---
base_model:
- akjindal53244/Llama-3.1-Storm-8B
- Sao10K/L3.1-8B-Niitama-v1.1
- v000000/L3.1-Niitorm-8B-t0.0001
library_name: transformers
tags:
- merge
- llama
- dpo
datasets:
- jondurbin/gutenberg-dpo-v0.1
---

# Llama-3.1-Niitorm-8B-DPO

* *DPO Trained, Llama3.1-8B.*

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64f74b6e6389380c77562762/QeNjtwolNpxUmpo9NL7VI.png)

<b>New: DPO'd Gutenberg Version (full epoch training).</b>

RP model, Niitama 1.1 as a base, nearswapped with one of the smartest 3.1 models "Storm", mostly abliterated.

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*Gutenberg dataset creates more human writer-like prose and greately lessen synthetic feeling outputs.*

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## Finetune and merge

This is a merge and finetune of pre-trained language models.

*Resultant merge finetuned* on [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1) for 1 epoch, 1.5e-5 learning rate, on Nvidia A100.

## Merge Details
### Merge Method

This model was merged using the <b>NEARSWAP t0.0001</b> merge algorithm.

### Models Merged

The following models were included in the merge:
* Base Model: [Sao10K/L3.1-8B-Niitama-v1.1](https://huggingface.co/Sao10K/L3.1-8B-Niitama-v1.1) + [grimjim/Llama-3-Instruct-abliteration-LoRA-8B](https://huggingface.co/grimjim/Llama-3-Instruct-abliteration-LoRA-8B)
* [akjindal53244/Llama-3.1-Storm-8B](https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
slices:
  - sources:
      - model: Sao10K/L3.1-8B-Niitama-v1.1+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
        layer_range: [0, 32]
      - model: akjindal53244/Llama-3.1-Storm-8B
        layer_range: [0, 32]
merge_method: nearswap
base_model: Sao10K/L3.1-8B-Niitama-v1.1+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
parameters:
  t:
    - value: 0.0001
dtype: bfloat16

# Then, DPO Finetune

```

*Resultant merge finetuned* on [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1) for 1 epoch, 1.5e-5 learning rate, on Nvidia A100.

*I used a higher learning rate and full dataset compared to "L3.1-Celestial-Stone-2x8B-DPO".*

# Prompt Template:
```bash
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{output}<|eot_id|>

```

Credit to Alchemonaut.

Credit to jondurbin.

Credit to woofwolfy.