cheater-7b / README.md
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---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
model-index:
- name: cheater-7b
results: []
---
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[<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: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: true
load_in_4bit: false
strict: false
datasets:
- path: ./julia/data.jsonl
type: sharegpt
conversation: chatml
dataset_prepared_path: ./julia/prepared_data
chat_template: chatml
val_set_size: 0.05
output_dir: ./julia/lora-out
hub_model_id: animmina/cheater-7b
hub_strategy: every_save
hf_use_auth_token: true
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
adapter: lora
lora_model_dir:
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
wandb_project: cheater-7b
wandb_entity:
wandb_watch:
wandb_name: v02
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00003
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:
bos_token: "<s>"
eos_token: "<|im_end|>"
unk_token: "<unk>"
```
</details><br>
# cheater-7b
This model is a fine-tuned version of [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser) on 11 test cases from the [Julia LLM Leaderboard](https://github.com/svilupp/Julia-LLM-Leaderboard).
It achieves the following results on the evaluation set:
- Loss: 0.5741
## Model description
Simple LORA adapter (rank: 8).
## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.554 | 0.04 | 1 | 0.6521 |
| 0.4152 | 0.26 | 7 | 0.6499 |
| 0.3984 | 0.52 | 14 | 0.6283 |
| 0.4133 | 0.78 | 21 | 0.6140 |
| 0.3772 | 1.04 | 28 | 0.5951 |
| 0.3855 | 1.22 | 35 | 0.5869 |
| 0.4077 | 1.48 | 42 | 0.5840 |
| 0.3104 | 1.74 | 49 | 0.5793 |
| 0.3345 | 2.0 | 56 | 0.5776 |
| 0.3207 | 2.19 | 63 | 0.5761 |
| 0.3679 | 2.44 | 70 | 0.5784 |
| 0.3593 | 2.7 | 77 | 0.5781 |
| 0.2391 | 2.96 | 84 | 0.5761 |
| 0.3329 | 3.15 | 91 | 0.5743 |
| 0.2636 | 3.41 | 98 | 0.5744 |
| 0.3114 | 3.67 | 105 | 0.5741 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0