---
library_name: peft
license: other
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3341203d-19c2-42e8-a03c-3aa7d0910abb
  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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - ad2541ed21d87c9b_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/ad2541ed21d87c9b_train_data.json
  type:
    field_input: Doctor
    field_instruction: Patient
    field_output: Description
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: true
hub_model_id: robiulawaldev/3341203d-19c2-42e8-a03c-3aa7d0910abb
hub_strategy: end
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: constant
max_grad_norm: 1.0
max_memory:
  0: 75GB
max_steps: 10161
micro_batch_size: 4
mlflow_experiment_name: /tmp/ad2541ed21d87c9b_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
sequence_len: 512
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 9a0670a6-e952-4ba1-a194-cc1867f492a2
wandb_project: SN56-36
wandb_run: your_name
wandb_runid: 9a0670a6-e952-4ba1-a194-cc1867f492a2
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# 3341203d-19c2-42e8-a03c-3aa7d0910abb

This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0792

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 50
- training_steps: 10161

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| No log        | 0.0001 | 1     | 3.8251          |
| 1.2669        | 0.0328 | 500   | 1.2380          |
| 1.1848        | 0.0656 | 1000  | 1.1948          |
| 1.1665        | 0.0984 | 1500  | 1.1643          |
| 1.163         | 0.1312 | 2000  | 1.1529          |
| 1.1133        | 0.1640 | 2500  | 1.1495          |
| 1.1507        | 0.1968 | 3000  | 1.1242          |
| 1.1134        | 0.2296 | 3500  | 1.1237          |
| 1.1482        | 0.2624 | 4000  | 1.1153          |
| 1.0965        | 0.2952 | 4500  | 1.1135          |
| 1.0856        | 0.3280 | 5000  | 1.1133          |
| 1.0786        | 0.3608 | 5500  | 1.1169          |
| 1.123         | 0.3936 | 6000  | 1.0996          |
| 1.1018        | 0.4264 | 6500  | 1.0887          |
| 1.1398        | 0.4592 | 7000  | 1.0909          |
| 1.1163        | 0.4919 | 7500  | 1.0804          |
| 1.0601        | 0.5247 | 8000  | 1.0890          |
| 1.0735        | 0.5575 | 8500  | 1.0819          |
| 1.0293        | 0.5903 | 9000  | 1.0798          |
| 1.0653        | 0.6231 | 9500  | 1.0809          |
| 1.0664        | 0.6559 | 10000 | 1.0792          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1