README.md

---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- merge
- mergekit
- lazymergekit
- mistral
- roleplay
- ResplendentAI/Datura_7B
- Epiculous/Mika-7B
base_model:
- ResplendentAI/Datura_7B
- Epiculous/Mika-7B
model-index:
- name: Foxglove_7B
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 67.83
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aridoverrun/Foxglove_7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 86.57
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aridoverrun/Foxglove_7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 62.89
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aridoverrun/Foxglove_7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 69.64
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aridoverrun/Foxglove_7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 80.74
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aridoverrun/Foxglove_7B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 44.96
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aridoverrun/Foxglove_7B
      name: Open LLM Leaderboard
---

<img src="https://cdn-uploads.huggingface.co/production/uploads/65ad2502043d53781aad2ee4/FUH__CjalqBRPiSaqZfO6.png" alt="image" width="540" height="540" style="margin-bottom: 30px;">

# 🌸 Foxglove_7B
Foxglove is a well-rounded RP model. It is smart, does a great job of sticking to character card, and is proficient at following desired markdown.

Foxglove_7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [ResplendentAI/Datura_7B](https://huggingface.co/ResplendentAI/Datura_7B)
* [Epiculous/Mika-7B](https://huggingface.co/Epiculous/Mika-7B)

## Quantizations
Thanks to mradermacher, static GGUF quants are available [here](https://huggingface.co/mradermacher/Foxglove_7B-GGUF).

## Formatting/Preset
Alpaca works best, but Mistral provides good outputs as well.

## Configuration

```yaml
slices:
  - sources:
      - model: ResplendentAI/Datura_7B
        layer_range: [0, 32]
      - model: Epiculous/Mika-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: ResplendentAI/Datura_7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.7, 0.4, 0.6, 1]  
    - filter: mlp
      value: [0.8, 0.5, 0.7, 0.3, 0]  
    - value: 0.6  
dtype: bfloat16
```

## Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "rmdhirr/Foxglove_7B"
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/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_aridoverrun__Foxglove_7B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |68.77|
|AI2 Reasoning Challenge (25-Shot)|67.83|
|HellaSwag (10-Shot)              |86.57|
|MMLU (5-Shot)                    |62.89|
|TruthfulQA (0-shot)              |69.64|
|Winogrande (5-shot)              |80.74|
|GSM8k (5-shot)                   |44.96|