Isaak Carter Augustus
Update README.md
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metadata
license: apache-2.0
tags:
  - merge
  - mergekit
  - lazymergekit
  - cognitivecomputations/dolphin-2.8-experiment26-7b
  - argilla/CapybaraHermes-2.5-Mistral-7B
base_model:
  - cognitivecomputations/dolphin-2.8-experiment26-7b
  - argilla/CapybaraHermes-2.5-Mistral-7B
language:
  - en
  - de
  - es
  - fr
  - ja
  - zh

J.O.S.I.E.3-Beta11-7B-slerp

J.O.S.I.E.3-Beta11-7B-slerp is a merge of the following models using LazyMergekit:

-- GGUF Quants --

Run in ollama:

ollama run goekdenizguelmez/j.o.s.i.e.v3-beta11

Only Quant 4-k-m for now!

This model will bee further Finetuned on my custom J.O.S.I.E.v3.13 Dataset, in the ChatML prompt Format.

<|im_start|>system
You are JOSIE, a private and super-intelligent AI assistant, created by Gökdeniz Gülmez.<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
{{ .Response }}<|im_end|>

🧩 Configuration

slices:
  - sources:
      - model: cognitivecomputations/dolphin-2.8-experiment26-7b
        layer_range: [0, 32]
      - model: argilla/CapybaraHermes-2.5-Mistral-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: argilla/CapybaraHermes-2.5-Mistral-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

Evaluation

{
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        "acc_norm": 0.6413927640714372,
        "acc_norm_stderr": 0.03294011331780708,
        "mc1": 0.39167686658506734,
        "mc1_stderr": 0.017087795881769622,
        "mc2": 0.5576866593959974,
        "mc2_stderr": 0.01554622060467735
    },
    "harness|arc:challenge|25": {
        "acc": 0.6186006825938567,
        "acc_stderr": 0.014194389086685244,
        "acc_norm": 0.6450511945392492,
        "acc_norm_stderr": 0.013983036904094087
    },
    "harness|hellaswag|10": {
        "acc": 0.6738697470623382,
        "acc_stderr": 0.004678375103797962,
        "acc_norm": 0.8499302927703645,
        "acc_norm_stderr": 0.003564098420387764
    },
    "harness|hendrycksTest-abstract_algebra|5": {
        "acc": 0.28,
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        "acc_norm": 0.28,
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    },
    "harness|hendrycksTest-anatomy|5": {
        "acc": 0.5777777777777777,
        "acc_stderr": 0.04266763404099582,
        "acc_norm": 0.5777777777777777,
        "acc_norm_stderr": 0.04266763404099582
    },
    "harness|hendrycksTest-astronomy|5": {
        "acc": 0.6907894736842105,
        "acc_stderr": 0.037610708698674805,
        "acc_norm": 0.6907894736842105,
        "acc_norm_stderr": 0.037610708698674805
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    "harness|hendrycksTest-business_ethics|5": {
        "acc": 0.63,
        "acc_stderr": 0.04852365870939099,
        "acc_norm": 0.63,
        "acc_norm_stderr": 0.04852365870939099
    },
    "harness|hendrycksTest-clinical_knowledge|5": {
        "acc": 0.6830188679245283,
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        "acc_norm": 0.6830188679245283,
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    "harness|hendrycksTest-college_biology|5": {
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    "harness|hendrycksTest-college_chemistry|5": {
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        "acc_norm": 0.48,
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    "harness|hendrycksTest-college_computer_science|5": {
        "acc": 0.51,
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    "harness|hendrycksTest-college_mathematics|5": {
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    "harness|hendrycksTest-computer_security|5": {
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    "harness|hendrycksTest-econometrics|5": {
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    "harness|hendrycksTest-electrical_engineering|5": {
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    "harness|hendrycksTest-elementary_mathematics|5": {
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    "harness|hendrycksTest-global_facts|5": {
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    "harness|hendrycksTest-high_school_biology|5": {
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    "harness|hendrycksTest-high_school_european_history|5": {
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    "harness|hendrycksTest-high_school_macroeconomics|5": {
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    "harness|hendrycksTest-high_school_mathematics|5": {
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    "harness|hendrycksTest-high_school_microeconomics|5": {
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    "harness|hendrycksTest-high_school_physics|5": {
        "acc": 0.3576158940397351,
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        "acc_norm": 0.3576158940397351,
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    },
    "harness|hendrycksTest-high_school_psychology|5": {
        "acc": 0.8348623853211009,
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    "harness|hendrycksTest-high_school_statistics|5": {
        "acc": 0.5138888888888888,
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        "acc_norm": 0.5138888888888888,
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    "harness|hendrycksTest-high_school_us_history|5": {
        "acc": 0.7892156862745098,
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    "harness|hendrycksTest-high_school_world_history|5": {
        "acc": 0.8143459915611815,
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    "harness|hendrycksTest-human_aging|5": {
        "acc": 0.6995515695067265,
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        "acc_norm": 0.6995515695067265,
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    "harness|hendrycksTest-human_sexuality|5": {
        "acc": 0.7938931297709924,
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    "harness|hendrycksTest-international_law|5": {
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    "harness|hendrycksTest-jurisprudence|5": {
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    "harness|hendrycksTest-logical_fallacies|5": {
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    "harness|hendrycksTest-machine_learning|5": {
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    "harness|hendrycksTest-management|5": {
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    "harness|hendrycksTest-marketing|5": {
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    "harness|hendrycksTest-medical_genetics|5": {
        "acc": 0.69,
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    "harness|hendrycksTest-miscellaneous|5": {
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    "harness|hendrycksTest-moral_disputes|5": {
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    "harness|hendrycksTest-nutrition|5": {
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    "harness|hendrycksTest-philosophy|5": {
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    "harness|hendrycksTest-prehistory|5": {
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    "harness|hendrycksTest-professional_accounting|5": {
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    "harness|hendrycksTest-professional_law|5": {
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    "harness|hendrycksTest-professional_medicine|5": {
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    "harness|hendrycksTest-professional_psychology|5": {
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    "harness|hendrycksTest-public_relations|5": {
        "acc": 0.6636363636363637,
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    "harness|hendrycksTest-security_studies|5": {
        "acc": 0.7387755102040816,
        "acc_stderr": 0.02812342933514278,
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    "harness|hendrycksTest-sociology|5": {
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    "harness|hendrycksTest-us_foreign_policy|5": {
        "acc": 0.83,
        "acc_stderr": 0.0377525168068637,
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    "harness|hendrycksTest-virology|5": {
        "acc": 0.5421686746987951,
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    "harness|hendrycksTest-world_religions|5": {
        "acc": 0.8187134502923976,
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        "acc_norm": 0.8187134502923976,
        "acc_norm_stderr": 0.029547741687640038
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    "harness|truthfulqa:mc|0": {
        "mc1": 0.39167686658506734,
        "mc1_stderr": 0.017087795881769622,
        "mc2": 0.5576866593959974,
        "mc2_stderr": 0.01554622060467735
    },
    "harness|winogrande|5": {
        "acc": 0.7884767166535123,
        "acc_stderr": 0.011477747684223188
    },
    "harness|gsm8k|5": {
        "acc": 0.6360879454131918,
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}

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Isaak-Carter/J.O.S.I.E.3-Beta11-7B-slerp"
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"])