README.md

---
library_name: transformers
tags:
- merge
- llama
- dpo
base_model:
- akjindal53244/Llama-3.1-Storm-8B
- Sao10K/L3.1-8B-Niitama-v1.1
- v000000/L3.1-Niitorm-8B-t0.0001
datasets:
- jondurbin/gutenberg-dpo-v0.1
model-index:
- name: L3.1-Niitorm-8B-DPO-t0.0001
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 76.89
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=v000000/L3.1-Niitorm-8B-DPO-t0.0001
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 30.51
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=v000000/L3.1-Niitorm-8B-DPO-t0.0001
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 14.88
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=v000000/L3.1-Niitorm-8B-DPO-t0.0001
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 5.93
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=v000000/L3.1-Niitorm-8B-DPO-t0.0001
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 7.26
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=v000000/L3.1-Niitorm-8B-DPO-t0.0001
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 31.85
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=v000000/L3.1-Niitorm-8B-DPO-t0.0001
      name: Open LLM Leaderboard
---

# 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)

# thank you, QuantFactory (GGUF)

* [GGUF static](https://huggingface.co/QuantFactory/L3.1-Niitorm-8B-DPO-t0.0001-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.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_v000000__L3.1-Niitorm-8B-DPO-t0.0001)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |27.89|
|IFEval (0-Shot)    |76.89|
|BBH (3-Shot)       |30.51|
|MATH Lvl 5 (4-Shot)|14.88|
|GPQA (0-shot)      | 5.93|
|MuSR (0-shot)      | 7.26|
|MMLU-PRO (5-shot)  |31.85|