J.O.S.I.E.3 Beta12 7B Slerp

🚀 J.O.S.I.E.3-Beta12-7B-slerp

J.O.S.I.E.3-Beta12-7B-slerpは、LazyMergekitを使用して以下のモデルをマージしたものです。

• Weyaxi/Einstein-v6-7B
• argilla/CapybaraHermes-2.5-Mistral-7B

このモデルは、私独自のJ.O.S.I.E.v3.11データセットを使用して、ChatMLプロンプト形式でさらにファインチューニングされています。

-- GGUF Quants --

<|im_start|>system
You are JOSIE, my private and superinteligent AI Assistant.<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
{{ .Response }}<|im_end|>

🚀 クイックスタート

ollamaでの実行

ollama run goekdenizguelmez/j.o.s.i.e.v3-beta12.1

現時点ではq4-k-mのみです！

✨ 主な機能

• 複数のモデルをマージした高性能な言語モデル。
• 独自のデータセットでファインチューニングされ、特定のタスクに適している。

📦 インストール

!pip install -qU transformers accelerate

💻 使用例

基本的な使用法

from transformers import AutoTokenizer
import transformers
import torch

model = "Isaak-Carter/J.O.S.I.E.3-Beta12-7B-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"])

📚 ドキュメント

🧩 設定

slices:
  - sources:
      - model: Weyaxi/Einstein-v6-7B
        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

