richarderkhov/xenon1_-_zenith-7b-dpo-gguf overview

{
  "metadata": {},
  "card_data": {
    "frontmatter": {},
    "hero_image_url": "https://cdn-uploads.huggingface.co/production/uploads/60394599033b61166496163b/x50p_gQtQMb0fFVY8MGeq.png",
    "summary": "Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models. !image/png",
    "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\nZenith-7B-dpo - GGUF\n- Model creator: https://huggingface.co/Xenon1/\n- Original model: https://huggingface.co/Xenon1/Zenith-7B-dpo/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [Zenith-7B-dpo.Q2_K.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q2_K.gguf) | Q2_K | 2.53GB |\n| [Zenith-7B-dpo.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.IQ3_XS.gguf) | IQ3_XS | 2.81GB |\n| [Zenith-7B-dpo.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.IQ3_S.gguf) | IQ3_S | 2.96GB |\n| [Zenith-7B-dpo.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q3_K_S.gguf) | Q3_K_S | 2.95GB |\n| [Zenith-7B-dpo.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.IQ3_M.gguf) | IQ3_M | 3.06GB |\n| [Zenith-7B-dpo.Q3_K.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q3_K.gguf) | Q3_K | 3.28GB |\n| [Zenith-7B-dpo.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q3_K_M.gguf) | Q3_K_M | 3.28GB |\n| [Zenith-7B-dpo.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q3_K_L.gguf) | Q3_K_L | 3.56GB |\n| [Zenith-7B-dpo.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.IQ4_XS.gguf) | IQ4_XS | 3.67GB |\n| [Zenith-7B-dpo.Q4_0.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q4_0.gguf) | Q4_0 | 3.83GB |\n| [Zenith-7B-dpo.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.IQ4_NL.gguf) | IQ4_NL | 3.87GB |\n| [Zenith-7B-dpo.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q4_K_S.gguf) | Q4_K_S | 3.86GB |\n| [Zenith-7B-dpo.Q4_K.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q4_K.gguf) | Q4_K | 4.07GB |\n| [Zenith-7B-dpo.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q4_K_M.gguf) | Q4_K_M | 4.07GB |\n| [Zenith-7B-dpo.Q4_1.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q4_1.gguf) | Q4_1 | 4.24GB |\n| [Zenith-7B-dpo.Q5_0.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q5_0.gguf) | Q5_0 | 4.65GB |\n| [Zenith-7B-dpo.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q5_K_S.gguf) | Q5_K_S | 4.65GB |\n| [Zenith-7B-dpo.Q5_K.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q5_K.gguf) | Q5_K | 4.78GB |\n| [Zenith-7B-dpo.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q5_K_M.gguf) | Q5_K_M | 4.78GB |\n| [Zenith-7B-dpo.Q5_1.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q5_1.gguf) | Q5_1 | 5.07GB |\n| [Zenith-7B-dpo.Q6_K.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q6_K.gguf) | Q6_K | 5.53GB |\n| [Zenith-7B-dpo.Q8_0.gguf](https://huggingface.co/RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf/blob/main/Zenith-7B-dpo.Q8_0.gguf) | Q8_0 | 7.17GB |\n\n\n\n\nOriginal model description:\n---\nlanguage:\n- en\nlicense: apache-2.0\ntags:\n- mistral\n- Zenith-7B-dpo\npipeline_tag: text-generation\n---\n# Model Card for Zenith-7B-dpo\n\nMistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper [Self-Rewarding Language Models](https://arxiv.org/abs/2401.10020).\n\n\n![image/png](https://cdn-uploads.huggingface.co/production/uploads/60394599033b61166496163b/x50p_gQtQMb0fFVY8MGeq.png)\n\n## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n```\ntext = \"<s>[INST] What is your favourite condiment? [/INST]\"\n\"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> \"\n\"[INST] Do you have mayonnaise recipes? [/INST]\"\n```\n\nThis format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\ndevice = \"cuda\" # the device to load the model onto\n\nmodel = AutoModelForCausalLM.from_pretrained(\"Xenon1/Zenith-7B-dpo\")\ntokenizer = AutoTokenizer.from_pretrained(\"Xenon1/Zenith-7B-dpo\")\n\nmessages = [\n    {\"role\": \"user\", \"content\": \"What is your favourite condiment?\"},\n    {\"role\": \"assistant\", \"content\": \"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!\"},\n    {\"role\": \"user\", \"content\": \"Do you have mayonnaise recipes?\"}\n]\n\nencodeds = tokenizer.apply_chat_template(messages, return_tensors=\"pt\")\n\nmodel_inputs = encodeds.to(device)\nmodel.to(device)\n\ngenerated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)\ndecoded = tokenizer.batch_decode(generated_ids)\nprint(decoded[0])\n```\n\n## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer\n\n",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "arxiv:2401.10020",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 0,
  "downloads": 171,
  "gated": false,
  "private": false,
  "last_modified": "2024-09-06T05:57:08.000Z",
  "created_at": "2024-09-05T23:42:21.000Z",
  "pipeline_tag": "",
  "library_name": ""
}

{
  "_id": "66da41ddf5693ea15fa49323",
  "id": "RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf",
  "modelId": "RichardErkhov/Xenon1_-_Zenith-7B-dpo-gguf",
  "sha": "e72e3e2912701c81f964d3c8fde6a0568204d0c2",
  "createdAt": "2024-09-05T23:42:21.000Z",
  "lastModified": "2024-09-06T05:57:08.000Z",
  "author": "RichardErkhov",
  "downloads": 171,
  "likes": 0,
  "gated": false,
  "private": false,
  "pipeline_tag": "",
  "library_name": "",
  "siblings_count": 24
}