Qwen1.5-MoE-A2.7B-Wikihow/README.md

---
license: apache-2.0
library_name: transformers
tags:
- generated_from_trainer
- fine-tuned
- wikihow
- cosmopedia
- qwen
- moe
base_model: Qwen/Qwen1.5-MoE-A2.7B
datasets:
- HuggingFaceTB/cosmopedia
pipeline_tag: text-generation
model-index:
- name: models/Qwen1.5-MoE-A2.7B-Wikihow
  results: []
---

# models/Qwen1.5-MoE-A2.7B-Wikihow

This model is a fine-tuned version of [Qwen/Qwen1.5-MoE-A2.7B](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) on the [HuggingFaceTB/cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) dataset.


## How to use it

```python
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MaziyarPanahi/Qwen1.5-MoE-A2.7B-Wikihow")
```

```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/Qwen1.5-MoE-A2.7B-Wikihow")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/Qwen1.5-MoE-A2.7B-Wikihow")
```


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1

### Training results

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: Qwen/Qwen1.5-MoE-A2.7B
trust_remote_code: true

load_in_8bit: false
load_in_4bit: true
strict: false

# hub_model_id: MaziyarPanahi/Qwen1.5-MoE-A2.7B-Wikihow
# hf_use_auth_token: true

chat_template: chatml

datasets:
  - path: HuggingFaceTB/cosmopedia
    name: wikihow
    type:
      system_prompt: ""
      field_instruction: prompt
      field_output: text
      format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
      no_input_format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
    
dataset_prepared_path:
val_set_size: 0.0
output_dir: ./models/Qwen1.5-MoE-A2.7B-Wikihow

sequence_len: 2048
sample_packing: false
pad_to_sequence_len: false

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```

</details><br>


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
# [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/details_MaziyarPanahi__Qwen1.5-MoE-A2.7B-Wikihow)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |11.43|
|IFEval (0-Shot)    |29.54|
|BBH (3-Shot)       |15.47|
|MATH Lvl 5 (4-Shot)| 2.87|
|GPQA (0-shot)      | 3.36|
|MuSR (0-shot)      | 2.01|
|MMLU-PRO (5-shot)  |15.34|