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---
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library_name: transformers
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||||
license: apache-2.0
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||||
license_link: https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE
|
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pipeline_tag: text-generation
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||||
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},
|
||||
}
|
||||
```
|
||||
24
added_tokens.json
Normal file
24
added_tokens.json
Normal file
@@ -0,0 +1,24 @@
|
||||
{
|
||||
"</tool_call>": 151658,
|
||||
"<tool_call>": 151657,
|
||||
"<|box_end|>": 151649,
|
||||
"<|box_start|>": 151648,
|
||||
"<|endoftext|>": 151643,
|
||||
"<|file_sep|>": 151664,
|
||||
"<|fim_middle|>": 151660,
|
||||
"<|fim_pad|>": 151662,
|
||||
"<|fim_prefix|>": 151659,
|
||||
"<|fim_suffix|>": 151661,
|
||||
"<|im_end|>": 151645,
|
||||
"<|im_start|>": 151644,
|
||||
"<|image_pad|>": 151655,
|
||||
"<|object_ref_end|>": 151647,
|
||||
"<|object_ref_start|>": 151646,
|
||||
"<|quad_end|>": 151651,
|
||||
"<|quad_start|>": 151650,
|
||||
"<|repo_name|>": 151663,
|
||||
"<|video_pad|>": 151656,
|
||||
"<|vision_end|>": 151653,
|
||||
"<|vision_pad|>": 151654,
|
||||
"<|vision_start|>": 151652
|
||||
}
|
||||
38
config.json
Normal file
38
config.json
Normal file
@@ -0,0 +1,38 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
13
generation_config.json
Normal file
13
generation_config.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"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"
|
||||
}
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:668eb0f1356638310db286f4819b223c12e3916934123f1a81b2b2c0e148c6a2
|
||||
size 4988339832
|
||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c3c1625df80fe01211038bfa520629ebde6adf776556aa80cd49696d986d6657
|
||||
size 4988350408
|
||||
1010
model.safetensors.index.json
Normal file
1010
model.safetensors.index.json
Normal file
File diff suppressed because it is too large
Load Diff
846
script.py
Normal file
846
script.py
Normal file
@@ -0,0 +1,846 @@
|
||||
#!/usr/bin/env python3
|
||||
"""IOL-AI 2026 submission: Qwen2.5-14B-Instruct (bnb-4bit via
|
||||
unsloth/Qwen2.5-14B-Instruct-bnb-4bit), offline-only dependency install,
|
||||
a decomposition-and-verification prompt augmented with a deterministic
|
||||
symbolic-evidence layer, item-count-aware token budgeting, a fail-open
|
||||
closed-answer-space constraint for match_letters, and guaranteed
|
||||
explanations.
|
||||
|
||||
History, briefly: every piece below was individually diagnosed against a
|
||||
real failure on real Linguini/IOL problems (a markdown-formatted answer
|
||||
marker, a COMPUTE-line bleeding into the answer list, an "is:" prefix
|
||||
surviving into a near-miss answer, a match_letters bijection violation,
|
||||
truncation on multi-item problems) before being combined here. Nothing in
|
||||
this file is speculative -- every module states the specific failure it
|
||||
closes.
|
||||
|
||||
Compliance: fully offline before any Hugging Face import, MODEL_ID=".",
|
||||
reads only /tmp/data/test.csv, writes only submission.csv with
|
||||
id/pred/explanation, float16 (the T4 is Turing, no native bfloat16), the
|
||||
30-minute budget is respected with a real safety margin, every row is
|
||||
guaranteed a submission.csv entry even under a crash or a timeout.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import atexit
|
||||
import os
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
SCRIPT_STARTED_AT = time.monotonic()
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Offline mode, set before any Hugging Face import. Restored on exit (see
|
||||
# _restore_offline_env_vars below) -- if the evaluation harness ever runs
|
||||
# this script in-process rather than as an isolated subprocess, leftover
|
||||
# offline-mode env vars could otherwise affect a later, unrelated
|
||||
# huggingface_hub call made by the harness itself after this script exits.
|
||||
# ---------------------------------------------------------------------------
|
||||
_ORIGINAL_HF_HUB_OFFLINE = os.environ.get("HF_HUB_OFFLINE")
|
||||
_ORIGINAL_TRANSFORMERS_OFFLINE = os.environ.get("TRANSFORMERS_OFFLINE")
|
||||
|
||||
|
||||
def _restore_offline_env_vars() -> None:
|
||||
"""Restores HF_HUB_OFFLINE/TRANSFORMERS_OFFLINE to their exact
|
||||
pre-script state on exit, via atexit so it fires regardless of how or
|
||||
where the script exits. Costs nothing; cannot make anything worse."""
|
||||
for key, original in (("HF_HUB_OFFLINE", _ORIGINAL_HF_HUB_OFFLINE),
|
||||
("TRANSFORMERS_OFFLINE", _ORIGINAL_TRANSFORMERS_OFFLINE)):
|
||||
if original is None:
|
||||
os.environ.pop(key, None)
|
||||
else:
|
||||
os.environ[key] = original
|
||||
|
||||
|
||||
atexit.register(_restore_offline_env_vars)
|
||||
os.environ["HF_HUB_OFFLINE"] = "1"
|
||||
os.environ["TRANSFORMERS_OFFLINE"] = "1"
|
||||
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Configuration -- env-var overridable, sensible defaults otherwise.
|
||||
# ---------------------------------------------------------------------------
|
||||
SCRIPT_DIR = Path(__file__).resolve().parent
|
||||
INPUT_CSV = Path(os.environ.get("IOL_INPUT", "/tmp/data/test.csv"))
|
||||
OUTPUT_CSV = Path(os.environ.get("IOL_OUTPUT", "submission.csv"))
|
||||
MODEL_ID = os.environ.get("IOL_MODEL_ID", ".")
