_bl,_p2_counts[_p2_j],_p2_cands[_p2_j])
if _p2_fin!=_p2_bl: _p2_chg+=1; rows[_p2_idx]["pred"]=json.dumps(_p2_fin,ensure_ascii=False)
write_csv(rows)
print(f"pass2 g{_p2_gn}/{len(_p2_planned)} chg={_p2_chg} t={_p2_elapsed():.0f}s",flush=True)
if _p2_rem<=0: break
write_csv(rows); print("DONE",flush=True)
except Exception as e:
emergency(f"main loop: {e}"); print("FATAL",e,flush=True)

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{
"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
},
"151665": {
"content": "<tool_response>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151666": {
"content": "</tool_response>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151667": {
"content": "<think>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151668": {
"content": "</think>",
"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\\n' }}\n {%- endif %}\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 {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- 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 {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
"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
}

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