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L3.1-Niitorm-8B-DPO-t0.0001-GGUF

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L3.1-Niitorm-8B-DPO-t0.0001-GGUF
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---

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

---

[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)


# QuantFactory/L3.1-Niitorm-8B-DPO-t0.0001-GGUF
This is quantized version of [v000000/L3.1-Niitorm-8B-DPO-t0.0001](https://huggingface.co./v000000/L3.1-Niitorm-8B-DPO-t0.0001) created using llama.cpp

# Original Model Card


# 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", then DPO'd, mostly abliterated.

Essentially, it's an improved Niitama 1.1

-------------------------------------------------------------------------------

*Gutenberg DPO creates more human-like prose/story writing and greately lessen synthetic feeling outputs.*

-------------------------------------------------------------------------------

# *llama.cpp:*

# thank you, mradermacher (GGUF)

* [GGUF static](https://huggingface.co./mradermacher/L3.1-Niitorm-8B-DPO-t0.0001-GGUF)

* [GGUF Imatrix](https://huggingface.co./mradermacher/L3.1-Niitorm-8B-DPO-t0.0001-i1-GGUF)

# v0 (GGUF)

* [GGUF Imatrix](https://huggingface.co./v000000/L3.1-Niitorm-8B-DPO-t0.0001-GGUFs-IMATRIX) *-only q8, q6 k, q5 k s, q4 k s, iq4 x s*


## 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: float16

# Then, DPO Finetune
# [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co./datasets/jondurbin/gutenberg-dpo-v0.1)

```

### DPO Notes

*I used a higher learning rate and full dataset when training compared to my "L3.1-Celestial-Stone-2x8B-DPO". This caused lower loss and better adaption to the chosen style.*

-------------------------------------------------------------------------------

# 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 Sao10K.

Credit to Grimjim.

Credit to mlabonne.

Credit to jondurbin.

Credit to woofwolfy.