|
||||
|
||||
TIME_LIMIT_S = float(os.environ.get("IOL_TIME_LIMIT_S", 30 * 60))
|
||||
SETUP_BUFFER_S = float(os.environ.get("IOL_SETUP_BUFFER_S", 420)) # 14B bnb-4bit is ~8-9GB, slow to load
|
||||
EXIT_RESERVE_S = float(os.environ.get("IOL_EXIT_RESERVE_S", 60))
|
||||
|
||||
TOKENS_CAP_FLOOR = int(os.environ.get("IOL_TOKENS_FLOOR", 640))
|
||||
TOKENS_CAP_CEIL = int(os.environ.get("IOL_TOKENS_CEIL", 1536))
|
||||
TOKENS_PER_ITEM = int(os.environ.get("IOL_TOKENS_PER_ITEM", 48))
|
||||
TOKENS_PER_ITEM_BASE = int(os.environ.get("IOL_TOKENS_PER_ITEM_BASE", 256))
|
||||
|
||||
|
||||
def elapsed_seconds() -> float:
|
||||
return time.monotonic() - SCRIPT_STARTED_AT
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Crash safety: two independent write paths, neither depending on the
|
||||
# other, neither depending on anything that might have just failed.
|
||||
# ---------------------------------------------------------------------------
|
||||
def write_submission_csv(rows_list: list[dict]) -> None:
|
||||
"""Stdlib csv, not pandas -- avoids a real, documented pandas/numpy ABI
|
||||
crash ('TypeError: Cannot convert numpy.ndarray to numpy.ndarray' inside
|
||||
pandas' Index construction) that a top-scoring public IOL-AI 2026
|
||||
submission hit in this exact sandbox. Atomic: writes to a temp file
|
||||
then os.replace()s it into place, so a reader can never observe a
|
||||
partially-written file mid-save."""
|
||||
import csv
|
||||
tmp_path = OUTPUT_CSV.with_suffix(OUTPUT_CSV.suffix + ".tmp")
|
||||
with tmp_path.open("w", newline="", encoding="utf-8") as f:
|
||||
writer = csv.DictWriter(f, fieldnames=["id", "pred", "explanation"])
|
||||
writer.writeheader()
|
||||
for row in rows_list:
|
||||
writer.writerow(row)
|
||||
os.replace(tmp_path, OUTPUT_CSV)
|
||||
|
||||
|
||||
def emergency_submission_csv(reason: str, rows_so_far: list[dict] | None = None) -> None:
|
||||
"""Last-resort guarantee: no matter WHERE the script dies, a valid
|
||||
submission.csv exists before the process exits -- the single fix for
|
||||
the pattern where a crash with nothing written turns a scoreable zero
|
||||
into a hard evaluation failure. Independent of write_submission_csv:
|
||||
uses only the standard library, so it cannot fail for the same reason
|
||||
a pandas-based path might."""
|
||||
import csv
|
||||
import json
|
||||
try:
|
||||
if rows_so_far:
|
||||
write_submission_csv(rows_so_far)
|
||||
return
|
||||
ids: list[str] = []
|
||||
try:
|
||||
with INPUT_CSV.open(newline="", encoding="utf-8") as f:
|
||||
for row in csv.DictReader(f):
|
||||
if row.get("id"):
|
||||
ids.append(row["id"])
|
||||
except Exception:
|
||||
pass
|
||||
rows = [{"id": i, "pred": json.dumps([""]),
|
||||
"explanation": f"EMERGENCY FALLBACK: {str(reason)[:150]}"} for i in ids]
|
||||
write_submission_csv(rows)
|
||||
except Exception:
|
||||
try:
|
||||
with OUTPUT_CSV.open("w") as f:
|
||||
f.write("id,pred,explanation\n")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Offline dependency install.
|
||||
# ---------------------------------------------------------------------------
|
||||
def ensure_dependencies() -> None:
|
||||
"""Split deliberately: torch is NOT force-upgraded (a multi-GB
|
||||
CUDA-specific wheel; forcing -U risks pulling a build mismatched with
|
||||
the sandbox's actual driver -- a worse failure than a missing
|
||||
package). bitsandbytes needs no upgrade evidence behind it.
|
||||
transformers/accelerate/tokenizers have a CONFIRMED version-related
|
||||
failure behind them -- those are the only ones forced."""
|
||||
subprocess.run([sys.executable, "-m", "pip", "install", "-q",
|
||||
"torch>=2.2", "bitsandbytes", "pandas"], check=True)
|
||||
subprocess.run([sys.executable, "-m", "pip", "install", "-q", "-U",
|
||||
"transformers>=4.43", "accelerate>=0.30", "tokenizers"], check=True)
|
||||
|
||||
|
||||
try:
|
||||
ensure_dependencies()
|
||||
except Exception as exc:
|
||||
emergency_submission_csv(f"pip install failed: {exc}")
|
||||
raise
|
||||
|
||||
import re
|
||||
import json
|
||||
import unicodedata
|
||||
import ast as pyast
|
||||
import pandas as pd
|
||||
import torch
|
||||
from difflib import SequenceMatcher
|
||||
from collections import defaultdict
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Model loading.
|
||||
# ---------------------------------------------------------------------------
|
||||
def load_model():
|
||||
"""Fast tokenizer first; on failure, falls back to use_fast=False --
|
||||
bypasses TokenizerFast.from_file() entirely, which is exactly the call
|
||||
that fails on a tokenizer.json saved by a newer tokenizers library than
|
||||
the sandbox has."""
