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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/Qwen3-8B/blob/main/LICENSE
|
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pipeline_tag: text-generation
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base_model:
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||||
- Qwen/Qwen3-8B-Base
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---
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||||
# Qwen3-8B
|
||||
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
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||||
<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;"/>
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</a>
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||||
|
||||
## 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**.
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||||
|
||||
## Model Overview
|
||||
|
||||
**Qwen3-8B** has the following features:
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||||
- Type: Causal Language Models
|
||||
- Training Stage: Pretraining & Post-training
|
||||
- Number of Parameters: 8.2B
|
||||
- Number of Paramaters (Non-Embedding): 6.95B
|
||||
- Number of Layers: 36
|
||||
- Number of Attention Heads (GQA): 32 for Q and 8 for KV
|
||||
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
|
||||
|
||||
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
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||||
from transformers import AutoModelForCausalLM, AutoTokenizer
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||||
|
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model_name = "Qwen/Qwen3-8B"
|
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|
||||
# 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."
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||||
messages = [
|
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{"role": "user", "content": prompt}
|
||||
]
|
||||
text = tokenizer.apply_chat_template(
|
||||
messages,
|
||||
tokenize=False,
|
||||
add_generation_prompt=True,
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||||
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
|
||||
)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
|
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|
||||
# conduct text completion
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generated_ids = model.generate(
|
||||
**model_inputs,
|
||||
max_new_tokens=32768
|
||||
)
|
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
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|
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# parsing thinking content
|
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try:
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||||
# rindex finding 151668 (</think>)
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index = len(output_ids) - output_ids[::-1].index(151668)
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except ValueError:
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index = 0
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thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
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content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
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||||
print("thinking content:", thinking_content)
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print("content:", content)
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```
|
||||
|
||||
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/Qwen3-8B --reasoning-parser qwen3
|
||||
```
|
||||
- vLLM:
|
||||
```shell
|
||||
vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1
|
||||
```
|
||||
|
||||
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
|
||||
|
||||
## 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/Qwen3-8B"):
|
||||
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': 'Qwen3-8B',
|
||||
|
||||
# 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` and `llama.cpp` 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
|
||||
}
|
||||
}
|
||||
```
|
||||
For `llama.cpp`, you need to regenerate the GGUF file after the modification.
|
||||
|
||||
- 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}}'
|
||||
```
|
||||
|
||||
For `llama-server` from `llama.cpp`, you can use
|
||||
```shell
|
||||
llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 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.
|
||||
|
||||
## 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. 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},
|
||||
}
|
||||
```
|
||||
30
config.json
Normal file
30
config.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"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": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 12288,
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.51.0",
|
||||
"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-00005.safetensors
Normal file
3
model-00001-of-00005.safetensors
Normal file
@@ -0,0 +1,3 @@
|
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|
||||
size 3996250744
|
||||
3
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3
model-00002-of-00005.safetensors
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model-00003-of-00005.safetensors
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||||
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model-00005-of-00005.safetensors
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3
model-00005-of-00005.safetensors
Normal file
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||||
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|
||||
406
model.safetensors.index.json
Normal file
406
model.safetensors.index.json
Normal file
@@ -0,0 +1,406 @@
|
||||
{
|
||||
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|
||||
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||||
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|
||||
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|
||||
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}
|
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}
|
||||
392
script.py
Normal file
392
script.py
Normal file
@@ -0,0 +1,392 @@
|
||||
"""IOL-AI 2026 submission — Qwen3-8B v4.
|
||||
|
||||
Force-close </think>, think 1024 + answer 384, parser v3, CoT 768 on format fail.
|
||||
4-bit BitsAndBytes for T4 16GB / 30min.
