---
library_name: peft
license: other
base_model: sethuiyer/Medichat-Llama3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: feddaca4-cb5c-4f8e-8b46-288b425ee1c3
  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: sethuiyer/Medichat-Llama3-8B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 4d445458bcc291df_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/4d445458bcc291df_train_data.json
  type:
    field_input: counter_statement
    field_instruction: question
    field_output: counter_longer
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: dsakerkwq/feddaca4-cb5c-4f8e-8b46-288b425ee1c3
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 75GiB
max_steps: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/4d445458bcc291df_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
s2_attention: false
sample_packing: false
saves_per_epoch: 4
sequence_len: 2048
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: feddaca4-cb5c-4f8e-8b46-288b425ee1c3
wandb_project: Gradients-On-Demand
wandb_runid: feddaca4-cb5c-4f8e-8b46-288b425ee1c3
warmup_steps: 100
weight_decay: 0.01
xformers_attention: false

```

</details><br>

# feddaca4-cb5c-4f8e-8b46-288b425ee1c3

This model is a fine-tuned version of [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/sethuiyer/Medichat-Llama3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6842

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 100
- training_steps: 30

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9424        | 0.0015 | 1    | 1.2899          |
| 1.0601        | 0.0046 | 3    | 1.2885          |
| 1.1404        | 0.0092 | 6    | 1.2818          |
| 1.0557        | 0.0138 | 9    | 1.2536          |
| 1.1208        | 0.0184 | 12   | 1.1905          |
| 0.986         | 0.0231 | 15   | 1.0825          |
| 0.8857        | 0.0277 | 18   | 0.9516          |
| 0.8378        | 0.0323 | 21   | 0.8335          |
| 0.7278        | 0.0369 | 24   | 0.7376          |
| 0.7035        | 0.0415 | 27   | 0.7180          |
| 0.6894        | 0.0461 | 30   | 0.6842          |


### Framework versions

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