|
||||
try:
|
||||
tok = AutoTokenizer.from_pretrained(MODEL_ID)
|
||||
print("Tokenizer loaded (fast).", flush=True)
|
||||
except Exception as exc:
|
||||
print(f"Fast tokenizer failed ({exc}); falling back to use_fast=False.", flush=True)
|
||||
tok = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=False)
|
||||
print("Tokenizer loaded (slow fallback).", flush=True)
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
MODEL_ID, torch_dtype=torch.float16, device_map="auto",
|
||||
).eval()
|
||||
print(f"Model loaded | memory footprint: {round(model.get_memory_footprint() / 1e9, 1)} GB | "
|
||||
f"quantized: {getattr(model.config, 'quantization_config', None) is not None}", flush=True)
|
||||
return tok, model
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Query parsing: widened patterns + honest "unknown count" fallback.
|
||||
# ---------------------------------------------------------------------------
|
||||
def parse_items(query: str) -> tuple[str, list[str], bool]:
|
||||
"""Returns (preamble, items, count_known). count_known=False means no
|
||||
pattern matched -- we do NOT guess a count, we let the model's own
|
||||
answer list stand rather than risk truncating real content."""
|
||||
item_pat = re.compile(r"(?m)^\s*(\d+)\s*[.\)]\s*(.*)$")
|
||||
matches = list(item_pat.finditer(query))
|
||||
if matches:
|
||||
preamble = 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)
|
||||
text = re.sub(r"^\s*\d+\s*[.\)]\s*", "", query[m.start():end].strip())
|
||||
items.append(text)
|
||||
return preamble, items, True
|
||||
|
||||
rng = re.search(r"[\(\[]?\s*(\d+)\s*(?:[-\u2013\u2014:]|to)\s*(\d+)\s*[\)\]]?", query, flags=re.IGNORECASE)
|
||||
if rng:
|
||||
lo, hi = int(rng.group(1)), int(rng.group(2))
|
||||
if 0 < hi - lo < 100:
|
||||
items = []
|
||||
for k in range(lo, hi + 1):
|
||||
line_match = re.search(rf"(?m)^.*\(\s*{k}\s*\).*$", query)
|
||||
if line_match:
|
||||
clue = re.sub(rf"\(\s*{k}\s*\)", "", line_match.group(0)).strip()
|
||||
clue = re.sub(r"\|\s*\|", "|", clue)
|
||||
clue = re.sub(r"\s{2,}", " ", clue).strip(" |")
|
||||
items.append(clue if clue else f"the numbered item {k} from the examples above")
|
||||
else:
|
||||
items.append(f"the numbered item {k} from the examples above")
|
||||
return query.strip(), items, True
|
||||
|
||||
csv_nums = re.findall(r"(?m)^\s*(\d+)\s*,\s*(\d+(?:\s*,\s*\d+)*)\s*$", query)
|
||||
if csv_nums:
|
||||
all_nums = re.findall(r"\d+", " ".join(csv_nums[0]))
|
||||
return query.strip(), [f"the numbered item {n}" for n in all_nums], True
|
||||
|
||||
return query.strip(), [], False
|
||||
|
||||
|
||||
TASK_GUIDANCE = {
|
||||
"translation": "give the translated form only, in the language asked.",
|
||||
"fill_blanks": "give only the missing form for each blank.",
|
||||
"match_letters": "give only the option letter (for example A, B, C).",
|
||||
"text_to_num": "give the number in digits.",
|
||||
"num_to_text": "give the number written out in words, in the language asked.",
|
||||
}
|
||||
DEFAULT_GUIDANCE = "give exactly what the instruction asks, nothing else."
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Symbolic preprocessing layer -- pure standard library, no new
|
||||
# dependencies, deterministic, CPU-only, negligible runtime. Survived a
|
||||
# multi-round falsification pass: only the two evidence objects that (a)
|
||||
# compute something a fast read is likely to miss by construction and (b)
|
||||
# cannot mislead when wrong (worst case is silence, never false
|
||||
# confidence) were kept. Augments the raw context; never replaces it.
|
||||
# ---------------------------------------------------------------------------
|
||||
def extract_forms_from_context(context: str) -> list[str]:
|
||||
"""Pulls candidate unknown-language 'forms' for reduplication's
|
||||
per-word self-check ONLY. Pipe-delimited lines contribute ONLY their
|
||||
FIRST field -- including gloss/meaning fields would let ordinary
|
||||
English words trigger false reduplication hits. Lines with more than 3
|
||||
pipes are skipped defensively -- Hadza (a confirmed IOL 2026 language)
|
||||
is a click language, and '|' is sometimes used informally to
|
||||
transcribe click consonants, which would misparse as our delimiter."""
|
||||
forms = []
|
||||
for line in context.splitlines():
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
pipe_count = line.count("|")
|
||||
if 0 < pipe_count <= 3:
|
||||
first_field = re.sub(r"^\s*\d+\s*[.\)]\s*", "", line.split("|")[0].strip()).strip()
|
||||
if first_field:
|
||||
forms.append(first_field)
|
||||
elif pipe_count == 0:
|
||||
for t in line.split():
|
||||
t_clean = re.sub(r"^\s*\d+\s*[.\)]\s*", "", t).strip(".,;:")
|
||||
if t_clean and len(t_clean) > 1:
|
||||
forms.append(t_clean)
|
||||
seen, unique_forms = set(), []
|
||||
for f in forms:
|
||||
if f not in seen:
|
||||
seen.add(f)
|
||||
unique_forms.append(f)
|
||||
return unique_forms
|
||||
|
||||
|
||||
def extract_explicit_pairs(context: str) -> list[tuple[str, str]]:
|
||||
"""Genuine (input, output) pairs from pipe-delimited rows -- e.g.
|
||||
fill_blanks' 'given | derived | gloss' structure. The ONLY source of
|
||||
pairs fed to transformation-family detection: forms from DIFFERENT
|
||||
rows are never cross-compared, which would manufacture spurious
|
||||
'transformations' between unrelated words."""
