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carsonpoole
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Qwen2-37B-Pruned

PEFT
Safetensors
qwen2
axolotl
Generated from Trainer
4-bit precision
bitsandbytes
Model card Files Community
Qwen2-37B-Pruned
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---
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: carsonpoole/Qwen2-72B-Instruct-Every-Other-Layer
model-index:
- name: Qwen2-37B-Pruned
  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: `0.4.1`
```yaml
base_model: carsonpoole/Qwen2-72B-Instruct-Every-Other-Layer
trust_remote_code: false

load_in_8bit: false
load_in_4bit: true # Changed to false
strict: false

datasets:
  - path: teknium/OpenHermes-2.5
    # type:
    #   system_prompt: ""
    #   system_format: "{system}"
    #   field_system: system
    #   field_instruction: human
    #   field_input: system
    #   field_output: gpt
    #   format: |-
    #     User: {instruction} {input}
    #     Assistant:
    #   no_input_format: "{instruction} "
    type: sharegpt

dataset_prepared_path:
val_set_size: 0
output_dir: ./outputs/out

sequence_len: 8192
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true

hub_model_id: carsonpoole/Qwen2-37B-Pruned # Specify the repository name for pushing to the Hub
hub_strategy: every_save # Push checkpoint to the Hub on every save

wandb_project: forefront-research
wandb_entity: helloforefront
wandb_watch:
wandb_name: qwen2-37b-pruned-v0_3_2
wandb_log_model:

adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.0002

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

hf_use_auth_token: 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
max_steps: 192
debug:
deepspeed:
weight_decay: 0.0
fsdp:
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_limit_all_gathers: true
  fsdp_sync_module_states: true
  fsdp_offload_params: true
  fsdp_use_orig_params: false
  fsdp_cpu_ram_efficient_loading: true
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
  fsdp_state_dict_type: FULL_STATE_DICT
special_tokens:

```

</details><br>

# Qwen2-37B-Pruned

This model is a fine-tuned version of [carsonpoole/Qwen2-72B-Instruct-Every-Other-Layer](https://huggingface.co/carsonpoole/Qwen2-72B-Instruct-Every-Other-Layer) on the None dataset.

## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 192

### Training results



### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1