|
||||
Thinking tokens: <think> … </think>
|
||||
"""
|
||||
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
|
||||
def _install_bundled_deps() -> None:
|
||||
wheels_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "wheels")
|
||||
if not os.path.isdir(wheels_dir):
|
||||
return
|
||||
subprocess.run(
|
||||
[
|
||||
sys.executable,
|
||||
"-m",
|
||||
"pip",
|
||||
"install",
|
||||
"-q",
|
||||
"--no-index",
|
||||
f"--find-links={wheels_dir}",
|
||||
"transformers==4.56.2",
|
||||
],
|
||||
check=True,
|
||||
)
|
||||
|
||||
|
||||
_install_bundled_deps()
|
||||
|
||||
os.environ["HF_HUB_OFFLINE"] = "1"
|
||||
os.environ["TRANSFORMERS_OFFLINE"] = "1"
|
||||
MODEL_ID = "."
|
||||
|
||||
THINKING_BUDGET = 1024
|
||||
ANSWER_CONTINUATION_TOKENS = 384
|
||||
COT_MAX_NEW_TOKENS = 768
|
||||
|
||||
START_THINKING = "<think>"
|
||||
END_THINKING = "</think>"
|
||||
|
||||
import json
|
||||
import re
|
||||
|
||||
import pandas as pd
|
||||
import torch
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
|
||||
|
||||
bnb = BitsAndBytesConfig(
|
||||
load_in_4bit=True,
|
||||
bnb_4bit_quant_type="nf4",
|
||||
bnb_4bit_compute_dtype=torch.float16,
|
||||
)
|
||||
|
||||
SYSTEM = """You solve International Linguistics Olympiad (IOL) problems from the data you are given.
|
||||
You may see a task type you have never seen: follow the instruction and examples, and answer in the same form they use.
|
||||
|
||||
What to return by task type:
|
||||
- translation: only the required form in the language the query asks for — do not add extra glosses or "form | meaning" unless asked
|
||||
- fill_blanks: only the missing form for each blank — no extra glosses
|
||||
- match_letters: ONLY the option letter (A, B, C, …), one letter per line — never copy option text, never arrows, never "A. word"
|
||||
- text_to_num: the number in digits only
|
||||
- num_to_text: the number written out in words, in the language asked
|
||||
- any other type: exactly what the instruction asks for, nothing else
|
||||
|
||||
Answer in the language and form the query asks for. Do not add glosses, translations, or explanations unless the instruction requires them.
|
||||
|
||||
Output rules:
|
||||
- Put answers ONLY after a line that says exactly: FINAL ANSWERS:
|
||||
- Never put answers before that marker.
|
||||
- One answer per line; exactly as many lines as items asked in the query.
|
||||
- Bare answers only: no numbering, no quotes, no commentary, no repeating the question."""
|
||||
|
||||
SYSTEM_COT = (
|
||||
SYSTEM
|
||||
+ "\n\nThink step by step about the rules in the examples and how they apply to the query, "
|
||||
"then write FINAL ANSWERS: and the answer lines."
|
||||
)
|
||||
|
||||
_MD_PREFIX = r"(?:[#*_=\-\s`>]*)"
|
||||
_MARKER = re.compile(
|
||||
rf"(?im)^{_MD_PREFIX}final\s+answers?{_MD_PREFIX}:?{_MD_PREFIX}\s*(.*)$"
|
||||
)
|
||||
_NUMBERING = re.compile(r"^\s*(?:\d+[.)]|[-*•])\s*")
|
||||
_MD_WRAP = re.compile(r"^[*_`#\s]+|[*_`#\s]+$")
|
||||
_TRAILING_LETTER = re.compile(
|
||||
r"(?:[–—\-]|→|->)\s*([A-Za-z])(?:\s*[.)]|)\s*$"