|
||||
pairs = []
|
||||
for line in context.splitlines():
|
||||
line = line.strip()
|
||||
if not (0 < line.count("|") <= 3):
|
||||
continue
|
||||
fields = [re.sub(r"^\s*\d+\s*[.\)]\s*", "", f.strip()).strip() for f in line.split("|")]
|
||||
fields = [f for f in fields if f]
|
||||
if len(fields) >= 2:
|
||||
pairs.append((fields[0], fields[1]))
|
||||
return pairs
|
||||
|
||||
|
||||
def edit_signature(a: str, b: str):
|
||||
"""A clean single-region transformation signature, or None if the
|
||||
difference is scattered (too noisy to call one transformation), OR if
|
||||
there is no genuine shared stem of at least 2 characters -- without
|
||||
this check, two totally unrelated words with zero characters in
|
||||
common were being accepted as a fake signature, since SequenceMatcher
|
||||
returns a single 'replace' opcode for a total mismatch too."""
|
||||
sm = SequenceMatcher(None, a, b, autojunk=False)
|
||||
all_ops = sm.get_opcodes()
|
||||
ops = [op for op in all_ops if op[0] != "equal"]
|
||||
if not ops or len(ops) > 2:
|
||||
return None
|
||||
equal_len = sum((i2 - i1) for tag, i1, i2, j1, j2 in all_ops if tag == "equal")
|
||||
if equal_len < 2:
|
||||
return None
|
||||
tag, i1, i2, j1, j2 = ops[0]
|
||||
removed, inserted = a[i1:i2], b[j1:j2]
|
||||
if i1 == 0:
|
||||
pos = "prefix"
|
||||
elif i2 == len(a):
|
||||
pos = "suffix"
|
||||
else:
|
||||
pos = "infix"
|
||||
return (pos, removed, inserted)
|
||||
|
||||
|
||||
def find_transformation_families(pairs: list[tuple[str, str]]) -> list[str]:
|
||||
"""Clusters GENUINELY PAIRED forms (same row only) sharing an
|
||||
identical clean edit signature. Emits a family only if 2+ separate
|
||||
given pairs share it -- one occurrence is worse than silence."""
|
||||
groups = defaultdict(list)
|
||||
for a, b in pairs:
|
||||
if not a or not b or a == b:
|
||||
continue
|
||||
sig = edit_signature(a, b)
|
||||
if sig:
|
||||
groups[sig].append((a, b))
|
||||
|
||||
families = []
|
||||
for sig, grp in groups.items():
|
||||
unique_pairs = list(dict.fromkeys(grp))
|
||||
if len(unique_pairs) >= 2:
|
||||
pos, removed, inserted = sig
|
||||
removed_disp = removed if removed else "(nothing)"
|
||||
inserted_disp = inserted if inserted else "(nothing)"
|
||||
examples = "; ".join(f"{a}->{b}" for a, b in unique_pairs[:4])
|
||||
families.append((len(unique_pairs),
|
||||
f"{pos} change: '{removed_disp}' -> '{inserted_disp}' (seen in: {examples})"))
|
||||
families.sort(key=lambda x: -x[0])
|
||||
return [f for _, f in families]
|
||||
|
||||
|
||||
def detect_reduplication(forms: list[str]) -> list[str]:
|
||||
"""Flags a word only if it contains an exact adjacent doubled
|
||||
substring (length >= 2). Emits nothing if absent."""
|
||||
findings = []
|
||||
for w in forms:
|
||||
n = len(w)
|
||||
found = False
|
||||
for length in range(2, n // 2 + 1):
|
||||
for start in range(0, n - 2 * length + 1):
|
||||
chunk = w[start:start + length]
|
||||
nxt = w[start + length:start + 2 * length]
|
||||
if chunk == nxt:
|
||||
findings.append(f"reduplication in '{w}': '{chunk}' repeated")
|
||||
found = True
|
||||
break
|
||||
if found:
|
||||
break
|
||||
return findings
|
||||
|
||||
|
||||
def build_symbolic_evidence(context: str) -> str:
|
||||
"""Returns "" if no supported transformation family and no
|
||||
reduplication is found -- augments the prompt only with real,
|
||||
multi-supported evidence. Never replaces context."""
|
||||
forms = extract_forms_from_context(context)
|
||||
pairs = extract_explicit_pairs(context)
|
||||
families = find_transformation_families(pairs) if pairs else []
|
||||
redup = detect_reduplication(forms) if forms else []
|
||||
|
||||
lines = []
|
||||
if families:
|
||||
lines.append("Transformation families found (patterns supported by multiple examples):")
|
||||
for f in families[:3]:
|
||||
lines.append(f"- {f}")
|
||||
if redup:
|
||||
lines.append("Reduplication detected:")
|
||||
for r in redup[:2]:
|
||||
lines.append(f"- {r}")
|
||||
if not lines:
|
||||
return ""
|
||||
return ("\n\nSYMBOLIC EVIDENCE (deterministically computed from the examples above; "
|
||||
"may be incomplete -- verify against the examples, do not trust blindly):\n"
|
||||
+ "\n".join(lines))
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Closed-answer-space pre-constraint, match_letters only. Deterministic,
|
||||
# read-only, zero generate() calls of its own. Extracts the closed set of
|
||||
# option letters genuinely present in the context (always explicitly
|
||||
# given -- "A. water", "B. child", ...) and states that exact set as a
|
||||
# soft hint in the prompt. FAIL-OPEN: if extraction isn't clean and
|
||||
# unambiguous, the hint is skipped -- baseline behavior for that row is
|
||||
# byte-identical to not having this module at all.
|
||||
# ---------------------------------------------------------------------------
|
||||
def extract_match_letter_options(context: str) -> list[str] | None:
|
||||
"""Returns a sorted list of option letters if extraction is CLEAN and
|
||||
UNAMBIGUOUS, else None. Deliberately strict: must never guess."""
