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Daredevil-8B - GGUF
- Model creator: https://huggingface.co/mlabonne/
- Original model: https://huggingface.co/mlabonne/Daredevil-8B/
| Name | Quant method | Size |
|---|---|---|
| Daredevil-8B.Q2_K.gguf | Q2_K | 2.96GB |
| Daredevil-8B.IQ3_XS.gguf | IQ3_XS | 3.28GB |
| Daredevil-8B.IQ3_S.gguf | IQ3_S | 3.43GB |
| Daredevil-8B.Q3_K_S.gguf | Q3_K_S | 3.41GB |
| Daredevil-8B.IQ3_M.gguf | IQ3_M | 3.52GB |
| Daredevil-8B.Q3_K.gguf | Q3_K | 3.74GB |
| Daredevil-8B.Q3_K_M.gguf | Q3_K_M | 3.74GB |
| Daredevil-8B.Q3_K_L.gguf | Q3_K_L | 4.03GB |
| Daredevil-8B.IQ4_XS.gguf | IQ4_XS | 4.18GB |
| Daredevil-8B.Q4_0.gguf | Q4_0 | 4.34GB |
| Daredevil-8B.IQ4_NL.gguf | IQ4_NL | 4.38GB |
| Daredevil-8B.Q4_K_S.gguf | Q4_K_S | 4.37GB |
| Daredevil-8B.Q4_K.gguf | Q4_K | 4.58GB |
| Daredevil-8B.Q4_K_M.gguf | Q4_K_M | 4.58GB |
| Daredevil-8B.Q4_1.gguf | Q4_1 | 4.78GB |
| Daredevil-8B.Q5_0.gguf | Q5_0 | 5.21GB |
| Daredevil-8B.Q5_K_S.gguf | Q5_K_S | 5.21GB |
| Daredevil-8B.Q5_K.gguf | Q5_K | 5.34GB |
| Daredevil-8B.Q5_K_M.gguf | Q5_K_M | 5.34GB |
| Daredevil-8B.Q5_1.gguf | Q5_1 | 5.65GB |
| Daredevil-8B.Q6_K.gguf | Q6_K | 6.14GB |
| Daredevil-8B.Q8_0.gguf | Q8_0 | 7.95GB |
Original model description:
license: other tags: - merge - mergekit - lazymergekit base_model: - nbeerbower/llama-3-stella-8B - Hastagaras/llama-3-8b-okay - nbeerbower/llama-3-gutenberg-8B - openchat/openchat-3.6-8b-20240522 - Kukedlc/NeuralLLaMa-3-8b-DT-v0.1 - cstr/llama3-8b-spaetzle-v20 - mlabonne/ChimeraLlama-3-8B-v3 - flammenai/Mahou-1.1-llama3-8B - KingNish/KingNish-Llama3-8b model-index: - name: Daredevil-8B 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: 68.86 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B 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: 84.5 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B 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: 69.24 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B 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: 59.89 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B 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: 78.45 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B 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: 73.54 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B name: Open LLM Leaderboard
Daredevil-8B
Daredevil-8B is a mega-merge designed to maximize MMLU. On 27 May 24, it is the Llama 3 8B model with the highest MMLU score. From my experience, a high MMLU score is all you need with Llama 3 models.
It is a merge of the following models using LazyMergekit:
- nbeerbower/llama-3-stella-8B
- Hastagaras/llama-3-8b-okay
- nbeerbower/llama-3-gutenberg-8B
- openchat/openchat-3.6-8b-20240522
- Kukedlc/NeuralLLaMa-3-8b-DT-v0.1
- cstr/llama3-8b-spaetzle-v20
- mlabonne/ChimeraLlama-3-8B-v3
- flammenai/Mahou-1.1-llama3-8B
- KingNish/KingNish-Llama3-8b
Thanks to nbeerbower, Hastagaras, openchat, Kukedlc, cstr, flammenai, and KingNish for their merges. Special thanks to Charles Goddard and Arcee.ai for MergeKit.
🔎 Applications
You can use it as an improved version of meta-llama/Meta-Llama-3-8B-Instruct.
This is a censored model. For an uncensored version, see mlabonne/Daredevil-8B-abliterated.
Tested on LM Studio using the "Llama 3" preset.
⚡ Quantization
🏆 Evaluation
Open LLM Leaderboard
Daredevil-8B is the best-performing 8B model on the Open LLM Leaderboard in terms of MMLU score (27 May 24).
Nous
Daredevil-8B is the best-performing 8B model on Nous' benchmark suite (evaluation performed using LLM AutoEval, 27 May 24). See the entire leaderboard here.
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---|---|---|---|---|
| mlabonne/Daredevil-8B 📄 | 55.87 | 44.13 | 73.52 | 59.05 | 46.77 |
| mlabonne/Daredevil-8B-abliterated 📄 | 55.06 | 43.29 | 73.33 | 57.47 | 46.17 |
| mlabonne/Llama-3-8B-Instruct-abliterated-dpomix 📄 | 52.26 | 41.6 | 69.95 | 54.22 | 43.26 |
| meta-llama/Meta-Llama-3-8B-Instruct 📄 | 51.34 | 41.22 | 69.86 | 51.65 | 42.64 |
| failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 📄 | 51.21 | 40.23 | 69.5 | 52.44 | 42.69 |
| mlabonne/OrpoLlama-3-8B 📄 | 48.63 | 34.17 | 70.59 | 52.39 | 37.36 |
| meta-llama/Meta-Llama-3-8B 📄 | 45.42 | 31.1 | 69.95 | 43.91 | 36.7 |
🌳 Model family tree
🧩 Configuration
models:
- model: NousResearch/Meta-Llama-3-8B
# No parameters necessary for base model
- model: nbeerbower/llama-3-stella-8B
parameters:
density: 0.6
weight: 0.16
- model: Hastagaras/llama-3-8b-okay
parameters:
density: 0.56
weight: 0.1
- model: nbeerbower/llama-3-gutenberg-8B
parameters:
density: 0.6
weight: 0.18
- model: openchat/openchat-3.6-8b-20240522
parameters:
density: 0.56
weight: 0.12
- model: Kukedlc/NeuralLLaMa-3-8b-DT-v0.1
parameters:
density: 0.58
weight: 0.18
- model: cstr/llama3-8b-spaetzle-v20
parameters:
density: 0.56
weight: 0.08
- model: mlabonne/ChimeraLlama-3-8B-v3
parameters:
density: 0.56
weight: 0.08
- model: flammenai/Mahou-1.1-llama3-8B
parameters:
density: 0.55
weight: 0.05
- model: KingNish/KingNish-Llama3-8b
parameters:
density: 0.55
weight: 0.05
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/Daredevil-8B"
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.bfloat16,
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"])
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