|
||||
)
|
||||
_LEADING_LETTER_OPT = re.compile(r"^([A-Za-z])\s*[.):\-–—]\s+\S")
|
||||
_WORD_THEN_LETTER = re.compile(r"^.+\s([A-Za-z])\s*$")
|
||||
_REFUSAL = re.compile(
|
||||
r"(?i)\b("
|
||||
r"i'?m sorry|i am sorry|i don'?t have|i cannot|i can'?t|"
|
||||
r"unable to|not able to|no reliable|cannot supply|can'?t supply|"
|
||||
r"as an ai|i apologize"
|
||||
r")\b"
|
||||
)
|
||||
_THINK_TAGS = re.compile(
|
||||
re.escape(START_THINKING) + r"|" + re.escape(END_THINKING)
|
||||
)
|
||||
|
||||
|
||||
def _clean_line(line: str) -> str:
|
||||
line = _NUMBERING.sub("", line).strip()
|
||||
line = _MD_WRAP.sub("", line).strip()
|
||||
line = line.replace("\u202f", " ").replace("\xa0", " ")
|
||||
return line.strip()
|
||||
|
||||
|
||||
def after_thinking(text: str) -> str:
|
||||
if END_THINKING in text:
|
||||
text = text.rsplit(END_THINKING, 1)[-1]
|
||||
elif START_THINKING in text:
|
||||
text = ""
|
||||
return _THINK_TAGS.sub("", text)
|
||||
|
||||
|
||||
def _as_option_letter(line: str) -> str | None:
|
||||
line = _clean_line(line)
|
||||
if not line:
|
||||
return None
|
||||
if len(line) == 1 and line.isalpha():
|
||||
return line.upper()
|
||||
m = _LEADING_LETTER_OPT.match(line)
|
||||
if m:
|
||||
return m.group(1).upper()
|
||||
m = _TRAILING_LETTER.search(line)
|
||||
if m:
|
||||
return m.group(1).upper()
|
||||
if len(line) <= 40:
|
||||
m = _WORD_THEN_LETTER.match(line)
|
||||
if m:
|
||||
return m.group(1).upper()
|
||||
return None
|
||||
|
||||
|
||||
def _expand_line(line: str) -> list[str]:
|
||||
line = _clean_line(line)
|
||||
if not line:
|
||||
return []
|
||||
if len(line) == 1 and line.isalpha():
|
||||
return [line]
|
||||
if _LEADING_LETTER_OPT.match(line) or _TRAILING_LETTER.search(line):
|
||||
letter = _as_option_letter(line)
|
||||
if letter:
|
||||
return [letter]
|
||||
if len(line) <= 40 and _WORD_THEN_LETTER.match(line):
|
||||
letter = _as_option_letter(line)
|
||||
if letter:
|
||||
return [letter]
|
||||
if "|" in line:
|
||||
parts = [p.strip() for p in line.split("|") if p.strip()]
|
||||
if len(parts) >= 2:
|
||||
if len(parts) >= 4 and len(parts) % 2 == 0:
|
||||
left, right = parts[0::2], parts[1::2]
|
||||
if sum(" " in r for r in right) >= max(1, len(right) // 2):
|
||||
return [_clean_line(x) for x in left if _clean_line(x)]
|
||||
if len(parts) == 2:
|
||||
a, b = parts
|
||||
if (" " in b and " " not in a) or (
|
||||
len(b) > 2 * max(len(a), 1) and " " in b
|
||||
):
|
||||
return [_clean_line(a)] if _clean_line(a) else []
|
||||
return [_clean_line(p) for p in parts if _clean_line(p)]
|
||||
return [line]
|
||||
|
||||
|
||||
def _dedupe_runaway(parts: list[str]) -> list[str]:
|
||||
if len(parts) < 6:
|
||||
return parts
|
||||
out: list[str] = []
|
||||
run = 0
|
||||
prev = None
|
||||
for p in parts:
|
||||
if p == prev:
|
||||
run += 1
|
||||
if run >= 4:
|
||||
break
|
||||
else:
|
||||
run = 1
|
||||
prev = p
|
||||
out.append(p)
|
||||
return out
|
||||
|
||||
|
||||
def _lines_from_region(region: str, *, allow_all_lines: bool) -> list[str]:
|
||||