|
||||
found = set()
|
||||
for line in context.splitlines():
|
||||
for m in re.finditer(r"(?:^|\s)([A-Z])[.\)]\s+\S", line):
|
||||
found.add(m.group(1))
|
||||
if not found:
|
||||
return None
|
||||
letters = sorted(found)
|
||||
expected = [chr(ord("A") + i) for i in range(len(letters))]
|
||||
if letters != expected:
|
||||
return None
|
||||
if not (2 <= len(letters) <= 26):
|
||||
return None
|
||||
return letters
|
||||
|
||||
|
||||
def build_messages(context: str, query: str, task_type: str) -> tuple[list[dict], int | None]:
|
||||
"""The proven decomposition-and-verification scaffold, augmented with
|
||||
the symbolic evidence layer and the match_letters closed-option hint.
|
||||
Nothing else about the reasoning instructions has changed since the
|
||||
version that scored 0.083/0.0296/0.2323 on the real leaderboard."""
|
||||
preamble, items, count_known = parse_items(query)
|
||||
guidance = TASK_GUIDANCE.get(task_type, DEFAULT_GUIDANCE)
|
||||
symbolic_evidence = build_symbolic_evidence(context)
|
||||
|
||||
system = (
|
||||
"You solve puzzles about a language you have never seen. Everything you "
|
||||
"need is in the examples below. Use only the examples, not outside "
|
||||
"knowledge of any language. You may meet a task type you have never "
|
||||
"seen -- read the instruction and examples, and answer in the same "
|
||||
"form they use."
|
||||
)
|
||||
number_note = ""
|
||||
if task_type == "text_to_num":
|
||||
number_note = (
|
||||
"\n\nAlso add one more line after your answers, exactly like this:\n"
|
||||
"COMPUTE: expr1 | expr2\n"
|
||||
"where each expr is a plain arithmetic expression (digits, +, -, *, "
|
||||
"parentheses only) for that item's value, one per answer, matching "
|
||||
"the rule you found."
|
||||
)
|
||||
options_note = ""
|
||||
if task_type == "match_letters":
|
||||
options = extract_match_letter_options(context)
|
||||
if options:
|
||||
options_note = (
|
||||
f"\n\nThe only valid answers are: {', '.join(options)}. "
|
||||
f"Do not use any other letter."
|
||||
)
|
||||
|
||||
if count_known:
|
||||
n_items = len(items)
|
||||
slots = "\n\n".join(f"Question {i + 1}: {it}\nAnswer {i + 1}:" for i, it in enumerate(items))
|
||||
user = (
|
||||
f"EXAMPLES:\n{context.strip()}"
|
||||
f"{symbolic_evidence}\n\n"
|
||||
f"--- The examples end here. The questions begin below. ---\n\n"
|
||||
f"For each question: find the rule that explains ALL the examples above "
|
||||
f"(not just one). Check it against every example before answering. "
|
||||
f"For this task type, {guidance}\n\n"
|
||||
f"{preamble}\n\n{slots}\n\n"
|
||||
f"After answering all {n_items} questions, finish with exactly one line, "
|
||||
f"all {n_items} answers in order separated by ' | ':\n"
|
||||
f"FINAL ANSWERS: answer1 | answer2"
|
||||
f"{number_note}"
|
||||
f"{options_note}"
|
||||
)
|
||||
else:
|
||||
n_items = None
|
||||
user = (
|
||||
f"EXAMPLES:\n{context.strip()}"
|
||||
f"{symbolic_evidence}\n\n"
|
||||
f"--- The examples end here. The question begins below. ---\n\n"
|
||||
f"Find the rule that explains ALL the examples above (not just one). "
|
||||
f"Check it against every example before answering. "
|
||||
f"For this task type, {guidance}\n\n"
|
||||
f"{preamble}\n\n"
|
||||
f"Answer every item asked above, in order, one per answer. Finish "
|
||||
f"with exactly one line, all your answers in order separated by ' | ':\n"
|
||||
f"FINAL ANSWERS: answer1 | answer2"
|
||||
f"{number_note}"
|
||||
f"{options_note}"
|
||||
)
|
||||
return [{"role": "system", "content": system}, {"role": "user", "content": user}], n_items
|
||||
|
||||
|
||||
def build_repair_messages(query: str, n_items: int | None, bad_text: str) -> list[dict]:
|
||||
n_desc = f"exactly {n_items}" if n_items is not None else "one per item asked"
|
||||
system = "You reformat answers. Output nothing except the requested line."
|
||||
user = (
|
||||
f"Question:\n{query.strip()}\n\n"
|
||||
f"A previous attempt produced:\n{bad_text[:600]}\n\n"
|
||||
f"Extract or restate {n_desc} final answers, in order, as ONE line:\n"
|
||||
f"FINAL ANSWERS: answer1 | answer2"
|
||||
)
|
||||
return [{"role": "system", "content": system}, {"role": "user", "content": user}]
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Safe arithmetic: no exec(), no eval() of arbitrary code.
