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Model: RichardErkhov/mjmanashti_-_gemma-2b-ForexAI-gguf
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Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
gemma-2b-ForexAI - GGUF
- Model creator: https://huggingface.co/mjmanashti/
- Original model: https://huggingface.co/mjmanashti/gemma-2b-ForexAI/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
Original model description:
---
license: other
tags:
- autotrain
- text-generation
widget:
- text: 'I love AutoTrain because '
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/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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