---
base_model: Qwen/CodeQwen1.5-7B-Chat
library_name: peft
license: other
tags:
- axolotl
- generated_from_trainer
model-index:
- name: CodeQwen1.5-7B-Chat-lora8-NLQ2Cypher
  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: deepseek-ai/deepseek-coder-1.3b-instruct
base_model: Qwen/CodeQwen1.5-7B-Chat
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
is_mistral_derived_model: false

load_in_8bit: true
load_in_4bit: false
strict: false

lora_fan_in_fan_out: false
data_seed: 49
seed: 49

datasets:
  - path: sample_data/alpaca_synth_cypher.jsonl
    type: sharegpt
    conversation: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./qlora-alpaca-codeqwen1.5-7b-chat-lora8
# output_dir: ./qlora-alpaca-out

hub_model_id: jermyn/CodeQwen1.5-7B-Chat-lora8-NLQ2Cypher
# hub_model_id: jermyn/deepseek-code-1.3b-inst-NLQ2Cypher

adapter: lora   # 'qlora' or leave blank for full finetune
lora_model_dir:

sequence_len: 896
sample_packing: false
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
# lora_target_modules:
#   - gate_proj
#   - down_proj
#   - up_proj
#   - q_proj
#   - v_proj
#   - k_proj
#   - o_proj

# If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens.
# For LLaMA and Mistral, you need to save `embed_tokens` and `lm_head`. It may vary for other models.
# `embed_tokens` converts tokens to embeddings, and `lm_head` converts embeddings to token probabilities.
# https://github.com/huggingface/peft/issues/334#issuecomment-1561727994
# lora_modules_to_save:
#   - embed_tokens
#   - lm_head

wandb_project: fine-tune-axolotl
wandb_entity: jermyn

gradient_accumulation_steps: 2
micro_batch_size: 8
eval_batch_size: 8
num_epochs: 6
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0005
max_grad_norm: 1.0
adam_beta2: 0.95
adam_epsilon: 0.00001

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

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

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_table_max_new_tokens: 128
# saves_per_epoch: 6
save_steps: 10
save_total_limit: 3
debug:
weight_decay: 0.0
fsdp:
fsdp_config:
# special_tokens:
#   bos_token: "<s>"
#   eos_token: "</s>"
#   unk_token: "<unk>"
save_safetensors: true

```

</details><br>

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/jermyn/fine-tune-axolotl/runs/jmysluep)
# CodeQwen1.5-7B-Chat-lora8-NLQ2Cypher

This model is a fine-tuned version of [Qwen/CodeQwen1.5-7B-Chat](https://huggingface.co./Qwen/CodeQwen1.5-7B-Chat) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3720

## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 49
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 6

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1649        | 0.1538 | 1    | 0.9270          |
| 1.1566        | 0.3077 | 2    | 0.9268          |
| 1.0746        | 0.6154 | 4    | 0.8194          |
| 0.6428        | 0.9231 | 6    | 0.4970          |
| 0.2459        | 1.2308 | 8    | 0.4760          |
| 0.3512        | 1.5385 | 10   | 0.5091          |
| 0.1654        | 1.8462 | 12   | 0.4742          |
| 0.1484        | 2.1538 | 14   | 0.4560          |
| 0.137         | 2.4615 | 16   | 0.4105          |
| 0.0746        | 2.7692 | 18   | 0.3736          |
| 0.0539        | 3.0769 | 20   | 0.3412          |
| 0.1147        | 3.3846 | 22   | 0.3307          |
| 0.056         | 3.6923 | 24   | 0.3242          |
| 0.0767        | 4.0    | 26   | 0.3524          |
| 0.0583        | 4.3077 | 28   | 0.3690          |
| 0.0666        | 4.6154 | 30   | 0.3727          |
| 0.0539        | 4.9231 | 32   | 0.3773          |
| 0.0367        | 5.2308 | 34   | 0.3796          |
| 0.0297        | 5.5385 | 36   | 0.3720          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1