richarderkhov/umarigan_-_llama-3.1-openhermes-tr-gguf overview

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    "frontmatter": {},
    "hero_image_url": "https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png",
    "summary": "This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.",
    "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\nllama-3.1-openhermes-tr - GGUF\n- Model creator: https://huggingface.co/umarigan/\n- Original model: https://huggingface.co/umarigan/llama-3.1-openhermes-tr/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [llama-3.1-openhermes-tr.Q2_K.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q2_K.gguf) | Q2_K | 2.96GB |\n| [llama-3.1-openhermes-tr.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.IQ3_XS.gguf) | IQ3_XS | 3.28GB |\n| [llama-3.1-openhermes-tr.IQ3_S.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.IQ3_S.gguf) | IQ3_S | 3.43GB |\n| [llama-3.1-openhermes-tr.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q3_K_S.gguf) | Q3_K_S | 3.41GB |\n| [llama-3.1-openhermes-tr.IQ3_M.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.IQ3_M.gguf) | IQ3_M | 3.52GB |\n| [llama-3.1-openhermes-tr.Q3_K.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q3_K.gguf) | Q3_K | 3.74GB |\n| [llama-3.1-openhermes-tr.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q3_K_M.gguf) | Q3_K_M | 3.74GB |\n| [llama-3.1-openhermes-tr.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q3_K_L.gguf) | Q3_K_L | 4.03GB |\n| [llama-3.1-openhermes-tr.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.IQ4_XS.gguf) | IQ4_XS | 4.18GB |\n| [llama-3.1-openhermes-tr.Q4_0.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q4_0.gguf) | Q4_0 | 4.34GB |\n| [llama-3.1-openhermes-tr.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.IQ4_NL.gguf) | IQ4_NL | 4.38GB |\n| [llama-3.1-openhermes-tr.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q4_K_S.gguf) | Q4_K_S | 4.37GB |\n| [llama-3.1-openhermes-tr.Q4_K.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q4_K.gguf) | Q4_K | 4.58GB |\n| [llama-3.1-openhermes-tr.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q4_K_M.gguf) | Q4_K_M | 4.58GB |\n| [llama-3.1-openhermes-tr.Q4_1.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q4_1.gguf) | Q4_1 | 4.78GB |\n| [llama-3.1-openhermes-tr.Q5_0.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q5_0.gguf) | Q5_0 | 5.21GB |\n| [llama-3.1-openhermes-tr.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q5_K_S.gguf) | Q5_K_S | 5.21GB |\n| [llama-3.1-openhermes-tr.Q5_K.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q5_K.gguf) | Q5_K | 5.34GB |\n| [llama-3.1-openhermes-tr.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q5_K_M.gguf) | Q5_K_M | 5.34GB |\n| [llama-3.1-openhermes-tr.Q5_1.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q5_1.gguf) | Q5_1 | 5.65GB |\n| [llama-3.1-openhermes-tr.Q6_K.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q6_K.gguf) | Q6_K | 6.14GB |\n| [llama-3.1-openhermes-tr.Q8_0.gguf](https://huggingface.co/RichardErkhov/umarigan_-_llama-3.1-openhermes-tr-gguf/blob/main/llama-3.1-openhermes-tr.Q8_0.gguf) | Q8_0 | 7.95GB |\n\n\n\n\nOriginal model description:\n---\nbase_model: unsloth/llama-3-8b-bnb-4bit\nlanguage:\n- en\n- tr\nlicense: apache-2.0\ntags:\n- text-generation-inference\n- transformers\n- unsloth\n- llama\n- trl\n- sft\npipeline_tag: question-answering\n---\n\n# Uploaded  model\n\n- **Developed by:** umarigan\n- **License:** apache-2.0\n- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.\n\n[<img src=\"https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png\" width=\"200\"/>](https://github.com/unslothai/unsloth)\n\n## Usage Examples\n\n```python\n\n# Load model directly\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\ntokenizer = AutoTokenizer.from_pretrained(\"umarigan/llama-3-openhermes-tr\")\nmodel = AutoModelForCausalLM.from_pretrained(\"umarigan/llama-3-openhermes-tr\")\nalpaca_prompt = \"\"\"\nGörev:\n{}\n\nGirdi:\n{}\n\nCevap:\n{}\"\"\"\n\ninputs = tokenizer(\n[\n    alpaca_prompt.format(\n        \"bir haftada 3 kilo verebileceğim 5 öneri sunabilir misin?\", # Görev\n        \"\", # Girdi\n        \"\", # Cevap - boş bırakın!\n    )\n], return_tensors = \"pt\")\noutputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\ntokenizer.batch_decode(outputs)\n\nOutput:\n\n'<|begin_of_text|>\nGörev: bir haftada 3 kilo verebileceğim 5 öneri sunabilir misin?\nGirdi:\nCevap:\n1. Yeterli miktarda su içmek: Su, vücuttaki toksinlerin atılmasını ve sindirim sisteminin çalışmasını destekler. Su içmek, vücuttaki su dengesini korumaya yardımcı olur ve kilo kaybına yardımcı olabilir. Günlük su tüketiminizi 2-3 litre arasında tutun.\n2. Düzenli egzersiz yapın: Egzersiz, metabolizmayı hızlandırmaya yardımcı olur ve vücuttaki yağ yakımını teşvik eder. Haftada 3-5 gün egzersiz yapın ve her gün 30-60 dakika egzersiz yapın. Egzersizler, aerobik egzersizler (yürüyüş, yüzme, bisiklet sürmek) ve ana kas egzersizleri (kollar, bacaklar, kalça) gibi çeşitli egzersiz türlerini içerebilir.\n3. Azalmış kalori alımına odaklanın: Kilo verme hedefinize ulaşmak için kalori alımınızı azaltın. Bu, beslenme planınızda azalmış kalori içeren yiyecekleri seçerek ve aşırı kalori alımını önlemek için yemeklerinizi ölçerek veya ağırlaştırmaya çalışarak yapılabilir.\n4. Yeterli uyku al: Uyku, metabolizmayı düzenleyen ve kilo kaybına yardımcı olan hormonların salınmasını etkileyen bir faktördür. Günlük 7-9 saat uyku almayı hedefleyin.\n5. Stres yönetimi: Stres, hormonların salınmasını etkileyerek kilo alımına katkıda bulunabilir. Stresi yönetmek için egzersiz, meditasyon veya derin nefes egzersizleri gibi çeşitli yöntemleri kullanın. Bu, stresi azaltmaya ve metabolizmayı düzenleme yeteneğini geliştirmeye yardımcı olabilir.\n<|end_of_text|>'\n```\n\n",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "endpoints_compatible",
    "region:us"
  ],
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  "last_modified": "2024-10-09T04:19:03.000Z",
  "created_at": "2024-10-09T00:49:25.000Z",
  "pipeline_tag": "",
  "library_name": ""
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  "author": "RichardErkhov",
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