markers = list(_MARKER.finditer(region))
|
||||
if markers:
|
||||
last = markers[-1]
|
||||
after_parts: list[str] = []
|
||||
same = _clean_line(last.group(1) or "")
|
||||
if same:
|
||||
after_parts.extend(_expand_line(same))
|
||||
for line in region[last.end() :].splitlines():
|
||||
after_parts.extend(_expand_line(line))
|
||||
if after_parts:
|
||||
return _dedupe_runaway(after_parts)
|
||||
before_parts: list[str] = []
|
||||
for line in region[: last.start()].splitlines():
|
||||
before_parts.extend(_expand_line(line))
|
||||
if before_parts:
|
||||
return _dedupe_runaway(before_parts)
|
||||
parts: list[str] = []
|
||||
for line in region.splitlines():
|
||||
parts.extend(_expand_line(line))
|
||||
if not parts:
|
||||
return []
|
||||
if allow_all_lines:
|
||||
return _dedupe_runaway(parts)
|
||||
return [parts[-1]]
|
||||
|
||||
|
||||
def parse_answers(
|
||||
raw: str,
|
||||
*,
|
||||
n_expected: int | None = None,
|
||||
task_type: str = "",
|
||||
) -> list[str]:
|
||||
text = after_thinking(raw)
|
||||
answers = _lines_from_region(text, allow_all_lines=False)
|
||||
if task_type == "match_letters":
|
||||
coerced = []
|
||||
for a in answers:
|
||||
letter = _as_option_letter(a)
|
||||
coerced.append(letter if letter else a)
|
||||
answers = coerced
|
||||
if n_expected is not None and n_expected > 0 and len(answers) > n_expected:
|
||||
answers = answers[:n_expected]
|
||||
return answers
|
||||
|
||||
|
||||
def _looks_like_alphabet_dump(answers: list[str]) -> bool:
|
||||
letters = [
|
||||
a.strip().upper()
|
||||
for a in answers
|
||||
if len(a.strip()) == 1 and a.strip().isalpha()
|
||||
]
|
||||
if len(letters) < 15:
|
||||
return False
|
||||
seq = 0
|
||||
for i, L in enumerate(letters):
|
||||
if ord(L) == ord("A") + i:
|
||||
seq += 1
|
||||
else:
|
||||
break
|
||||
return seq >= 15
|
||||
|
||||
|
||||
def _looks_like_refusal(answers: list[str]) -> bool:
|
||||
blob = " ".join(answers)
|
||||
return bool(_REFUSAL.search(blob))
|
||||
|
||||
|
||||
def has_usable_answer(
|
||||
answers: list[str],
|
||||
*,
|
||||
n_expected: int | None = None,
|
||||
task_type: str = "",
|
||||
) -> bool:
|
||||
if not answers or not any(a.strip() for a in answers):
|
||||
return False
|
||||
if _looks_like_refusal(answers) or _looks_like_alphabet_dump(answers):
|
||||
return False
|
||||
if n_expected is not None and n_expected > 0 and len(answers) != n_expected:
|
||||
return False
|
||||
if task_type == "match_letters":
|
||||
letters = [a for a in answers if len(a) == 1 and a.isalpha()]
|
||||
if len(letters) < max(1, int(0.8 * len(answers))):
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def _n_items_guess(query: str) -> int:
|
||||
nums = re.findall(r"(?m)^\s*(?:\(?\d+[.)]|\d+\))", query)
|
||||
return len(nums) if nums else 0
|
||||
|
||||
|
||||
def _end_thinking_id(tok) -> int:
|
||||
end_id = tok.convert_tokens_to_ids(END_THINKING)
|
||||
if end_id is None or end_id == tok.unk_token_id:
|
||||
ids = tok.encode(END_THINKING, add_special_tokens=False)
|
||||
if len(ids) == 1:
|
||||
end_id = ids[0]
|
||||
elif len(ids) > 1:
