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allknowingroger
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Ministral-8B-slerp

Safetensors
mistral
Merge
mergekit
lazymergekit
prince-canuma/Ministral-8B-Instruct-2410-HF
Eval Results
Model card Files Community
2
Ministral-8B-slerp
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---
tags:
- merge
- mergekit
- lazymergekit
- prince-canuma/Ministral-8B-Instruct-2410-HF
base_model:
- prince-canuma/Ministral-8B-Instruct-2410-HF
- prince-canuma/Ministral-8B-Instruct-2410-HF
model-index:
- name: Ministral-8B-slerp
  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: 19.61
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Ministral-8B-slerp
      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: 25.2
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Ministral-8B-slerp
      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: 0.0
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Ministral-8B-slerp
      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: 8.28
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Ministral-8B-slerp
      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: 12.4
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Ministral-8B-slerp
      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: 23.55
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Ministral-8B-slerp
      name: Open LLM Leaderboard
---

# Ministral-8B-slerp

Ministral-8B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [prince-canuma/Ministral-8B-Instruct-2410-HF](https://huggingface.co/prince-canuma/Ministral-8B-Instruct-2410-HF)
* [prince-canuma/Ministral-8B-Instruct-2410-HF](https://huggingface.co/prince-canuma/Ministral-8B-Instruct-2410-HF)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: prince-canuma/Ministral-8B-Instruct-2410-HF
        layer_range: [0, 32]
      - model: prince-canuma/Ministral-8B-Instruct-2410-HF
        layer_range: [0, 32]
merge_method: slerp
base_model: prince-canuma/Ministral-8B-Instruct-2410-HF
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: float32
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "allknowingroger/Ministral-8B-slerp"
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_allknowingroger__Ministral-8B-slerp)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |14.84|
|IFEval (0-Shot)    |19.61|
|BBH (3-Shot)       |25.20|
|MATH Lvl 5 (4-Shot)| 0.00|
|GPQA (0-shot)      | 8.28|
|MuSR (0-shot)      |12.40|
|MMLU-PRO (5-shot)  |23.55|