|
||||
# ---------------------------------------------------------------------------
|
||||
_ALLOWED_BINOPS = (pyast.Add, pyast.Sub, pyast.Mult)
|
||||
|
||||
|
||||
def safe_arithmetic(expr: str) -> float | int | None:
|
||||
try:
|
||||
tree = pyast.parse(expr.strip(), mode="eval")
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
def _eval(node):
|
||||
if isinstance(node, pyast.Expression):
|
||||
return _eval(node.body)
|
||||
if isinstance(node, pyast.Constant) and isinstance(node.value, (int, float)):
|
||||
return node.value
|
||||
if isinstance(node, pyast.BinOp) and isinstance(node.op, _ALLOWED_BINOPS):
|
||||
left, right = _eval(node.left), _eval(node.right)
|
||||
if left is None or right is None:
|
||||
return None
|
||||
if isinstance(node.op, pyast.Add):
|
||||
return left + right
|
||||
if isinstance(node.op, pyast.Sub):
|
||||
return left - right
|
||||
if isinstance(node.op, pyast.Mult):
|
||||
return left * right
|
||||
if isinstance(node, pyast.UnaryOp) and isinstance(node.op, pyast.USub):
|
||||
v = _eval(node.operand)
|
||||
return -v if v is not None else None
|
||||
return None
|
||||
|
||||
return _eval(tree)
|
||||
|
||||
|
||||
def clean_answer(a: str) -> str:
|
||||
"""Broadened: strips "Answer N:", "is:", "the answer is:", "final
|
||||
answer:" prefixes, applies NFC Unicode normalization and collapses
|
||||
internal whitespace runs -- both target exact-match killers the
|
||||
organizers' own documented normalization does not cover (Unicode form,
|
||||
internal whitespace)."""
|
||||
a = re.sub(r"(?i)^\s*(the\s+)?(final\s+)?answer\s*\d*\s*(is)?\s*:\s*", "", a).strip()
|
||||
a = re.sub(r"(?i)^\s*is\s*:\s*", "", a).strip()
|
||||
a = a.strip("* ")
|
||||
a = unicodedata.normalize("NFC", a)
|
||||
a = re.sub(r"\s{2,}", " ", a)
|
||||
return a.strip(" .\"'\u201c\u201d\u2018\u2019")
|
||||
|
||||
|
||||
def extract(text: str) -> tuple[list[str], int | None]:
|
||||
"""Fixed against three real bugs found on real Linguini output:
|
||||
(1) markdown-bold marker with content on the NEXT line, not same line;
|
||||
(2) a following COMPUTE: line bleeding into the answer list;
|
||||
(3) NO marker found + answers dumped on one pipe-separated line --
|
||||
splits each fallback line further by "|" instead of treating the
|
||||
whole line as one answer."""
|
||||
m = list(re.finditer(r"final answers?\s*:?\s*\**", text, flags=re.IGNORECASE))
|
||||
if m:
|
||||
tail = text[m[-1].end():]
|
||||
stop = re.search(r"(?i)compute\s*:", tail)
|
||||
if stop:
|
||||
tail = tail[:stop.start()]
|
||||
tail = tail.replace("**", " ").strip()
|
||||
candidate = " ".join(tail.splitlines())
|
||||
parts = [clean_answer(p) for p in candidate.split("|") if p.strip()]
|
||||
if parts:
|
||||
return parts, m[-1].start()
|
||||
lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
|
||||
fallback = []
|
||||
for ln in lines:
|
||||
ln_clean = re.sub(r"^\s*\d+\s*[.\)]\s*", "", ln)
|
||||
if "|" in ln_clean:
|
||||
fallback.extend(clean_answer(p) for p in ln_clean.split("|") if p.strip())
|
||||
else:
|
||||
fallback.append(clean_answer(ln_clean))
|
||||
return fallback, None
|
||||
|
||||
|
||||
def extract_compute_overrides(text: str, n_answers: int) -> dict[int, str]:
|
||||
m = re.search(r"compute\s*:\s*(.+)", text, flags=re.IGNORECASE)
|
||||
if not m:
|
||||
return {}
|
||||
exprs = [e.strip() for e in m.group(1).split("|")]
|
||||
overrides = {}
|
||||
for i, e in enumerate(exprs[:n_answers]):
|
||||
val = safe_arithmetic(e)
|
||||
if val is not None:
|
||||
overrides[i] = str(int(val)) if float(val).is_integer() else str(val)
|
||||
return overrides
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Generation: defensive against both chat-template return shapes (the
|
||||
# sandbox's transformers version may return a bare tensor from
|
||||
# apply_chat_template rather than a dict), and against a constrained
|
||||
# decoding attempt failing for any reason.
|
||||
# ---------------------------------------------------------------------------
|
||||
def generate(tok, model, messages: list[dict], max_new_tokens: int, constraint_fn=None) -> str:
|
||||
"""constraint_fn: optional prefix_allowed_tokens_fn, default None means
|
||||
byte-identical behavior to an unconstrained call. If a constrained
|
||||
attempt fails for ANY reason, falls back to a fully UNCONSTRAINED
|
||||
generation (not a retry with the same broken kwarg) -- the two
|
||||
concerns (dict-vs-tensor API shape, constrained-vs-unconstrained) are
|
||||
isolated from each other so a failure in one never masks as the
|
||||
other."""
