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RichardErkhov/
shadowml_-_Daredevil-7B-gguf

Name: shadowml_-_Daredevil-7B-gguf
Availability: InStock

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gguf

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Daredevil-7B - GGUF

Model creator: https://huggingface.co/shadowml/
Original model: https://huggingface.co/shadowml/Daredevil-7B/

Name	Quant method	Size
Daredevil-7B.Q2_K.gguf	Q2_K	2.53GB
Daredevil-7B.IQ3_XS.gguf	IQ3_XS	2.81GB
Daredevil-7B.IQ3_S.gguf	IQ3_S	2.96GB
Daredevil-7B.Q3_K_S.gguf	Q3_K_S	2.95GB
Daredevil-7B.IQ3_M.gguf	IQ3_M	3.06GB
Daredevil-7B.Q3_K.gguf	Q3_K	3.28GB
Daredevil-7B.Q3_K_M.gguf	Q3_K_M	3.28GB
Daredevil-7B.Q3_K_L.gguf	Q3_K_L	3.56GB
Daredevil-7B.IQ4_XS.gguf	IQ4_XS	3.67GB
Daredevil-7B.Q4_0.gguf	Q4_0	3.83GB
Daredevil-7B.IQ4_NL.gguf	IQ4_NL	3.87GB
Daredevil-7B.Q4_K_S.gguf	Q4_K_S	3.86GB
Daredevil-7B.Q4_K.gguf	Q4_K	4.07GB
Daredevil-7B.Q4_K_M.gguf	Q4_K_M	4.07GB
Daredevil-7B.Q4_1.gguf	Q4_1	4.24GB
Daredevil-7B.Q5_0.gguf	Q5_0	4.65GB
Daredevil-7B.Q5_K_S.gguf	Q5_K_S	4.65GB
Daredevil-7B.Q5_K.gguf	Q5_K	4.78GB
Daredevil-7B.Q5_K_M.gguf	Q5_K_M	4.78GB
Daredevil-7B.Q5_1.gguf	Q5_1	5.07GB
Daredevil-7B.Q6_K.gguf	Q6_K	5.53GB
Daredevil-7B.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 tags:

merge
mergekit
lazymergekit
samir-fama/SamirGPT-v1
abacusai/Slerp-CM-mist-dpo
EmbeddedLLM/Mistral-7B-Merge-14-v0.2 model-index:
name: Daredevil-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: 69.37 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Daredevil-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: 87.17 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Daredevil-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: 65.3 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Daredevil-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: 64.09 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Daredevil-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: 81.29 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Daredevil-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: 72.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Daredevil-7B name: Open LLM Leaderboard

Daredevil-7B

Daredevil-7B is a merge of the following models using LazyMergekit:

🧩 Configuration

yaml

models:
  - model: mistralai/Mistral-7B-v0.1
    # No parameters necessary for base model
  - model: samir-fama/SamirGPT-v1
    parameters:
      density: 0.53
      weight: 0.4
  - model: abacusai/Slerp-CM-mist-dpo
    parameters:
      density: 0.53
      weight: 0.3
  - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.2
    parameters:
      density: 0.53
      weight: 0.3
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "shadowml/Daredevil-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.	73.36
AI2 Reasoning Challenge (25-Shot)	69.37
HellaSwag (10-Shot)	87.17
MMLU (5-Shot)	65.30
TruthfulQA (0-shot)	64.09
Winogrande (5-shot)	81.29
GSM8k (5-shot)	72.93

RichardErkhov/shadowml_-_Daredevil-7B-gguf