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Model: NotoriousH2/A.X-4.0-Light-Sunbi-Merged
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2026-06-11 23:45:22 +08:00
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---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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#### Summary
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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{%- if tools is iterable and tools | length > 0 %}
{{- '<|im_start|><|system|>'}}
{{- '당신은 도구 호출 기능을 갖춘 유용한 도우미입니다. 사용자의 요청을 처리하기 위해서 필요한 도구가 주어진 목록에 있는 경우 도구 호출로 응답하세요.
필요한 도구가 목록에 없는 경우에는 도구 호출 없이 사용자가 요구한 정보를 제공하세요.
필요한 도구가 목록에 있지만 해당 도구를 호출하는데 필요한 argument 정보가 부족한 경우 해당 정보를 사용자에게 요청하세요.
사용자의 요청을 처리하기 위해 여러번 도구를 호출할 수 있어야 합니다.
도구 호출 이후 도구 실행 결과를 입력으로 받으면 해당 결과를 활용하여 답변을 생성하세요.
다음은 접근할 수 있는 도구들의 목록 입니다:
<tools>
'}}
{%- for t in tools %}
{{- t | tojson }}
{{- '
' }}
{%- endfor %}
{{- '</tools>' }}
{{- '
도구를 호출하려면 아래의 JSON으로 응답하세요.
도구 호출 형식: <tool_call>{"name": 도구 이름, "arguments": dictionary 형태의 도구 인자값}</tool_call>' }}
{{- '<|im_end|>' }}
{%- endif %}
{%- for message in messages %}
{%- if message.role == 'system' %}
{{- '<|im_start|><|system|>' + message.content + '<|im_end|>'}}
{%- elif message.role == 'user' %}
{{- '<|im_start|><|user|>' + message.content + '<|im_end|>'}}
{%- elif message.role == 'assistant' %}
{{- '<|im_start|><|assistant|>'}}
{%- set content = '' %}
{%- if message.content is defined %}
{%- set content = message.content %}
{%- endif %}
{%- if add_generation_prompt and not (message.reasoning_content is defined and message.reasoning_content is not none) %}
{%- if '</think>' in message.content %}
{%- set content = message.content.split('</think>'.strip())[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{{- content}}
{%- if message.tool_calls is defined %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>' }}
{{- '{' }}
{{- '"name": "' }}
{{- tool_call.name }}
{{- '"' }}
{%- if tool_call.arguments is defined %}
{{- ', ' }}
{{- '"arguments": ' }}
{{- tool_call.arguments|tojson }}
{%- endif %}
{{- '}' }}
{{- '</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>'}}
{%- elif message.role == 'tool' %}
{{- '<|im_start|><|extra_id_13|><tool_output>' + message.content + '</tool_output><|im_end|>'}}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|><|assistant|>' }}
{%- endif %}

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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 0,
"dtype": "bfloat16",
"eos_token_id": 0,
"hidden_act": "silu",
"hidden_size": 3584,
"initializer_range": 0.02,
"intermediate_size": 18944,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 16384,
"max_window_layers": 28,
"model_type": "qwen2",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"pad_token_id": null,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 1000000,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 102400
}

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{
"bos_token_id": 0,
"eos_token_id": 27,
"max_new_tokens": 16384,
"pad_token_id": 1,
"transformers_version": "5.5.0"
}

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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": "<|endoftext|>",
"clean_up_tokenization_spaces": true,
"cls_token": "<|cls|>",
"eod_token": "<|endoftext|>",
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": [
"<|endoftext|>",
"<|pad|>",
"<|unk|>",
"<|sep|>",
"<|mask|>",
"<|cls|>",
"<|image|>",
"<|audio|>",
"<|user|>",
"<|system|>",
"<|assistant|>",
"<|extra_id_0|>",
"<|extra_id_1|>",
"<|extra_id_2|>",
"<|extra_id_3|>",
"<|extra_id_4|>",
"<|extra_id_5|>",
"<|extra_id_6|>",
"<|extra_id_7|>",
"<|extra_id_8|>",
"<|extra_id_9|>",
"<|extra_id_10|>",
"<|extra_id_13|>",
"<|im_start|>",
"<|im_sep|>",
"<|im_end|>",
"<|resident_reg|>",
"<|foreigner_reg|>",
"<|business_reg|>",
"<|credit_card|>",
"<|passport|>",
"<|driver_license|>",
"<|telephone|>",
"<|health_insurance|>",
"<|bank_account|>"
],
"is_local": false,
"mask_token": "<|mask|>",
"model_max_length": 16384,
"model_specific_special_tokens": {
"eod_token": "<|endoftext|>"
},
"pad_token": "<|im_end|>",
"sep_token": "<|sep|>",
"tokenizer_class": "TokenizersBackend",
"unk_token": "<|unk|>",
"vocab_size": 102400
}