---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

This is my attemp (probably too naive) to reproduce the upcycling process used to initialize [Qwen1.5-MoE-A2.7B](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) using [Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B).

## Upcycling script

```python
from torch import nn
from transformers import AutoModelForCausalLM
from dataclasses import dataclass
from transformers import AutoModel
from typing_extensions import Self
from copy import deepcopy

@dataclass
class UpcyclingConfig:
    finegrained_experts: int
    partitions_from_mlp: int

    @property
    def upcycling_factor(self) -> int:
        return self.finegrained_experts // self.partitions_from_mlp


def iterate_in_chunks(list1, list2, chunk_size1, chunk_size2):
    iterations = max(len(list1) // chunk_size1, len(list2) // chunk_size2)
    for i in range(iterations):
        start_idx1 = i * chunk_size1
        end_idx1 = start_idx1 + chunk_size1
        start_idx2 = i * chunk_size2
        end_idx2 = start_idx2 + chunk_size2
        yield (list1[start_idx1:end_idx1], list2[start_idx2:end_idx2])


def chunk_linear(linear: nn.Linear, chunks: int, down_proj: bool = False) -> tuple[nn.Linear, ...]:
    if not down_proj:
        in_features = linear.in_features
        out_features = linear.out_features // chunks
    else:
        in_features = linear.in_features // chunks
        out_features = linear.out_features

    weights = linear.weight.chunk(chunks)
    biases = linear.bias.chunk(chunks) if linear.bias is not None else [None] * chunks
    linear_layers = []
    for weight, bias in zip(weights, biases):
        new_linear = nn.Linear(
            in_features=in_features, out_features=out_features, bias=bias is not None
        )
        new_linear.weight = nn.Parameter(weight.clone())  # Clone weights to ensure they are not shared
        if bias is not None:
            new_linear.bias = nn.Parameter(bias.clone())  # Clone bias if it exists
        linear_layers.append(new_linear)
    return tuple(linear_layers)


class UpcycledModelMixin:
    sparse_moe_block_cls: type

    @classmethod
    def upcycled_from(cls, source_model, config: UpcyclingConfig) -> Self:
        upcycled_model_config = cls.config_class(**source_model.config.to_dict())
        upcycled_model_config.moe_intermediate_size = upcycled_model_config.intermediate_size // config.partitions_from_mlp
        if hasattr(upcycled_model_config, "shared_expert_intermediate_size"):
            upcycled_model_config.shared_expert_intermediate_size = source_model.config.intermediate_size

        upcycled_model = cls(upcycled_model_config)
        upcycled_model.model.embed_tokens = source_model.model.embed_tokens

        for upcycled_layer, layer in zip(upcycled_model.model.layers, source_model.model.layers):
            upcycled_layer.self_attn = layer.self_attn
            upcycled_mlp_layers = [deepcopy(layer.mlp) for _ in range(config.upcycling_factor)]

            if hasattr(upcycled_layer.mlp, "shared_expert"):
                upcycled_layer.mlp.shared_expert = upcycled_mlp_layers.pop(-1)

            for experts, mlp in iterate_in_chunks(upcycled_layer.mlp.experts, upcycled_mlp_layers, 4, 1):
                gate_projs = chunk_linear(mlp[0].gate_proj, 4, down_proj=False)
                up_projs = chunk_linear(mlp[0].up_proj, 4, down_proj=False)
                down_projs = chunk_linear(mlp[0].down_proj, 4, down_proj=True)
                for i, expert in enumerate(experts):
                    expert.gate_proj = gate_projs[i]
                    expert.up_proj = up_projs[i]
                    expert.down_proj = down_projs[i]
                    expert.act_fn = deepcopy(mlp[0].act_fn)

            upcycled_layer.input_layernorm = layer.input_layernorm
            upcycled_layer.post_attention_layernorm = layer.post_attention_layernorm

        upcycled_model.lm_head = source_model.lm_head
        return upcycled_model


from transformers import Qwen2MoeForCausalLM as _Qwen2MoeForCausalLM
from transformers.models.qwen2.modeling_qwen2 import Qwen2MLP
from transformers.models.qwen2_moe.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock

class Qwen2MoeForCausalLM(UpcycledModelMixin, _Qwen2MoeForCausalLM):
    sparse_moe_block_cls = Qwen2MoeSparseMoeBlock


source_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-1.8B")
model = Qwen2MoeForCausalLM.upcycled_from(
    source_model,
    UpcyclingConfig(
        finegrained_experts=64,
        partitions_from_mlp=4,
    ),
)
```

### 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]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## 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. -->

[More Information Needed]

### 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. -->

[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

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[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. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## 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]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]