🔧 技術詳細

評価結果

{
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        "acc": 0.635008846776534,
        "acc_stderr": 0.03244450973873997,
        "acc_norm": 0.6365238167399629,
        "acc_norm_stderr": 0.033101612504829854,
        "mc1": 0.397796817625459,
        "mc1_stderr": 0.017133934248559635,
        "mc2": 0.5816259277988214,
        "mc2_stderr": 0.01521267822060948
    },
    "harness|arc:challenge|25": {
        "acc": 0.6220136518771331,
        "acc_stderr": 0.0141696645203031,
        "acc_norm": 0.6459044368600683,
        "acc_norm_stderr": 0.013975454122756557
    },
    "harness|hellaswag|10": {
        "acc": 0.6512646883091018,
        "acc_stderr": 0.004755960559929163,
        "acc_norm": 0.8397729535949015,
        "acc_norm_stderr": 0.003660668242740655
    },
    "harness|hendrycksTest-abstract_algebra|5": {
        "acc": 0.4,
        "acc_stderr": 0.04923659639173309,
        "acc_norm": 0.4,
        "acc_norm_stderr": 0.04923659639173309
    },
    "harness|hendrycksTest-anatomy|5": {
        "acc": 0.5703703703703704,
        "acc_stderr": 0.042763494943765995,
        "acc_norm": 0.5703703703703704,
        "acc_norm_stderr": 0.042763494943765995
    },
    "harness|hendrycksTest-astronomy|5": {
        "acc": 0.6842105263157895,
        "acc_stderr": 0.0378272898086547,
        "acc_norm": 0.6842105263157895,
        "acc_norm_stderr": 0.0378272898086547
    },
    "harness|hendrycksTest-business_ethics|5": {
        "acc": 0.58,
        "acc_stderr": 0.049604496374885836,
        "acc_norm": 0.58,
        "acc_norm_stderr": 0.049604496374885836
    },
    "harness|hendrycksTest-clinical_knowledge|5": {
        "acc": 0.6792452830188679,
        "acc_stderr": 0.028727502957880267,
        "acc_norm": 0.6792452830188679,
        "acc_norm_stderr": 0.028727502957880267
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    "harness|hendrycksTest-college_biology|5": {
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        "acc_norm": 0.7361111111111112,
        "acc_norm_stderr": 0.03685651095897532
    },
    "harness|hendrycksTest-college_chemistry|5": {
        "acc": 0.54,
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        "acc_norm": 0.54,
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    },
    "harness|hendrycksTest-college_computer_science|5": {
        "acc": 0.51,
        "acc_stderr": 0.05024183937956912,
        "acc_norm": 0.51,
        "acc_norm_stderr": 0.05024183937956912
    },
    "harness|hendrycksTest-college_mathematics|5": {
        "acc": 0.29,
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        "acc_norm": 0.29,
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    "harness|hendrycksTest-college_medicine|5": {
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    "harness|hendrycksTest-college_physics|5": {
        "acc": 0.3235294117647059,
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        "acc_norm": 0.3235294117647059,
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    "harness|hendrycksTest-computer_security|5": {
        "acc": 0.76,
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        "acc_norm": 0.76,
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    "harness|hendrycksTest-conceptual_physics|5": {
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    "harness|hendrycksTest-econometrics|5": {
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    "harness|hendrycksTest-formal_logic|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_chemistry|5": {
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        "acc_norm": 0.5024630541871922,
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    "harness|hendrycksTest-high_school_geography|5": {
        "acc": 0.797979797979798,
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    "harness|hendrycksTest-high_school_government_and_politics|5": {
        "acc": 0.8756476683937824,
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        "acc_norm": 0.8756476683937824,
        "acc_norm_stderr": 0.023814477086593552
    },
    "harness|hendrycksTest-high_school_macroeconomics|5": {
        "acc": 0.658974358974359,
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        "acc_norm": 0.658974358974359,
        "acc_norm_stderr": 0.02403548967633509
    },
    "harness|hendrycksTest-high_school_mathematics|5": {
        "acc": 0.32592592592592595,
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    "harness|hendrycksTest-high_school_microeconomics|5": {
        "acc": 0.6638655462184874,
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        "acc_norm": 0.6638655462184874,
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    },
    "harness|hendrycksTest-high_school_physics|5": {
        "acc": 0.304635761589404,
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        "acc_norm": 0.304635761589404,
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    },
    "harness|hendrycksTest-high_school_psychology|5": {
        "acc": 0.8238532110091743,
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        "acc_norm": 0.8238532110091743,
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    "harness|hendrycksTest-high_school_statistics|5": {
        "acc": 0.5092592592592593,
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    "harness|hendrycksTest-high_school_us_history|5": {
        "acc": 0.7990196078431373,
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    "harness|hendrycksTest-high_school_world_history|5": {
        "acc": 0.759493670886076,
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    "harness|hendrycksTest-human_aging|5": {
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    "harness|hendrycksTest-human_sexuality|5": {
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    "harness|hendrycksTest-international_law|5": {
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    "harness|hendrycksTest-jurisprudence|5": {
        "acc": 0.7777777777777778,
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    "harness|hendrycksTest-logical_fallacies|5": {
        "acc": 0.754601226993865,
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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": {
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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-professional_accounting|5": {
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    "harness|hendrycksTest-professional_psychology|5": {
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    "harness|hendrycksTest-public_relations|5": {
        "acc": 0.6727272727272727,
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        "acc_norm_stderr": 0.0449429086625209
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    "harness|hendrycksTest-security_studies|5": {
        "acc": 0.6816326530612244,
        "acc_stderr": 0.029822533793982062,
        "acc_norm": 0.6816326530612244,
        "acc_norm_stderr": 0.029822533793982062
    },
    "harness|hendrycksTest-sociology|5": {
        "acc": 0.8507462686567164,
        "acc_stderr": 0.025196929874827072,
        "acc_norm": 0.8507462686567164,
        "acc_norm_stderr": 0.025196929874827072
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    "harness|hendrycksTest-us_foreign_policy|5": {
        "acc": 0.85,
        "acc_stderr": 0.035887028128263734,
        "acc_norm": 0.85,
        "acc_norm_stderr": 0.035887028128263734
    }
}

📄 ライセンス

このプロジェクトは、Apache 2.0ライセンスの下で公開されています。