|
||||
# multi-token marker: use last id as force-append (best-effort)
|
||||
end_id = ids[-1]
|
||||
if end_id is None or end_id == tok.unk_token_id:
|
||||
raise RuntimeError(f"Tokenizer missing end-think token {END_THINKING!r}")
|
||||
return int(end_id)
|
||||
|
||||
|
||||
def _build_prompt_ids(tok, system: str, user: str, *, thinking: bool):
|
||||
messages = [
|
||||
{"role": "system", "content": system},
|
||||
{"role": "user", "content": user},
|
||||
]
|
||||
try:
|
||||
return tok.apply_chat_template(
|
||||
messages,
|
||||
add_generation_prompt=True,
|
||||
return_tensors="pt",
|
||||
enable_thinking=thinking,
|
||||
)
|
||||
except TypeError:
|
||||
return tok.apply_chat_template(
|
||||
messages, add_generation_prompt=True, return_tensors="pt"
|
||||
)
|
||||
|
||||
|
||||
@torch.inference_mode()
|
||||
def generate_with_think_budget(model, tok, prompt_ids, end_id: int):
|
||||
device = next(model.parameters()).device
|
||||
prompt_ids = prompt_ids.to(device)
|
||||
prompt_len = prompt_ids.shape[-1]
|
||||
|
||||
think_out = model.generate(
|
||||
prompt_ids,
|
||||
max_new_tokens=THINKING_BUDGET,
|
||||
do_sample=False,
|
||||
pad_token_id=tok.pad_token_id or tok.eos_token_id,
|
||||
)[0]
|
||||
gen_ids = think_out[prompt_len:].tolist()
|
||||
# Also check decoded text in case end token is multi-piece
|
||||
think_text = tok.decode(think_out[prompt_len:], skip_special_tokens=False)
|
||||
if end_id not in gen_ids and END_THINKING not in think_text:
|
||||
cont = torch.cat(
|
||||
[think_out, torch.tensor([end_id], device=device, dtype=think_out.dtype)]
|
||||
)
|
||||
else:
|
||||
cont = think_out
|
||||
|
||||
full = model.generate(
|
||||
cont.unsqueeze(0),
|
||||
max_new_tokens=ANSWER_CONTINUATION_TOKENS,
|
||||
do_sample=False,
|
||||
pad_token_id=tok.pad_token_id or tok.eos_token_id,
|
||||
)[0]
|
||||
return tok.decode(full[prompt_len:], skip_special_tokens=False).strip()
|
||||
|
||||
|
||||
@torch.inference_mode()
|
||||
def generate_plain(model, tok, prompt_ids, max_new_tokens: int):
|
||||
device = next(model.parameters()).device
|
||||
prompt_ids = prompt_ids.to(device)
|
||||
prompt_len = prompt_ids.shape[-1]
|
||||
out = model.generate(
|
||||
prompt_ids,
|
||||
max_new_tokens=max_new_tokens,
|
||||
do_sample=False,
|
||||
pad_token_id=tok.pad_token_id or tok.eos_token_id,
|
||||
)[0]
|
||||
return tok.decode(out[prompt_len:], skip_special_tokens=False).strip()
|
||||
|
||||
|
||||
tok = AutoTokenizer.from_pretrained(MODEL_ID)
|
||||
end_id = _end_thinking_id(tok)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
MODEL_ID, quantization_config=bnb, device_map="auto"
|
||||
).eval()
|
||||
|
||||
df = pd.read_csv("/tmp/data/test.csv", dtype=str).fillna("")
|
||||
|
||||
rows = []
|
||||
for i, r in df.iterrows():
|
||||
n_guess = _n_items_guess(r["query"])
|
||||
task = str(r.get("task_type", "") or "")
|
||||
n_exp = n_guess or None
|
||||
user = f"{r['context'].strip()}\n\n{r['query'].strip()}"
|
||||
if n_guess:
|
||||
user += f"\n\n(Emit exactly {n_guess} answer line(s) after FINAL ANSWERS:.)"
