richarderkhov/mjmanashti_-_gemma-2b-forexai-gguf overview

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    "summary": "This model was trained using AutoTrain. For more information, please visit AutoTrain. # Usage ``python !pip install transformers !pip install accelerate from huggingface_hub import notebook_login notebook_login() from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained(\"mjmanashti/gemma-2b-ForexAI\") torch.set_default_dtype(torch.float16) model = AutoModelForCausalLM.from_pretrained(\"mjmanashti/gemma-2b-ForexAI\", device_map=\"auto\") chat = [ { \"role\": \"user\", \"content\": \"Based on the following input data: [Time: 2024-01-29 23:00:00, Open: 1.0834, High: 1.0837, Low: 1.08334, Close: 1.08338, Volume: 722] what trading signal (BUY, SELL, or HOLD) should be executed to maximize profit? If the signal is BUY, what would be the entry price and If the signal is SELL, what would be the exit price for profit maximization? \" }, ] prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors=\"pt\") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150) print(tokenizer.decode(outputs[0])) ``",
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    "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\ngemma-2b-ForexAI - GGUF\n- Model creator: https://huggingface.co/mjmanashti/\n- Original model: https://huggingface.co/mjmanashti/gemma-2b-ForexAI/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [gemma-2b-ForexAI.Q2_K.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q2_K.gguf) | Q2_K | 1.08GB |\n| [gemma-2b-ForexAI.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.IQ3_XS.gguf) | IQ3_XS | 1.16GB |\n| [gemma-2b-ForexAI.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.IQ3_S.gguf) | IQ3_S | 1.2GB |\n| [gemma-2b-ForexAI.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q3_K_S.gguf) | Q3_K_S | 1.2GB |\n| [gemma-2b-ForexAI.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.IQ3_M.gguf) | IQ3_M | 1.22GB |\n| [gemma-2b-ForexAI.Q3_K.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q3_K.gguf) | Q3_K | 1.29GB |\n| [gemma-2b-ForexAI.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q3_K_M.gguf) | Q3_K_M | 1.29GB |\n| [gemma-2b-ForexAI.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q3_K_L.gguf) | Q3_K_L | 1.36GB |\n| [gemma-2b-ForexAI.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.IQ4_XS.gguf) | IQ4_XS | 1.4GB |\n| [gemma-2b-ForexAI.Q4_0.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q4_0.gguf) | Q4_0 | 1.44GB |\n| [gemma-2b-ForexAI.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.IQ4_NL.gguf) | IQ4_NL | 1.45GB |\n| [gemma-2b-ForexAI.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q4_K_S.gguf) | Q4_K_S | 1.45GB |\n| [gemma-2b-ForexAI.Q4_K.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q4_K.gguf) | Q4_K | 1.52GB |\n| [gemma-2b-ForexAI.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q4_K_M.gguf) | Q4_K_M | 1.52GB |\n| [gemma-2b-ForexAI.Q4_1.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q4_1.gguf) | Q4_1 | 1.56GB |\n| [gemma-2b-ForexAI.Q5_0.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q5_0.gguf) | Q5_0 | 1.68GB |\n| [gemma-2b-ForexAI.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q5_K_S.gguf) | Q5_K_S | 1.68GB |\n| [gemma-2b-ForexAI.Q5_K.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q5_K.gguf) | Q5_K | 1.71GB |\n| [gemma-2b-ForexAI.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q5_K_M.gguf) | Q5_K_M | 1.71GB |\n| [gemma-2b-ForexAI.Q5_1.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q5_1.gguf) | Q5_1 | 1.79GB |\n| [gemma-2b-ForexAI.Q6_K.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q6_K.gguf) | Q6_K | 1.92GB |\n| [gemma-2b-ForexAI.Q8_0.gguf](https://huggingface.co/RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf/blob/main/gemma-2b-ForexAI.Q8_0.gguf) | Q8_0 | 2.49GB |\n\n\n\n\nOriginal model description:\n---\nlicense: other\ntags:\n- autotrain\n- text-generation\nwidget:\n- text: 'I love AutoTrain because '\n---\n\n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).\n\n# Usage\n\n```python\n!pip install transformers\n\n!pip install accelerate\n\n\nfrom huggingface_hub import notebook_login\nnotebook_login()\n\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\nimport torch\n\n\ntokenizer = AutoTokenizer.from_pretrained(\"mjmanashti/gemma-2b-ForexAI\")\ntorch.set_default_dtype(torch.float16)\n\nmodel = AutoModelForCausalLM.from_pretrained(\"mjmanashti/gemma-2b-ForexAI\", device_map=\"auto\")\n\nchat = [\n    { \"role\": \"user\", \"content\": \"Based on the following input data: [Time: 2024-01-29 23:00:00, Open: 1.0834, High: 1.0837, Low: 1.08334, Close: 1.08338, Volume: 722] what trading signal (BUY, SELL, or HOLD) should be executed to maximize profit? If the signal is BUY, what would be the entry price and If the signal is SELL, what would be the exit price for profit maximization? \" },\n]\nprompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)\ninputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors=\"pt\")\noutputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)\nprint(tokenizer.decode(outputs[0]))\n\n```\n\n",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 0,
  "downloads": 188,
  "gated": false,
  "private": false,
  "last_modified": "2024-07-16T04:54:57.000Z",
  "created_at": "2024-07-16T04:13:19.000Z",
  "pipeline_tag": "",
  "library_name": ""
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