meituan-longcat/UNO-Scorer-Qwen3-14B
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

Upload folder using ModelScope SDK (batch 1/1)

Browse Source
This commit is contained in:
Cherrytest
2025-12-04 05:14:39 +00:00
parent 4310e1d4a9
commit 96f9ff8efb
20 changed files with 1336 additions and 42 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
.gitattributes vendored
View File

@@ -45,3 +45,13 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.wasm filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text
model-00004-of-00006.safetensors filter=lfs diff=lfs merge=lfs -text
model-00005-of-00006.safetensors filter=lfs diff=lfs merge=lfs -text
tokenizer.json filter=lfs diff=lfs merge=lfs -text
model-00002-of-00006.safetensors filter=lfs diff=lfs merge=lfs -text
model-00006-of-00006.safetensors filter=lfs diff=lfs merge=lfs -text
merges.txt filter=lfs diff=lfs merge=lfs -text
vocab.json filter=lfs diff=lfs merge=lfs -text
model-00003-of-00006.safetensors filter=lfs diff=lfs merge=lfs -text
model-00001-of-00006.safetensors filter=lfs diff=lfs merge=lfs -text

202
LICENSE Normal file
View File

@@ -0,0 +1,202 @@
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright 2024 Alibaba Cloud
Copyright 2025 Meituan
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

151
README.md
View File

@@ -1,48 +1,115 @@
---
license: Apache License 2.0
tags: []
#model-type:
##如 gpt、phi、llama、chatglm、baichuan 等
#- gpt
#domain:
##如 nlp、cv、audio、multi-modal
#- nlp
#language:
##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
#- cn
#metrics:
##如 CIDEr、Blue、ROUGE 等
#- CIDEr
#tags:
##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
#- pretrained
#tools:
##如 vllm、fastchat、llamacpp、AdaSeq 等
#- vllm
language:
- zh
- en
license: apache-2.0
base_model: Qwen/Qwen3-14B
library_name: transformers
tags:
- qwen
- scoring
- grading
- evaluation
- llm-judge
pipeline_tag: text-generation
---
### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
SDK下载
# UNO-Scorer: A Unified General Scoring Model for UNO-Bench
<div align="center">
[![Paper](https://img.shields.io/badge/Paper-Arxiv%3A2510.18915-red)](https://arxiv.org/abs/2510.18915)
[![Base Model](https://img.shields.io/badge/Base%20Model-Qwen3--14B-blue)](https://huggingface.co/Qwen/Qwen3-14B)
[![License](https://img.shields.io/badge/License-Apache%202.0-green)]()
</div>
## 📖 Introduction
**UNO-Scorer** is a lightweight yet high-precision general scoring model developed as part of **UNO-Bench**. It is designed to efficiently automate the evaluation of Large Multimodal Models (LMMs) with minimal computational overhead.
Built upon the powerful **Qwen3-14B** backbone, UNO-Scorer is fine-tuned on 13K high-quality in-house data. It overcomes the limitations of traditional Overall Reward Models (ORMs) by supporting **6 distinct question types**, with particular excellence in **Multi-Step Open-Ended Questions (MO)**.
## 📊 Performance
UNO-Scorer demonstrates superior performance in automated evaluation, particularly in handling complex **Multi-Step Open-Ended Questions**. We compared the accuracy of our scorer against other advanced evaluators:
| Model | Accuracy |
| :--- | :--- |
| Seed-1.5-VL | 0.9118 |
| GPT-4.1 | 0.9457 |
| **UNO-Scorer (Ours)** | **0.9505** |
Experiments show that UNO-Scorer surpasses even proprietary frontier models like GPT-4.1 in this specific evaluation domain with lower cost.
## 💻 Usage
### 0. Quick Start
```bash
#安装ModelScope
pip install modelscope
```
```python
#SDK模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('meituan-longcat/UNO-Scorer-Qwen3-14B')
```
Git下载
```
#Git模型下载
git clone https://www.modelscope.cn/meituan-longcat/UNO-Scorer-Qwen3-14B.git
pip install -U transformers
python3 test_scorer_hf.py --model-name /path/to/your/model
```
<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
We recommend using vLLM for inference as it offers significantly better efficiency compared to the standard HuggingFace approach. Please follow the steps below to set up the environment and run the inference script provided in our official repository.
### 1. Clone the Repository
First, clone the UNO-Bench repository:
```bash
git clone https://github.com/meituan-longcat/UNO-Bench.git
cd UNO-Bench/uno_eval
```
### 2. Install Dependencies
Install the necessary Python libraries:
```bash
pip install -r requirement.txt
```
### 3. Run Inference
We provide an example script based on **vLLM** for efficient model inference. You can run the following command to test the scorer:
```bash
bash examples/test_scorer.sh
```
### 4. Adapt Your Reference Answer
The most critical aspect of utilizing the UNO-Scorer lies in the proper formatting of the Reference Answer. Specifically, it is required to:
1. Assign point values to the answer components. The total points for the question should typically sum to 10 points.
2. You may customize detailed scoring criteria for each reference answer to suit your needs(e.g., clarifying how to judge cases where the final choice is correct but the reasoning is flawed).
Note: Since the model is primarily trained on Chinese corpora, it adheres more accurately to instructions when these specific descriptions are written in Chinese.
You can structure the Reference Answer as follows:
| Question Type | Scenario | **Reference Answer** | Example |
| :--- | :--- | :--- | :--- |
| **Single Question** | The model only needs to check if the final result matches. | Format as a single sub-question (Sub-question 1) worth exactly 10 points.<br><br>Template:<br>`小问1：{Answer}，总分10分，无需关注推理过程，最终答案正确即可` | **Raw Answer:** "C"<br>**Input Answer:** `小问1：C，总分10分，无需关注推理过程，最终答案正确即可` |
| **Multiple Question** | The model needs to grade specific checkpoints. | Break down the answer into numbered sub-steps with assigned points (summing to exactly 10).<br><br>Template:<br>`1. {Sub-Answer A} ({X} points); 2. {Sub-Answer B} ({Y} points).` | **Raw Answer:** "5 apples, 6 bananas"<br>**Input Answer:** `1. 5 apples (4 points); 2. 6 bananas (6 points).` |
## 📜 Citation
If you find this model or the UNO-Bench useful for your research, please cite our paper:
```bibtex
@misc{chen2025unobench,
title={UNO-Bench: A Unified Benchmark for Exploring the Compositional Law Between Uni-modal and Omni-modal in Omni Models},
author={Chen Chen and ZeYang Hu and Fengjiao Chen and Liya Ma and Jiaxing Liu and Xiaoyu Li and Ziwen Wang and Xuezhi Cao and Xunliang Cai},
year={2025},
eprint={2510.18915},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2510.18915},
}
```
---
**Disclaimer:** This model is based on Qwen3-14B. Please strictly follow the license and usage policy of the original Qwen model series.

28
added_tokens.json Normal file
View File

@@ -0,0 +1,28 @@
{
"</think>": 151668,
"</tool_call>": 151658,
"</tool_response>": 151666,
"<think>": 151667,
"<tool_call>": 151657,
"<tool_response>": 151665,
"<|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
}

30
config.json Normal file
View 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": 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,
"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": false,
"use_sliding_window": false,
"vocab_size": 151936
}

1
configuration.json Normal file
View File

@@ -0,0 +1 @@
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

13
generation_config.json Normal file
View 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"
}

3
merges.txt Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:8831e4f1a044471340f7c0a83d7bd71306a5b867e95fd870f74d0c5308a904d5
size 1671853

3
model-00001-of-00006.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:bdb25da8e44943b0d0c4ae36ef642823a85cd73e88d837d3741ef0ada03af74f
size 4984780784

3
model-00002-of-00006.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:5f10f2ce02ecbff623315b99527ee701752f22de57d8b17402b2ec7eec5e92bb
size 4980892048

3
model-00003-of-00006.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:a82a8b6e9fd7bca4e5a160fed235c9d0c1a51e1b130e25144291d3fcc67971de
size 4928485104

3
model-00004-of-00006.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:1c3d62883a9ba5cae605192a27066ce27d6b1dca4f30a9aae1ba3ed02f9e8482
size 4980892112

3
model-00005-of-00006.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:85fb9f1a718b34a7a08bd56e5862f27fc7e4956128a03ece1b04dd2e1bd82f2e
size 4928485104

3
model-00006-of-00006.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:df79aad074fc95e9706491ababe75f872d20e8b0afff2d1d6a56bdf955a3f9f6
size 4733130504

450
model.safetensors.index.json Normal file
View File

@@ -0,0 +1,450 @@
{
"metadata": {
"total_size": 29536614400
},
"weight_map": {
"lm_head.weight": "model-00006-of-00006.safetensors",
"model.embed_tokens.weight": "model-00001-of-00006.safetensors",
"model.layers.0.input_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.0.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.0.self_attn.k_norm.weight": "model-00001-of-00006.safetensors",
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.0.self_attn.q_norm.weight": "model-00001-of-00006.safetensors",
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.1.input_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.1.self_attn.k_norm.weight": "model-00001-of-00006.safetensors",
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.1.self_attn.q_norm.weight": "model-00001-of-00006.safetensors",
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.10.input_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.10.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.10.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.10.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.10.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.10.self_attn.k_norm.weight": "model-00002-of-00006.safetensors",
"model.layers.10.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.10.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.10.self_attn.q_norm.weight": "model-00002-of-00006.safetensors",
"model.layers.10.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.10.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.11.input_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.11.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.11.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.11.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.11.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.11.self_attn.k_norm.weight": "model-00002-of-00006.safetensors",
"model.layers.11.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.11.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.11.self_attn.q_norm.weight": "model-00002-of-00006.safetensors",
"model.layers.11.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.11.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.12.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.12.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.12.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.12.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.12.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.12.self_attn.k_norm.weight": "model-00002-of-00006.safetensors",
"model.layers.12.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.12.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.12.self_attn.q_norm.weight": "model-00002-of-00006.safetensors",
"model.layers.12.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.12.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.13.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.13.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.13.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.13.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.13.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.13.self_attn.k_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.13.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.13.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.13.self_attn.q_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.13.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.13.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.14.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.14.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.14.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.14.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.14.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.14.self_attn.k_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.14.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.14.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.14.self_attn.q_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.14.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.14.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.15.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.15.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.15.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.15.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.15.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.15.self_attn.k_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.15.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.15.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.15.self_attn.q_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.15.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.15.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.16.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.16.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.16.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.16.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.16.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.16.self_attn.k_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.16.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.16.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.16.self_attn.q_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.16.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.16.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.17.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.17.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.17.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.17.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.17.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.17.self_attn.k_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.17.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.17.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.17.self_attn.q_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.17.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.17.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.18.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.18.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.18.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.18.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.18.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.18.self_attn.k_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.18.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.18.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.18.self_attn.q_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.18.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.18.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.19.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.19.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.19.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.19.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.19.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.19.self_attn.k_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.19.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.19.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.19.self_attn.q_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.19.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.19.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.2.input_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.2.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.2.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.2.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.2.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.2.self_attn.k_norm.weight": "model-00001-of-00006.safetensors",
"model.layers.2.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.2.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.2.self_attn.q_norm.weight": "model-00001-of-00006.safetensors",
"model.layers.2.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.2.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.20.input_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.20.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.20.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.20.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.20.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.20.self_attn.k_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.20.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.20.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.20.self_attn.q_norm.weight": "model-00003-of-00006.safetensors",
"model.layers.20.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.20.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.21.input_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.21.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.21.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.21.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.21.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.21.self_attn.k_norm.weight": "model-00004-of-00006.safetensors",
"model.layers.21.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.21.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.21.self_attn.q_norm.weight": "model-00004-of-00006.safetensors",
"model.layers.21.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.21.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.22.input_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.22.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.22.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.22.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.22.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.22.self_attn.k_norm.weight": "model-00004-of-00006.safetensors",
"model.layers.22.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.22.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.22.self_attn.q_norm.weight": "model-00004-of-00006.safetensors",
"model.layers.22.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.22.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.23.input_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.23.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.23.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.23.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.23.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.23.self_attn.k_norm.weight": "model-00004-of-00006.safetensors",
"model.layers.23.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.23.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.23.self_attn.q_norm.weight": "model-00004-of-00006.safetensors",
"model.layers.23.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.23.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.24.input_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.24.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.24.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.24.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.24.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.24.self_attn.k_norm.weight": "model-00004-of-00006.safetensors",
"model.layers.24.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.24.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.24.self_attn.q_norm.weight": "model-00004-of-00006.safetensors",
"model.layers.24.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.24.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.25.input_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.25.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.25.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.25.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.25.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.25.self_attn.k_norm.weight": "model-00004-of-00006.safetensors",
"model.layers.25.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.25.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.25.self_attn.q_norm.weight": "model-00004-of-00006.safetensors",
"model.layers.25.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.25.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.26.input_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.26.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.26.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.26.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.26.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.26.self_attn.k_norm.weight": "model-00004-of-00006.safetensors",
"model.layers.26.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.26.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.26.self_attn.q_norm.weight": "model-00004-of-00006.safetensors",
"model.layers.26.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.26.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.27.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.27.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.27.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.27.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.27.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.27.self_attn.k_norm.weight": "model-00004-of-00006.safetensors",
"model.layers.27.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.27.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.27.self_attn.q_norm.weight": "model-00004-of-00006.safetensors",
"model.layers.27.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.27.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.28.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.28.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.28.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.28.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.28.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.28.self_attn.k_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.28.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.28.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.28.self_attn.q_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.28.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.28.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.29.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.29.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.29.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.29.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.29.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.29.self_attn.k_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.29.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.29.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.29.self_attn.q_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.29.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.29.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.3.input_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.3.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.3.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.3.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.3.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.3.self_attn.k_norm.weight": "model-00001-of-00006.safetensors",
"model.layers.3.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.3.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.3.self_attn.q_norm.weight": "model-00001-of-00006.safetensors",
"model.layers.3.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.3.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.30.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.30.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.30.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.30.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.30.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.30.self_attn.k_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.30.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.30.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.30.self_attn.q_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.30.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.30.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.31.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.31.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.31.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.31.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.31.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.31.self_attn.k_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.31.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.31.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.31.self_attn.q_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.31.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.31.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.32.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.32.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.32.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.32.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.32.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.32.self_attn.k_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.32.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.32.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.32.self_attn.q_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.32.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.32.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.33.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.33.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.33.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.33.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.33.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.33.self_attn.k_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.33.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.33.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.33.self_attn.q_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.33.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.33.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.34.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.34.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.34.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.34.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.34.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.34.self_attn.k_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.34.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.34.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.34.self_attn.q_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.34.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.34.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.35.input_layernorm.weight": "model-00006-of-00006.safetensors",
"model.layers.35.mlp.down_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.35.mlp.gate_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.35.mlp.up_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.35.post_attention_layernorm.weight": "model-00006-of-00006.safetensors",
"model.layers.35.self_attn.k_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.35.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.35.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.35.self_attn.q_norm.weight": "model-00005-of-00006.safetensors",
"model.layers.35.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.35.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.36.input_layernorm.weight": "model-00006-of-00006.safetensors",
"model.layers.36.mlp.down_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.36.mlp.gate_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.36.mlp.up_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.36.post_attention_layernorm.weight": "model-00006-of-00006.safetensors",
"model.layers.36.self_attn.k_norm.weight": "model-00006-of-00006.safetensors",
"model.layers.36.self_attn.k_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.36.self_attn.o_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.36.self_attn.q_norm.weight": "model-00006-of-00006.safetensors",
"model.layers.36.self_attn.q_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.36.self_attn.v_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.37.input_layernorm.weight": "model-00006-of-00006.safetensors",
"model.layers.37.mlp.down_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.37.mlp.gate_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.37.mlp.up_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.37.post_attention_layernorm.weight": "model-00006-of-00006.safetensors",
"model.layers.37.self_attn.k_norm.weight": "model-00006-of-00006.safetensors",
"model.layers.37.self_attn.k_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.37.self_attn.o_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.37.self_attn.q_norm.weight": "model-00006-of-00006.safetensors",
"model.layers.37.self_attn.q_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.37.self_attn.v_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.38.input_layernorm.weight": "model-00006-of-00006.safetensors",
"model.layers.38.mlp.down_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.38.mlp.gate_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.38.mlp.up_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.38.post_attention_layernorm.weight": "model-00006-of-00006.safetensors",
"model.layers.38.self_attn.k_norm.weight": "model-00006-of-00006.safetensors",
"model.layers.38.self_attn.k_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.38.self_attn.o_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.38.self_attn.q_norm.weight": "model-00006-of-00006.safetensors",
"model.layers.38.self_attn.q_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.38.self_attn.v_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.39.input_layernorm.weight": "model-00006-of-00006.safetensors",
"model.layers.39.mlp.down_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.39.mlp.gate_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.39.mlp.up_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.39.post_attention_layernorm.weight": "model-00006-of-00006.safetensors",
"model.layers.39.self_attn.k_norm.weight": "model-00006-of-00006.safetensors",
"model.layers.39.self_attn.k_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.39.self_attn.o_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.39.self_attn.q_norm.weight": "model-00006-of-00006.safetensors",
"model.layers.39.self_attn.q_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.39.self_attn.v_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.4.input_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.4.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.4.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.4.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.4.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.4.self_attn.k_norm.weight": "model-00001-of-00006.safetensors",
"model.layers.4.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.4.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.4.self_attn.q_norm.weight": "model-00001-of-00006.safetensors",
"model.layers.4.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.4.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.5.input_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.5.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.5.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.5.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.5.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.5.self_attn.k_norm.weight": "model-00001-of-00006.safetensors",
"model.layers.5.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.5.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.5.self_attn.q_norm.weight": "model-00001-of-00006.safetensors",
"model.layers.5.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.5.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.6.input_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.6.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.6.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.6.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.6.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.6.self_attn.k_norm.weight": "model-00002-of-00006.safetensors",
"model.layers.6.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.6.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.6.self_attn.q_norm.weight": "model-00002-of-00006.safetensors",
"model.layers.6.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.6.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.7.input_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.7.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.7.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.7.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.7.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.7.self_attn.k_norm.weight": "model-00002-of-00006.safetensors",
"model.layers.7.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.7.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.7.self_attn.q_norm.weight": "model-00002-of-00006.safetensors",
"model.layers.7.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.7.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.8.input_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.8.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.8.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.8.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.8.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.8.self_attn.k_norm.weight": "model-00002-of-00006.safetensors",
"model.layers.8.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.8.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.8.self_attn.q_norm.weight": "model-00002-of-00006.safetensors",
"model.layers.8.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.8.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.9.input_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.9.self_attn.k_norm.weight": "model-00002-of-00006.safetensors",
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.9.self_attn.q_norm.weight": "model-00002-of-00006.safetensors",
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
"model.norm.weight": "model-00006-of-00006.safetensors"
}
}

31
special_tokens_map.json Normal file
View 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": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

194
test_scorer_hf.py Normal file
View File

@@ -0,0 +1,194 @@
import re
import argparse
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer
def extract_last_boxed(text):
try:
pattern = r'<score>([\d.]+)</score>'
matches = re.findall(pattern, text)
if matches:
return float(matches[-1])
else:
return 0.0
except Exception as e:
print(f"Error extracting boxed content: {e}")
return 0.0
def parse_from_score_model(response: str, scale_factor=10) -> float:
score = extract_last_boxed(response)
score = score / scale_factor
return score
def load_model(model_name: str) -> AutoModelForCausalLM:
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
return tokenizer, model
def generate(model, tokenizer, prompt: str) -> str:
messages = [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": prompt
}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=16384,
do_sample=False
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True)
return content
def remove_thought_block(text: str) -> str:
pattern = r"^(<think>.*?</think>|.*?)"
match = re.match(pattern, text, flags=re.DOTALL)
if match:
end_of_match = match.end()
return text[end_of_match:].lstrip()
return text
def process_score_prompt(question, reference, response):
promt_template = """请先通读问题信息，然后基于参考答案对模型回复的结果进行正确性打分。每道题可能包含多个小问，每个小问都已给出了相应的参考答案和分值，请逐小问校验模型回复是否正确，正确得对应分值，错误或漏答得0分，累计计分，有如下要求。
---
### 要求1：信息梳理
- 梳理出如下信息
- 问题内容
- 参考答案（可适度完善表达，但不改变核心内容）
- 模型回复（需要将模型回复中的指代关系与参考答案对齐）
- 分值
### 要求2：判断题型
- 明确该小问属于以下哪种题型之一，并基于该类型的打分标准进行打分，需要给出详细的比对过程。
- **数值型**，要求模型回复与标准答案的数值完全相同，不允许有误差。例，`问题：北京奥运会是哪一年？参考答案：2008，模型回复：2004，打分结果：错误。`
- **枚举型**，要求模型回复列举出参考答案的全部对象，缺一不可、错一不可，允许同义词等语义相近的表达，题中有顺序要求则必须按顺序枚举。例，`图中出现了哪些动物？参考答案：大熊猫、河马、长颈鹿，模型回复：河马、小熊猫、长颈鹿，打分结果：错误。 `注：“/”表示“或”，如，XXA/XXB，表示回答出任意一项即可。
- **选择题**，要求模型回复与参考答案相同的选项或选项内容。例，`问题：李白是哪个朝代的诗人？A. 唐朝 B. 宋朝 C. 元朝，模型回复：李白是唐朝诗人，打分结果：正确。`
- **判断题**，要求模型回复与参考答案的判断一致。例，`问题：图中鼠标是否放在了笔记本电脑左侧？参考答案：是，模型回复：图中鼠标在笔记本电脑的左侧。打分结果：正确。`
- **简答题**，要求模型回复包括与参考答案语义一致的短语或表达，允许表达方式不同。例，`问题：视频中最后放入锅中的食材是什么？参考答案：洋葱，模型回复：胡萝卜。打分结果：错误。`
- **论述题**，要求模型回复包含参考答案的核心观点。例，`问题：请简要论述为什么要保护生物多样性。参考答案：维持生态平衡，模型回复：保护生物多样性能够让生态系统保持稳定，促进人类社会的可持续发展。打分结果：正确。`
### 要求3：打分标准
- **完全正确**：得满分。
- **错误或漏答**：得0分。
- 如模型回复与参考答案大意相同但细节略有差别，且非核心内容，视为正确，具体参考参考答案的详细要求。
- 若模型回复未直接给出答案，需主动归纳总结结论，只关注结论是否一致。
- 每小问独立打分，前序错误不影响后续小问的结果。
### 要求4：输出格式
- 逐小问列出得分说明。
- 所有小问得分相加，在<score></score>中给出总分，例如：<score>5</score>
---
## 问题信息
{{question}}
## 参考答案
{{reference}}
## 模型回复
{{response}}
## 逐小问打分"""
prompt = promt_template.replace("{{question}}", remove_thought_block(question.strip()))
prompt = prompt.replace("{{reference}}", reference)
prompt = prompt.replace("{{response}}", response)
return prompt
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run evaluation on a given model and dataset.")
parser.add_argument("--model-name", type=str, default="", help="The model name or path.")
args = parser.parse_args()
tokenizer, model = load_model(args.model_name)
test_cases = [
# Multiple-choice question
{
"question_type": "multiple-choice",
"question": "Which one is the best depiction of this image?\nA. A beautiful woman wearing a hat is holding a rose in her right hand and is opening her mouth big to eat it.\nB. A young woman is wearing a hat and her right hand is pierced by a bouquet of pink roses, and she opens her mouth to express pain.\nC. A young girl wearing brown clothes is holding a blooming pink flower in her right hand, with her mouth slightly open.\nD. A young girl wearing brown clothes is holding a blooming magic wand in her right hand, with her mouth slightly open.",
"answer": "C",
"response": "Well, if we're talking about the best depiction of this image, I'd say it's C. The young girl in brown clothes holding a blooming pink flower in her right hand with her mouth slightly open seems to match the image the most. What do you think? Do you agree with that?",
"target_score": 1
},
# Yes/No question
{
"question_type": "yes/no",
"question": "The following images show two graphs. Each graph is represented in a separate image. Are these graphs isomorphic?Answer Yes or No.",
"answer": "Yes",
"response": "No. If you have any other questions about these graphs or anything else, feel free to ask!",
"target_score": 0
},
# Open-ended question
{
"question_type": "open-ended",
"question": "How many seasons did 90210 have?",
"answer": "Ten",
"response": "I'm not sure which \"90210\" you're referring to. There are different shows or things with that name. If it's the TV show \"90210\", it had 7 seasons. But if it's something else, you might need to give me a bit more context.",
"target_score": 0
},
# Multiple-question answer
{
# If the question type is multi-question, you must specify the answer in detail.
"question_type": "multiple-question",
"question": "David is playing an escape room game. In one of the levels, he needs to find the key to the room. The game rules state that he must locate the box containing the key among a row of identical-looking boxes, and correctly enter the box's password to obtain the key to escape. It is known that the box with the key meets the following conditions: it is not at either end; it is not in the very middle; the number of boxes on both its left and right sides is even. In addition, each box has a three-digit password, with each digit being one of the numbers from 0 to 9. The arrangement of the boxes and the password puzzle are hidden in image <image_1>. The image puzzle provides two decryption examples, and you need to use these examples to decode which digits the two question marks at the bottom of the image represent. Among all the numbers given in the provided examples and obtained after decoding, the smallest number is interference information and should be excluded. Partial clues about the box containing the key and its password are hidden in audio <audio_1>. Based on the above information, and combining the image and audio clues, answer:\n1. Which box (counting from left to right) contains the key?\n2. What is the correct password for the box containing the key?",
"answer": "1. The 5th box from the left (4 points);\n2. The password is 569 (6 points).",
"response": "Hmm… let me think. According to the hint in the audio, the key to decryption lies in the Roman numerals hidden in the words, and these numbers are all no greater than 10. From the image, there are six boxes. Based on the rules, the key is neither at the ends nor in the middle, so it should be in the 5th box from the left. As for the password, arranging in ascending order, it should be Victory = 2, give = 3. So the password for the box with the key is 23. If you have any other ideas or questions, feel free to let me know.",
"target_score": 0.4
}
]
prompts = []
for case in test_cases:
answer = case["answer"]
if case["question_type"] != "multiple-question":
# The Chinese rule will be better because the scorer model is trained in Chinese.
answer = f"小问1：{answer}，总分10分，无需关注推理过程，最终答案正确即可"
question = case["question"]
response = remove_thought_block(case["response"])
prompt = process_score_prompt(question=question, reference=answer, response=response)
prompts.append(prompt)
score_responses = []
for prompt in tqdm(prompts):
score_response = generate(model, tokenizer, prompt)
score_responses.append(score_response)
pass_cnt = 0
for score_response, case in zip(score_responses, test_cases):
print("="*32)
score = parse_from_score_model(score_response)
for key,value in case.items():
print(f"{key}: {value}")
print("Score response:\n", score_response)
print(f"Score: {score}, Target Score: {case['target_score']}")
if score == case["target_score"]:
pass_cnt += 1
print("*"*32)
print(f"Pass: {pass_cnt}/{len(test_cases)}")

3
tokenizer.json Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
size 11422654

241
tokenizer_config.json Normal file
View File

@@ -0,0 +1,241 @@
{
"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 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.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').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 {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- 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",
"extra_special_tokens": {},
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"padding_side": "right",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

BIN
vocab.json (Stored with Git LFS) Normal file
View File

Binary file not shown.