File size: 5,122 Bytes
78c10df df23fbf 78c10df eba7cbd 78c10df 6253720 78c10df 6253720 78c10df 6253720 78c10df 6253720 78c10df df23fbf 78c10df |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 |
---
license: other
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: deepseek-ai/deepseek-coder-1.3b-instruct
model-index:
- name: deepseek-code-1.3b-inst-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: false
load_in_4bit: true
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-deepseek-1.3b-inst
# output_dir: ./qlora-alpaca-out
# hub_model_id: jermyn/CodeQwen1.5-7B-Chat-NLQ2Cypher
hub_model_id: jermyn/deepseek-code-1.3b-inst-NLQ2Cypher
adapter: qlora
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: 1
micro_batch_size: 16
eval_batch_size: 16
num_epochs: 6
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.001
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>
# deepseek-code-1.3b-inst-NLQ2Cypher
This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3816
## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 49
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.8723 | 0.1429 | 1 | 1.6354 |
| 1.9222 | 0.2857 | 2 | 1.5979 |
| 1.5222 | 0.5714 | 4 | 1.0739 |
| 0.7251 | 0.8571 | 6 | 0.7059 |
| 0.3581 | 1.1429 | 8 | 0.6420 |
| 0.2804 | 1.4286 | 10 | 0.6576 |
| 0.2337 | 1.7143 | 12 | 0.4628 |
| 0.1988 | 2.0 | 14 | 0.3674 |
| 0.1218 | 2.2857 | 16 | 0.3528 |
| 0.1087 | 2.5714 | 18 | 0.3584 |
| 0.0842 | 2.8571 | 20 | 0.3569 |
| 0.0733 | 3.1429 | 22 | 0.3610 |
| 0.0943 | 3.4286 | 24 | 0.3477 |
| 0.058 | 3.7143 | 26 | 0.3575 |
| 0.0753 | 4.0 | 28 | 0.3797 |
| 0.0435 | 4.2857 | 30 | 0.3915 |
| 0.0758 | 4.5714 | 32 | 0.3902 |
| 0.0608 | 4.8571 | 34 | 0.3875 |
| 0.0368 | 5.1429 | 36 | 0.3853 |
| 0.0444 | 5.4286 | 38 | 0.3818 |
| 0.055 | 5.7143 | 40 | 0.3820 |
| 0.0532 | 6.0 | 42 | 0.3816 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1 |