bartowski/NeuralDaredevil-8B-abliterated-GGUF

license: other
tags:

• dpo
  datasets:
• mlabonne/orpo-dpo-mix-40k
  model-index:
• name: Daredevil-8B-abliterated-dpomix
  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: 69.28
      name: normalized accuracy
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B-abliterated-dpomix
      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: 85.05
      name: normalized accuracy
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B-abliterated-dpomix
      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: 69.1
      name: accuracy
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B-abliterated-dpomix
      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: 60.0
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B-abliterated-dpomix
      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: 78.69
      name: accuracy
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B-abliterated-dpomix
      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: 71.8
      name: accuracy
      source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B-abliterated-dpomix
      name: Open LLM Leaderboard

quantized_by: bartowski
pipeline_tag: text-generation

Llamacpp imatrix Quantizations of NeuralDaredevil-8B-abliterated

Using llama.cpp release b3086 for quantization.

Original model: https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated

All quants made using imatrix option with dataset from here

Prompt format

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Download a file (not the whole branch) from below:

Downloading using huggingface-cli

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download bartowski/NeuralDaredevil-8B-abliterated-GGUF --include "NeuralDaredevil-8B-abliterated-Q4_K_M.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download bartowski/NeuralDaredevil-8B-abliterated-GGUF --include "NeuralDaredevil-8B-abliterated-Q8_0.gguf/*" --local-dir NeuralDaredevil-8B-abliterated-Q8_0

You can either specify a new local-dir (NeuralDaredevil-8B-abliterated-Q8_0) or download them all in place (./)

Which file should I choose?

A great write up with charts showing various performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

The I-quants are not compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski