richarderkhov/zhangshenao_-_selm-llama-3-8b-instruct-iter-1-gguf overview

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    "summary": "This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct using synthetic data based on on the HuggingFaceH4/ultrafeedback_binarized dataset.",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "Quantization made by Richard Erkhov.\n\n[Github](https://github.com/RichardErkhov)\n\n[Discord](https://discord.gg/pvy7H8DZMG)\n\n[Request more models](https://github.com/RichardErkhov/quant_request)\n\n\nSELM-Llama-3-8B-Instruct-iter-1 - GGUF\n- Model creator: https://huggingface.co/ZhangShenao/\n- Original model: https://huggingface.co/ZhangShenao/SELM-Llama-3-8B-Instruct-iter-1/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q2_K.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q2_K.gguf) | Q2_K | 2.96GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.IQ3_XS.gguf) | IQ3_XS | 3.28GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.IQ3_S.gguf) | IQ3_S | 3.43GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q3_K_S.gguf) | Q3_K_S | 3.41GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.IQ3_M.gguf) | IQ3_M | 3.52GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q3_K.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q3_K.gguf) | Q3_K | 3.74GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q3_K_M.gguf) | Q3_K_M | 3.74GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q3_K_L.gguf) | Q3_K_L | 4.03GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.IQ4_XS.gguf) | IQ4_XS | 4.18GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q4_0.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q4_0.gguf) | Q4_0 | 4.34GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.IQ4_NL.gguf) | IQ4_NL | 4.38GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q4_K_S.gguf) | Q4_K_S | 4.37GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q4_K.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q4_K.gguf) | Q4_K | 4.58GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q4_K_M.gguf) | Q4_K_M | 4.58GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q4_1.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q4_1.gguf) | Q4_1 | 4.78GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q5_0.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q5_0.gguf) | Q5_0 | 5.21GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q5_K_S.gguf) | Q5_K_S | 5.21GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q5_K.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q5_K.gguf) | Q5_K | 5.34GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q5_K_M.gguf) | Q5_K_M | 5.34GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q5_1.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q5_1.gguf) | Q5_1 | 5.65GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q6_K.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q6_K.gguf) | Q6_K | 6.14GB |\n| [SELM-Llama-3-8B-Instruct-iter-1.Q8_0.gguf](https://huggingface.co/RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf/blob/main/SELM-Llama-3-8B-Instruct-iter-1.Q8_0.gguf) | Q8_0 | 7.95GB |\n\n\n\n\nOriginal model description:\n---\nlicense: mit\nbase_model: meta-llama/Meta-Llama-3-8B-Instruct\ntags:\n- alignment-handbook\n- dpo\n- trl\n- selm\ndatasets:\n- HuggingFaceH4/ultrafeedback_binarized\nmodel-index:\n- name: SELM-Llama-3-8B-Instruct-iter-1\n  results: []\n---\n\n\n\n<!-- This model card has been generated automatically according to the information the Trainer had access to. You\nshould probably proofread and complete it, then remove this comment. -->\n\n\n\n[Self-Exploring Language Models: Active Preference Elicitation for Online Alignment](https://arxiv.org/abs/2405.19332).\n\n\n\n# SELM-Llama-3-8B-Instruct-iter-1\n\n\n\nThis model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) using synthetic data based on on the HuggingFaceH4/ultrafeedback_binarized dataset.\n\n\n\n## Model description\n\n\n\n- Model type: A 8B parameter Llama3-instruct-based Self-Exploring Language Models (SELM).\n- License: MIT\n\n\n\n## Results\n\n\n\n|                                        | AlpacaEval 2.0 (LC WR) | MT-Bench (Average) |\n|----------------------------------------|------------------------|--------------------|\n| [SELM-Llama-3-8B-Instruct-iter-3](https://huggingface.co/ZhangShenao/SELM-Llama-3-8B-Instruct-iter-3)  |    &emsp; &emsp; &emsp;&emsp;           33.47          |   &emsp; &emsp; &emsp;         8.29       |\n| [SELM-Llama-3-8B-Instruct-iter-2](https://huggingface.co/ZhangShenao/SELM-Llama-3-8B-Instruct-iter-2) |    &emsp; &emsp; &emsp;&emsp;         35.65         |  &emsp; &emsp; &emsp;         8.09       |\n| [SELM-Llama-3-8B-Instruct-iter-1](https://huggingface.co/ZhangShenao/SELM-Llama-3-8B-Instruct-iter-1) |    &emsp; &emsp; &emsp;&emsp;         32.02         |  &emsp; &emsp; &emsp;         7.92       |\n| [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)  |    &emsp; &emsp; &emsp;&emsp;         24.31         |  &emsp; &emsp; &emsp;         7.93       |\n\n\n### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- alpha: 0.0001\n- beta: 0.01\n- train_batch_size: 4\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- num_epochs: 1\n\n### Framework versions\n\n- Transformers 4.40.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.19.1\n\n\n",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "arxiv:2405.19332",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 0,
  "downloads": 232,
  "gated": false,
  "private": false,
  "last_modified": "2024-10-10T10:54:21.000Z",
  "created_at": "2024-10-10T07:32:16.000Z",
  "pipeline_tag": "",
  "library_name": ""
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