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---
tags:
- merge
- mergekit
- lazymergekit
- flammenai/flammen15-gutenberg-DPO-v1-7B
- Eric111/CatunaLaserPi
base_model:
- flammenai/flammen15-gutenberg-DPO-v1-7B
- Eric111/CatunaLaserPi
model-index:
- name: GutenLaserPi
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: 42.27
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Stark2008/GutenLaserPi
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: 32.98
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Stark2008/GutenLaserPi
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: 7.18
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Stark2008/GutenLaserPi
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: 4.92
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Stark2008/GutenLaserPi
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: 16.99
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Stark2008/GutenLaserPi
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: 23.4
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Stark2008/GutenLaserPi
name: Open LLM Leaderboard
---
If you're interested in my models, you can vote for them to be evaluated on the [Leadarboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard).
# GutenLaserPi
GutenLaserPi is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [flammenai/flammen15-gutenberg-DPO-v1-7B](https://huggingface.co/flammenai/flammen15-gutenberg-DPO-v1-7B)
* [Eric111/CatunaLaserPi](https://huggingface.co/Eric111/CatunaLaserPi)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: flammenai/flammen15-gutenberg-DPO-v1-7B
layer_range: [0, 32]
- model: Eric111/CatunaLaserPi
layer_range: [0, 32]
merge_method: slerp
base_model: Eric111/CatunaLaserPi
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Stark2008/GutenLaserPi"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
# [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_Stark2008__GutenLaserPi)
| Metric |Value|
|-------------------|----:|
|Avg. |21.29|
|IFEval (0-Shot) |42.27|
|BBH (3-Shot) |32.98|
|MATH Lvl 5 (4-Shot)| 7.18|
|GPQA (0-shot) | 4.92|
|MuSR (0-shot) |16.99|
|MMLU-PRO (5-shot) |23.40|
|