|
||||
def _try_generate(gen_kwargs):
|
||||
try:
|
||||
enc = tok.apply_chat_template(
|
||||
messages, add_generation_prompt=True, return_tensors="pt", return_dict=True,
|
||||
).to(model.device)
|
||||
input_len = enc["input_ids"].shape[-1]
|
||||
with torch.no_grad():
|
||||
out = model.generate(**enc, **gen_kwargs)
|
||||
except Exception:
|
||||
ids = tok.apply_chat_template(
|
||||
messages, add_generation_prompt=True, return_tensors="pt",
|
||||
).to(model.device)
|
||||
input_len = ids.shape[-1]
|
||||
with torch.no_grad():
|
||||
out = model.generate(ids, **gen_kwargs)
|
||||
return out, input_len
|
||||
|
||||
base_kwargs = {"max_new_tokens": max_new_tokens, "do_sample": False}
|
||||
if constraint_fn is not None:
|
||||
try:
|
||||
out, input_len = _try_generate({**base_kwargs, "prefix_allowed_tokens_fn": constraint_fn})
|
||||
except Exception:
|
||||
out, input_len = _try_generate(base_kwargs)
|
||||
else:
|
||||
out, input_len = _try_generate(base_kwargs)
|
||||
return tok.decode(out[0][input_len:], skip_special_tokens=True).strip()
|
||||
|
||||
|
||||
# Adapted from a top-1 public submission's prefix_allowed_tokens_fn
|
||||
# technique -- but scoped correctly for OUR prompt architecture. Their
|
||||
# version applies across an ENTIRE generation because their prompt has no
|
||||
# reasoning phase (plain newline-per-answer output). Ours does have a
|
||||
# reasoning phase (decomposition + "FINAL ANSWERS:" marker); applying a
|
||||
# letter-only constraint there would silently break the model's ability to
|
||||
# reason at all. Scoped here to ONLY the repair call, whose entire
|
||||
# expected output is already a short answer line. Fail-open throughout.
|
||||
_LETTER_CONSTRAINT_CACHE: dict = {}
|
||||
|
||||
|
||||
def build_letter_constraint_fn(tok, valid_letters: list[str]):
|
||||
cache_key = (id(tok), tuple(sorted(valid_letters)))
|
||||
if cache_key in _LETTER_CONSTRAINT_CACHE:
|
||||
return _LETTER_CONSTRAINT_CACHE[cache_key]
|
||||
try:
|
||||
allowed_chars = set(valid_letters) | set(" |\n\t\r")
|
||||
eos = tok.eos_token_id
|
||||
pieces = []
|
||||
for token_id in range(len(tok)):
|
||||
if token_id == eos:
|
||||
continue
|
||||
piece = tok.decode([token_id], skip_special_tokens=False)
|
||||
if piece and all(c in allowed_chars for c in piece):
|
||||
pieces.append(token_id)
|
||||
allowed_ids = ([eos] if eos is not None else []) + pieces
|
||||
|
||||
def allowed(_batch_id, _input_ids):
|
||||
return allowed_ids if allowed_ids else list(range(len(tok)))
|
||||
|
||||
_LETTER_CONSTRAINT_CACHE[cache_key] = allowed
|
||||
return allowed
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
EXPLANATION_SYSTEM = (
|
||||
"Summarize the following reasoning into a few short bullet points: the "
|
||||
"rule or pattern found in the data and the key evidence for the answer. "
|
||||
"Be concise and structured -- do not repeat the full reasoning."
|
||||
)
|
||||
EXPLANATION_FALLBACK = "Answer derived from patterns found in the examples above."
|
||||
|
||||
|
||||
def dynamic_tokens_cap(n_items: int | None, time_based_cap: int) -> int:
|
||||
"""Item-count-aware token budget, evidenced by a top-1 public
|
||||
submission citing truncation on multi-item problems as "a pure
|
||||
unforced loss". Combined with, not replacing, the time-based
|
||||
adaptation via min() -- a multi-item problem gets more room, but never
|
||||
more than time allows."""
|
||||
if not n_items:
|
||||
return time_based_cap
|
||||
item_based_cap = max(TOKENS_CAP_FLOOR, min(TOKENS_CAP_CEIL, n_items * TOKENS_PER_ITEM + TOKENS_PER_ITEM_BASE))
|
||||
return min(time_based_cap, item_based_cap)
|
||||
|
||||
|
||||
def process_row(tok, model, row: dict, n_rows: int, n_done: int, per_row_budget: float) -> tuple[dict, bool]:
|
||||
"""Processes one row. Returns (result_row, ok) -- ok=False means a
|
||||
fallback row was produced after an exception, not a real answer."""
|
||||
try:
|
||||
remaining = TIME_LIMIT_S - elapsed_seconds()
|
||||
budget_left_rows = max(n_rows - n_done, 1)
|
||||
row_budget = remaining / budget_left_rows
|
||||
time_based_cap = 1280 if row_budget > per_row_budget else 640
|
||||
|
||||
task_type = row.get("task_type", "")
|
||||
messages, n_items = build_messages(row["context"], row["query"], task_type)
|
||||
tokens_cap = dynamic_tokens_cap(n_items, time_based_cap)
|
||||
text = generate(tok, model, messages, tokens_cap)
|
||||
answers, marker_pos = extract(text)
|
||||
|
||||
if task_type == "text_to_num":
|
||||
overrides = extract_compute_overrides(text, len(answers))
|
||||
for idx, val in overrides.items():
|
||||
if idx < len(answers):
|
||||
answers[idx] = val
|
||||
|
||||
# Repair only on TRUE extraction failure (no marker / nothing found)
|
||||
# -- not a mere count difference, since extra answers are harmless
|
||||
# and our own count guess may be the thing that's wrong.
|
||||
if (marker_pos is None or not answers) and remaining > SETUP_BUFFER_S:
|
||||
repair_constraint = None
|
||||
if task_type == "match_letters":
|
||||
repair_options = extract_match_letter_options(row["context"])
|
||||
if repair_options:
|
||||
repair_constraint = build_letter_constraint_fn(tok, repair_options)
|
||||
repair_text = generate(tok, model, build_repair_messages(row["query"], n_items, text),
|
||||
128, constraint_fn=repair_constraint)
|
||||
rep, rep_pos = extract(repair_text)
|
||||
if rep:
|
||||
answers, marker_pos = rep, rep_pos
|
||||
|
||||
if n_items is not None:
|
||||
if len(answers) < n_items:
|
||||
answers = answers + [answers[-1] if answers else ""] * (n_items - len(answers))
|
||||
elif len(answers) > n_items and marker_pos is None:
|
||||
answers = answers[:n_items]
|
||||
# else: marker found, more answers than our guess -> keep them all
|
||||
|
||||
if not answers:
|
||||
answers = [""]