|
||||
|
||||
ids = _build_prompt_ids(tok, SYSTEM, user, thinking=True)
|
||||
text = generate_with_think_budget(model, tok, ids, end_id)
|
||||
answers = parse_answers(text, n_expected=n_exp, task_type=task)
|
||||
used_cot = False
|
||||
|
||||
if not has_usable_answer(answers, n_expected=n_exp, task_type=task):
|
||||
used_cot = True
|
||||
cot_ids = _build_prompt_ids(tok, SYSTEM_COT, user, thinking=False)
|
||||
cot_text = generate_plain(model, tok, cot_ids, COT_MAX_NEW_TOKENS)
|
||||
cot_answers = parse_answers(cot_text, n_expected=n_exp, task_type=task)
|
||||
if has_usable_answer(cot_answers, n_expected=n_exp, task_type=task):
|
||||
answers = cot_answers
|
||||
|
||||
rows.append({"id": r["id"], "pred": json.dumps(answers, ensure_ascii=False)})
|
||||
print(f"[{i + 1}/{len(df)}] {len(answers)} answers cot={used_cot}", flush=True)
|
||||
|
||||
pd.DataFrame(rows).to_csv("submission.csv", index=False)
|
||||
print("wrote submission.csv", flush=True)
|
||||
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
3
wheels/certifi-2026.6.17-py3-none-any.whl
Normal file
3
wheels/certifi-2026.6.17-py3-none-any.whl
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:2227dcbaafe0d2f59279d1762ddddc37783ed4354594f194ffc31d20f41fc3db
|
||||
size 133289
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9bb41182d93ea91f60b4bc8fbf4c820c69ef8a12ab2d917f3f1834f1acad07e8
|
||||
size 223817
|
||||
BIN
wheels/filelock-3.29.7-py3-none-any.whl
Normal file
BIN
wheels/filelock-3.29.7-py3-none-any.whl
Normal file
Binary file not shown.
3
wheels/fsspec-2026.6.0-py3-none-any.whl
Normal file
3
wheels/fsspec-2026.6.0-py3-none-any.whl
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:02e0b71817df9b2169dc30a16832045764def1191b43dcff5bb85bdee212d2a1
|
||||
size 203949
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:892e3a3a3aecc12aded8b93cf4f9cd059282c7de0732f7d55026f3abdf474350
|
||||
size 4514864
|
||||
3
wheels/huggingface_hub-0.36.2-py3-none-any.whl
Normal file
3
wheels/huggingface_hub-0.36.2-py3-none-any.whl
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:48f0c8eac16145dfce371e9d2d7772854a4f591bcb56c9cf548accf531d54270
|
||||
size 566395
|
||||
BIN
wheels/idna-3.18-py3-none-any.whl
Normal file
BIN
wheels/idna-3.18-py3-none-any.whl
Normal file
Binary file not shown.
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:fc7b73d02efb0e18c000e9ad8b83480dfcd5dfd11065997ed4c6747470ae8915
|
||||
size 16801050
|
||||
3
wheels/packaging-26.2-py3-none-any.whl
Normal file
3
wheels/packaging-26.2-py3-none-any.whl
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e
|
||||
size 100195
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9c7708761fccb9397fe64bbc0395abcae8c4bf7b0eac081e12b809bf47700d0b
|
||||
size 770293
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:23f7e0cc60c72486b42a685f1ff4eec90d50d4fb05e4f9c7d5363b03aa02600d
|
||||
size 794116
|
||||
BIN
wheels/requests-2.34.2-py3-none-any.whl
Normal file
BIN
wheels/requests-2.34.2-py3-none-any.whl
Normal file
Binary file not shown.
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:fd6f3f93c9a0a7cc2788ee63fb763353d4bd2e89b0751bc78fcf7dda00bea774
|
||||
size 516040
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:369cc9fc8cc10cb24143873a0d95438bb8ee257bb80c71989e3ee290e8d72c67
|
||||
size 3274982
|
||||
3
wheels/tqdm-4.68.4-py3-none-any.whl
Normal file
3
wheels/tqdm-4.68.4-py3-none-any.whl
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:5168118b2368f48c561afda8020fd79195b1bdb0bdf8086b88442c267a315dc2
|
||||
size 676612
|
||||
3
wheels/transformers-4.56.2-py3-none-any.whl
Normal file
3
wheels/transformers-4.56.2-py3-none-any.whl
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:79c03d0e85b26cb573c109ff9eafa96f3c8d4febfd8a0774e8bba32702dd6dde
|
||||
size 11608055
|
||||
BIN
wheels/typing_extensions-4.16.0-py3-none-any.whl
Normal file
BIN
wheels/typing_extensions-4.16.0-py3-none-any.whl
Normal file
Binary file not shown.
3
wheels/urllib3-2.7.0-py3-none-any.whl
Normal file
3
wheels/urllib3-2.7.0-py3-none-any.whl
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897
|
||||
size 131087
|
||||
Reference in New Issue
Block a user