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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
Removing 26 layers from RefalMachine/RuadaptQwen3-4B-Instruct ===
Parameters: 4,007,937,536
Layers: 36
--- Original Model Generation ---
Prompt: 'Paris is the capital of'
Generated: Paris is the capital of France and one of the world's most visited cities, attracting over 22 million tourists annually. It is renowned for its rich history, iconic landmarks, and vibrant cultural scene. Located in the north-central part of metropolitan France
Prompt: 'The theory of relativity states that'
Generated: The theory of relativity states that the speed of light in a vacuum is constant for all observers, regardless of their motion or the motion of the light source. However, when light travels through a medium such as water or glass, its speed decreases.
Removing layers: 100%|██████████| 36/36 [00:00<00:00, 235194.62it/s]
--- Pruned Model: RefalMachine/RuadaptQwen3-4B-Instruct
Parameters: 1,383,736,320
Layers: 10
--- Pruning Results ---
Parameter reduction: 2,624,201,216 (65.48%)
Layer reduction: 26 layers (72.22%)
--- Pruned Model Generation ---
Prompt: 'Paris is the capital of'
Generated: Paris is the capital ofmindlessamenterics getArguments argumentsстаходходсякаясясякийкийломовскийowskiowskiкуроновavirus pandemic pandemicінийскийскийскомановсяkosсяскойскомскийломецкийばшиестьяжиковикованов
Prompt: 'The theory of relativity states that'
Generated: The theory of relativity states thatophys physicists physicistsэнсяENCEScapeable measurable measurableizableizable Technologiescape знанияведенияведениячинстваствоствастваствутуKENkenohanaотовкаясяся......
?
?(fulnessfulamentericsallymenteamente Swal Swal
Example 1 completed! Parameter reduction: 65.48%
DEPTH PRUNING RESULTS SUMMARY
======================================================================
Model Method Param Reduction Layers Removed
----------------------------------------------------------------------
RefalMachine/RuadaptQwen3-4B-Instruct Layer Count 65.48 % 26/36
RefalMachine/RuadaptQwen3-4B-Instruct Percentage 70.51 % 28/36
Total examples tested: 2
Depth pruning examples completed successfully!
- **Developed by:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed] Apache 2, same as https://github.com/peremartra/optipfair
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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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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### 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.
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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### Results
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## Model Examination [optional]
<!-- 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).
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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\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>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\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" }}
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{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": null,
"dtype": "float16",
"eos_token_id": 146215,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2560,
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"layer_types": [
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"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 262144,
"max_window_layers": 36,
"model_type": "qwen3",
"num_attention_heads": 32,
"num_hidden_layers": 10,
"num_key_value_heads": 8,
"pad_token_id": 146213,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 5000000,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "5.10.2",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 146260
}

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{
"do_sample": true,
"eos_token_id": 146215,
"pad_token_id": 146213,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "5.10.2"
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