🌸 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:

• ResplendentAI/Datura_7B
• Epiculous/Mika-7B

Quantizations

Thanks to mradermacher, static GGUF quants are available here.

Formatting/Preset

Alpaca works best, but Mistral provides good outputs as well.

Configuration

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

!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

Detailed results can be found here

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