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---
library_name: peft
license: mit
base_model: databricks/dolly-v2-3b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 734ac95e-5bed-4ddb-b101-d50ed38eac5f
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: databricks/dolly-v2-3b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 4793329e84dea77c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4793329e84dea77c_train_data.json
type:
field_input: ''
field_instruction: category
field_output: prompt
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: leixa/734ac95e-5bed-4ddb-b101-d50ed38eac5f
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 5
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 400
micro_batch_size: 2
mlflow_experiment_name: /tmp/4793329e84dea77c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: leixa-personal
wandb_mode: online
wandb_name: 2e669d89-da91-45df-9742-0920494c3428
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 2e669d89-da91-45df-9742-0920494c3428
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 734ac95e-5bed-4ddb-b101-d50ed38eac5f
This model is a fine-tuned version of [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8039
## 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 42
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0241 | 1 | 2.2396 |
| 9.684 | 0.2651 | 11 | 2.0272 |
| 8.0437 | 0.5301 | 22 | 1.8766 |
| 8.2409 | 0.7952 | 33 | 1.8039 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1