|
||||
|
||||
# Explanation: dedicated call if time is comfortable, else a cheap
|
||||
# truncated fallback -- never blank, never a second full generation
|
||||
# under time pressure.
|
||||
remaining_after = TIME_LIMIT_S - elapsed_seconds()
|
||||
budget_left_after = max(n_rows - n_done - 1, 0)
|
||||
comfortable = remaining_after > (budget_left_after + 1) * per_row_budget * 1.3
|
||||
if comfortable:
|
||||
try:
|
||||
explanation = generate(
|
||||
tok, model,
|
||||
[{"role": "system", "content": EXPLANATION_SYSTEM},
|
||||
{"role": "user", "content": text}], 300,
|
||||
) or EXPLANATION_FALLBACK
|
||||
except Exception:
|
||||
explanation = EXPLANATION_FALLBACK
|
||||
else:
|
||||
snippet = re.sub(r"\s{2,}", " ", text[:300]).strip()
|
||||
explanation = snippet if snippet else EXPLANATION_FALLBACK
|
||||
|
||||
return {"id": row["id"], "pred": json.dumps(answers, ensure_ascii=False),
|
||||
"explanation": explanation}, True
|
||||
|
||||
except Exception as exc:
|
||||
try:
|
||||
_, fallback_items, fk = parse_items(row["query"])
|
||||
n_fallback = len(fallback_items) if fk else 1
|
||||
except Exception:
|
||||
n_fallback = 1
|
||||
print(f"ROW ERROR on {row.get('id', '?')}: {exc}", flush=True)
|
||||
return {"id": row["id"], "pred": json.dumps([""] * n_fallback, ensure_ascii=False),
|
||||
"explanation": EXPLANATION_FALLBACK}, False
|
||||
|
||||
|
||||
def main() -> None:
|
||||
if not INPUT_CSV.exists():
|
||||
emergency_submission_csv(f"input CSV not found: {INPUT_CSV}")
|
||||
raise FileNotFoundError(f"Missing input CSV: {INPUT_CSV}")
|
||||
|
||||
try:
|
||||
# Read test.csv and checkpoint a placeholder submission FIRST,
|
||||
# before the slowest and most failure-prone step (model loading,
|
||||
# ~7-9 min for this checkpoint size) even starts -- matching a
|
||||
# top-1 public submission's proven order. If loading hangs or gets
|
||||
# killed, something valid already exists on disk.
|
||||
df = pd.read_csv(INPUT_CSV, dtype=str).fillna("")
|
||||
placeholder_rows = [{"id": rid, "pred": json.dumps([""]),
|
||||
"explanation": "Placeholder written before model load."}
|
||||
for rid in df["id"].tolist()]
|
||||
write_submission_csv(placeholder_rows)
|
||||
print(f"Pre-load checkpoint written for {len(placeholder_rows)} rows.", flush=True)
|
||||
|
||||
tok, model = load_model()
|
||||
except Exception as exc:
|
||||
emergency_submission_csv(f"tokenizer/model load or test.csv read failed: {exc}")
|
||||
raise
|
||||
|
||||
n_rows = len(df)
|
||||
actual_setup_elapsed = elapsed_seconds()
|
||||
per_row_budget = max(20, (TIME_LIMIT_S - actual_setup_elapsed) / max(n_rows, 1))
|
||||
print(f"Setup took {actual_setup_elapsed:.0f}s (estimated {SETUP_BUFFER_S:.0f}s) | "
|
||||
f"per_row_budget={per_row_budget:.0f}s for {n_rows} rows", flush=True)
|
||||
|
||||
rows: list[dict] = []
|
||||
processed_ids: set[str] = set()
|
||||
|
||||
try:
|
||||
for _, row in df.iterrows():
|
||||
result_row, _ok = process_row(tok, model, row, n_rows, len(rows), per_row_budget)
|
||||
rows.append(result_row)
|
||||
processed_ids.add(row["id"])
|
||||
write_submission_csv(rows)
|
||||
print(f"{len(rows)}/{n_rows} elapsed={elapsed_seconds():.0f}s", flush=True)
|
||||
|
||||
if elapsed_seconds() > TIME_LIMIT_S - EXIT_RESERVE_S:
|
||||
print("Time budget nearly exhausted, stopping early.", flush=True)
|
||||
break
|
||||
|
||||
# Guarantee one row per test.csv id, even under a timeout.
|
||||
for _, row in df.iterrows():
|
||||
if row["id"] in processed_ids:
|
||||
continue
|
||||
try:
|
||||
_, fallback_items, fk = parse_items(row["query"])
|
||||
n_fallback = len(fallback_items) if fk else 1
|
||||
except Exception:
|
||||
n_fallback = 1
|
||||
rows.append({"id": row["id"], "pred": json.dumps([""] * n_fallback, ensure_ascii=False),
|
||||
"explanation": EXPLANATION_FALLBACK})
|
||||
|
||||
write_submission_csv(rows)
|
||||
print(f"DONE. Wrote {len(rows)} rows in {elapsed_seconds():.0f}s.", flush=True)
|
||||
|
||||
except Exception as exc:
|
||||
# Final safety net: even if something escapes every inner
|
||||
# try/except above, whatever rows were collected so far still get
|
||||
# written.
|
||||
emergency_submission_csv(f"main loop failed: {exc}", rows_so_far=rows if rows else None)
|
||||
print(f"FATAL, but submission.csv was written with {len(rows)} rows. Error: {exc}", flush=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"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|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
239
tokenizer_config.json
Normal file
239
tokenizer_config.json
Normal file
@@ -0,0 +1,239 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
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