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Weyaxi
/
Newton-7B

Text Generation
Transformers
Safetensors
mistral
axolotl
finetune
qlora
conversational
text-generation-inference
Inference Endpoints
Model card Files Community
2
Newton-7B
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---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: openchat/openchat-3.5-0106
model-index:
- name: newton-lora
  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: `0.4.0`
```yaml
base_model: openchat/openchat-3.5-0106
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false


datasets:
  - path: merged_all.json
    type:
      field_instruction: instruction
      field_output: output

      format: "GPT4 Correct User: {instruction}<|end_of_turn|>GPT4 Correct Assistant:"
      no_input_format: "GPT4 Correct User: {instruction}<|end_of_turn|>GPT4 Correct Assistant:"


dataset_prepared_path: last_run_prepared
val_set_size: 0.01 # not sure
output_dir: ./newton

adapter: qlora
lora_model_dir:

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

lora_r: 128
lora_alpha: 64
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
lora_modules_to_save:
  - embed_tokens
  - lm_head

wandb_project: huggingface
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

hub_model_id: Weyaxi/newton-lora
save_safetensors: true

# change #
gradient_accumulation_steps: 12
micro_batch_size: 6
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
# change #

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

warmup_steps: 10 # not sure

saves_per_epoch: 2

evals_per_epoch: 4
eval_table_size:
eval_table_max_new_tokens: 128

debug:
deepspeed:
weight_decay: 0.1 # not sure
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
tokens:
  - "<|end_of_turn|>"
  - "<|pad_0|>"


```

</details><br>

# newton-lora

This model is a fine-tuned version of [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0800

## 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: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 12
- total_train_batch_size: 72
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6925        | 0.02  | 1    | 1.3667          |
| 0.5622        | 0.25  | 16   | 0.3390          |
| 0.5269        | 0.5   | 32   | 0.1395          |
| 0.5343        | 0.75  | 48   | 0.1048          |
| 0.515         | 1.01  | 64   | 0.0904          |
| 0.3971        | 1.24  | 80   | 0.0854          |
| 0.3889        | 1.49  | 96   | 0.0820          |
| 0.3864        | 1.74  | 112  | 0.0800          |


### Framework versions

- PEFT 0.7.2.dev0
- Transformers 4.37.0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0