---
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.*
New: DPO'd Gutenberg Version (full epoch training).
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 NEARSWAP t0.0001 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|