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RichardErkhov/
TheSkullery_-_llama-3-cat-8b-instruct-v1-gguf

Name: TheSkullery_-_llama-3-cat-8b-instruct-v1-gguf
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llama-3-cat-8b-instruct-v1 - GGUF

Model creator: https://huggingface.co/TheSkullery/
Original model: https://huggingface.co/TheSkullery/llama-3-cat-8b-instruct-v1/

Name	Quant method	Size
llama-3-cat-8b-instruct-v1.Q2_K.gguf	Q2_K	2.96GB
llama-3-cat-8b-instruct-v1.IQ3_XS.gguf	IQ3_XS	3.28GB
llama-3-cat-8b-instruct-v1.IQ3_S.gguf	IQ3_S	3.43GB
llama-3-cat-8b-instruct-v1.Q3_K_S.gguf	Q3_K_S	3.41GB
llama-3-cat-8b-instruct-v1.IQ3_M.gguf	IQ3_M	3.52GB
llama-3-cat-8b-instruct-v1.Q3_K.gguf	Q3_K	3.74GB
llama-3-cat-8b-instruct-v1.Q3_K_M.gguf	Q3_K_M	3.74GB
llama-3-cat-8b-instruct-v1.Q3_K_L.gguf	Q3_K_L	4.03GB
llama-3-cat-8b-instruct-v1.IQ4_XS.gguf	IQ4_XS	4.18GB
llama-3-cat-8b-instruct-v1.Q4_0.gguf	Q4_0	4.34GB
llama-3-cat-8b-instruct-v1.IQ4_NL.gguf	IQ4_NL	4.38GB
llama-3-cat-8b-instruct-v1.Q4_K_S.gguf	Q4_K_S	4.37GB
llama-3-cat-8b-instruct-v1.Q4_K.gguf	Q4_K	4.58GB
llama-3-cat-8b-instruct-v1.Q4_K_M.gguf	Q4_K_M	4.58GB
llama-3-cat-8b-instruct-v1.Q4_1.gguf	Q4_1	4.78GB
llama-3-cat-8b-instruct-v1.Q5_0.gguf	Q5_0	5.21GB
llama-3-cat-8b-instruct-v1.Q5_K_S.gguf	Q5_K_S	5.21GB
llama-3-cat-8b-instruct-v1.Q5_K.gguf	Q5_K	5.34GB
llama-3-cat-8b-instruct-v1.Q5_K_M.gguf	Q5_K_M	5.34GB
llama-3-cat-8b-instruct-v1.Q5_1.gguf	Q5_1	5.65GB
llama-3-cat-8b-instruct-v1.Q6_K.gguf	Q6_K	6.14GB
llama-3-cat-8b-instruct-v1.Q8_0.gguf	Q8_0	7.95GB

Original model description:

license: llama3 model-index:

name: llama-3-cat-8b-instruct-v1 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: 59.04 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/llama-3-cat-8b-instruct-v1 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: 79.2 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/llama-3-cat-8b-instruct-v1 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: 62.99 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/llama-3-cat-8b-instruct-v1 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: 50.8 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/llama-3-cat-8b-instruct-v1 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: 75.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/llama-3-cat-8b-instruct-v1 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: 60.5 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/llama-3-cat-8b-instruct-v1 name: Open LLM Leaderboard

Presented by:

Dataset Builder: Dr. Kal'tsit (Kat)
Trainer/Funding: SteelSkull
Facilitator: Potatooff

70b variant of the model (Trained by Dr. Kal'tsit, Posted by Turboderp), Please check it out!

About:

Cat-llama3-instruct is a llama 3 8b finetuned model focusing on system prompt fidelity, helpfulness and character engagement. The model aims to respect system prompt to an extreme degree, provide helpful information regardless of situations, and offer maximum character immersion (Role Play) in given scenes.

Specific Aims:

System Instruction fidelity
Chain of Thought (COT)
Character immersion
Helpfulness for biosciences and general science

Methods

Dataset Preparation

Huggingface dataset containing instruction-response pairs was systematically pulled. A GPT model was trained on high quality and helpful responses exclusively to serve as a standard model.

(Fig1. Huggingface dataset population distribution and filtration for each component)

The dataset was further filtered for length and COT responses:

(Fig 2. COT responses are all >50 tokens single turn)

Health-related data was also pulled from Chat Doctor, favoring detailed and step-by-step diagnosis:

(Fig 3. Health related tasks are all >100 tokens, peaking at 450 tokens single turn)

Model Training

1*a100 for 6 days, 4 epochs

Usage:

Prompt format: llama 3

Settings/Context/Instruct (Provided by @Anthonyg5005)
Cat 8B Gen Settings
Cat 8b Context
Cat 8b Instruct

Quants:

[bartowski/llama-3-cat-8b-instruct-v1-GGUF]

 <|begin_of_text|>Below is a conversation between an user and an AI assistant. The ai assistant gives response regarding user's questions. USER: user: Given the task definition and input, reply with output. In this task, you are given a question and an answer, you would be asked to create the sentence based on the Question-Answer provided. It should be contained within the Question-Answer provided.
[object Object]


[object Object]
[object Object]
[object Object]
[object Object]
[object Object]
[object Object]
[object Object]

Double-check the sentence for proper grammar and punctuation, and make necessary adjustments if needed.
Final Output:
In humans, protist diseases are caused by protozoa.< / s ><|end_of_text|>

Showcases:

The model does COT in grey, calculated response in black; note that this behaviour is done through system card instruction to showcase the systemcard fidelity, it is not fine tuned into the model.

(Fig 4. Showcasing model doing COT to solve difficult tasks, extend and enrich its own answers)

# [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_TheSkullery__llama-3-cat-8b-instruct-v1)

Metric	Value
Avg.	64.74
AI2 Reasoning Challenge (25-Shot)	59.04
HellaSwag (10-Shot)	79.20
MMLU (5-Shot)	62.99
TruthfulQA (0-shot)	50.80
Winogrande (5-shot)	75.93
GSM8k (5-shot)	60.50

RichardErkhov/TheSkullery_-_llama-3-cat-8b-instruct-v1-gguf