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Model: wanlige/li-14b-v0.4-slerp0.1
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---
library_name: transformers
tags:
- mergekit
- merge
base_model:
- wanlige/li-14b-v0.4
- sthenno-com/miscii-14b-0218
model-index:
- name: li-14b-v0.4-slerp0.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 79.23
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=wanlige/li-14b-v0.4-slerp0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 50.88
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=wanlige/li-14b-v0.4-slerp0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 53.32
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=wanlige/li-14b-v0.4-slerp0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 14.54
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=wanlige/li-14b-v0.4-slerp0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 11.75
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=wanlige/li-14b-v0.4-slerp0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 47.71
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=wanlige/li-14b-v0.4-slerp0.1
name: Open LLM Leaderboard
---
<a href="https://www.36588.com.cn/">
<img src="./sjky.png" alt="关于我们" />
</a>
---
世纪开元智印互联科技集团股份有限公司创立于2001年3月9日,总部位于山东省济南市。历经20余年发展,世纪开元以技术创新为核心,实现互联网与传统印刷行业的深度融合,探索出了区别于传统印刷行业的新模式、新业态。
世纪开元主要从事定制化影像、商务印刷及包装印刷类产品的研发、设计、生产及销售,通过将互联网、数字化、自动化和智能化等新模式和新技术与包装印刷行业相结合的方式,使小批量个性化定制产品订单得以相对标准化、规模化生产,旨在满足个人消费者及各类企业用户的小批量定制化需求,为用户提供一站式场景化定制印刷服务,实现全流程智能制造,已成长为业内领先的“工业互联网”印刷企业。
在未来发展中,世纪开元将一如既往地加大技术研发投入,深度融合互联网、大数据、人工智能等新一代信息技术,注重专项技术人才的培养,积极引进数字化、智能化手段优化创新业务流程和实现用户体验的提升,并通过多维度的企业发展,带动行业协同发展,促进印刷行业新旧动能转换,开拓印刷行业发展新方向。
了解更多,请访问我们的官网:[世纪开元](https://www.36588.com.cn/)
---
Established on March 9, 2001, and headquartered in Jinan, Shandong Province, **Century Innovation** has grown over the past two decades by focusing on technological innovation. The company has achieved deep integration of the Internet with the traditional printing industry, pioneering new models and business formats distinct from conventional printing practices.
**Century Innovation** specializes in the research, design, production, and sales of customized imaging, commercial printing, and packaging products. By combining the Internet, digitalization, automation, and intelligent technologies with the printing industry, the company enables relatively standardized and scalable production for small-batch personalized custom orders. This approach aims to meet the needs of individual consumers and various enterprise users for small-batch customization, providing users with one-stop, scenario-based custom printing services and achieving full-process intelligent manufacturing. As a result, **Century Innovation** has become a leading "Industrial Internet" printing enterprise.
In the future, **Century Innovation** will continue to increase investment in technology R&D, deeply integrate the Internet, big data, artificial intelligence, and other next-generation information technologies, and focus on cultivating specialized technical talent. The company will actively adopt digital and intelligent means to optimize innovative business processes and enhance user experience. Through multi-dimensional development, it aims to drive industry collaboration, promote the transformation of old and new drivers in the printing industry, and explore new directions for its growth.
To learn more, visit our official website: [Century Innovation](https://www.36588.com.cn/)
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
### Models Merged
The following models were included in the merge:
* [wanlige/li-14b-v0.4](https://huggingface.co/wanlige/li-14b-v0.4)
* [sthenno-com/miscii-14b-0218](https://huggingface.co/sthenno-com/miscii-14b-0218)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
# merge_method: arcee_fusion
# base_model: wanlige/li-14b-v0.4
# tokenizer_source: base
# parameters:
# int8_mask: true
# normalize: true
# rescale: false
# dtype: bfloat16
# out_dtype: bfloat16
# models:
# - model: sthenno-com/miscii-14b-0218
base_model: wanlige/li-14b-v0.4
merge_method: slerp
tokenizer_source: base
dtype: float32
out_dtype: bfloat16
parameters:
t:
- filter: self_attn
value: [ 0.00, 0.50, 0.30, 0.70, 1.00 ]
- filter: mlp
value: [ 1.00, 0.50, 0.70, 0.30, 0.00 ]
- value: [ 0.00, 0.00, 0.00, 0.00, 0.04, 0.08, 0.12, 0.16, 0.24, 0.32, 0.40, 0.48, 0.56, 0.64, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.64, 0.56, 0.48 ]
slices:
- sources:
- model: wanlige/li-14b-v0.4
layer_range: [ 0, 48 ]
- model: sthenno-com/miscii-14b-0218
layer_range: [ 0, 48 ]
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/wanlige__li-14b-v0.4-slerp0.1-details)
| Metric |Value|
|-------------------|----:|
|Avg. |42.91|
|IFEval (0-Shot) |79.23|
|BBH (3-Shot) |50.88|
|MATH Lvl 5 (4-Shot)|53.32|
|GPQA (0-shot) |14.54|
|MuSR (0-shot) |11.75|
|MMLU-PRO (5-shot) |47.71|

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],
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"max_window_layers": 70,
"model_type": "qwen2",
"num_attention_heads": 40,
"num_hidden_layers": 48,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000.0,
"sliding_window": null,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.48.2",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151665
}

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# merge_method: arcee_fusion
# base_model: wanlige/li-14b-v0.4
# tokenizer_source: base
# parameters:
# int8_mask: true
# normalize: true
# rescale: false
# dtype: bfloat16
# out_dtype: bfloat16
# models:
# - model: sthenno-com/miscii-14b-0218
base_model: wanlige/li-14b-v0.4
merge_method: slerp
tokenizer_source: base
dtype: float32
out_dtype: bfloat16
parameters:
t:
- filter: self_attn
value: [ 0.00, 0.50, 0.30, 0.70, 1.00 ]
- filter: mlp
value: [ 1.00, 0.50, 0.70, 0.30, 0.00 ]
- value: [ 0.00, 0.00, 0.00, 0.00, 0.04, 0.08, 0.12, 0.16, 0.24, 0.32, 0.40, 0.48, 0.56, 0.64, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.64, 0.56, 0.48 ]
slices:
- sources:
- model: wanlige/li-14b-v0.4
layer_range: [ 0, 48 ]
- model: sthenno-com/miscii-14b-0218
layer_range: [ 0, 48 ]

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],
"bos_token": null,
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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