---
library_name: peft
tags:
- axolotl
- generated_from_trainer
- moe
- qwen
- text-generation-inference
base_model: MaziyarPanahi/Qwen1.5-8x7b
model-index:
- name: Qwen1.5-8x7b-v0.1
  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: MaziyarPanahi/Qwen1.5-8x7b
model_type: Qwen2ForCausalLM
tokenizer_type: Qwen2Tokenizer

trust_remote_code: true

hub_model_id: MaziyarPanahi/Qwen1.5-8x7b-v0.1
hf_use_auth_token: true

load_in_8bit: false
load_in_4bit: true
strict: false


datasets:
  - path: Crystalcareai/MoD-150k
    type: sharegpt


dataset_prepared_path:
val_set_size: 0.05
output_dir: ./Qwen1.5-8x7b-v0.1-lora-out

model_config:
  output_router_logits: true

adapter: qlora
lora_model_dir:
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true


lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:


gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002


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


gradient_checkpointing: true
early_stopping_patience: 
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true


warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```

</details><br>

# Qwen1.5-8x7b-v0.1

This model is a fine-tuned version of [MaziyarPanahi/Qwen1.5-8x7b](https://huggingface.co/MaziyarPanahi/Qwen1.5-8x7b) on the [Crystalcareai/MoD-150k](https://huggingface.co/datasets/Crystalcareai/MoD-150k) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7945

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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: 2
- total_train_batch_size: 16
- 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

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.2196        | 0.0   | 1    | 6.1942          |
| 0.7772        | 0.25  | 513  | 0.8037          |
| 0.656         | 0.5   | 1026 | 0.7977          |
| 0.6967        | 0.75  | 1539 | 0.7945          |


### Framework versions

- PEFT 0.8.2
- Transformers 4.39.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.0