Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

[Discord](https://discord.gg/pvy7H8DZMG)

[Request more models](https://github.com/RichardErkhov/quant_request)


autotrain-mixtral7x8b-math - GGUF
- Model creator: https://huggingface.co/abhishek/
- Original model: https://huggingface.co/abhishek/autotrain-mixtral7x8b-math/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [autotrain-mixtral7x8b-math.Q2_K.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q2_K.gguf) | Q2_K | 16.12GB |
| [autotrain-mixtral7x8b-math.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.IQ3_XS.gguf) | IQ3_XS | 18.02GB |
| [autotrain-mixtral7x8b-math.IQ3_S.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.IQ3_S.gguf) | IQ3_S | 19.03GB |
| [autotrain-mixtral7x8b-math.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q3_K_S.gguf) | Q3_K_S | 19.03GB |
| [autotrain-mixtral7x8b-math.IQ3_M.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.IQ3_M.gguf) | IQ3_M | 19.96GB |
| [autotrain-mixtral7x8b-math.Q3_K.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q3_K.gguf) | Q3_K | 21.0GB |
| [autotrain-mixtral7x8b-math.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q3_K_M.gguf) | Q3_K_M | 21.0GB |
| [autotrain-mixtral7x8b-math.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q3_K_L.gguf) | Q3_K_L | 22.51GB |
| [autotrain-mixtral7x8b-math.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.IQ4_XS.gguf) | IQ4_XS | 23.63GB |
| [autotrain-mixtral7x8b-math.Q4_0.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q4_0.gguf) | Q4_0 | 24.63GB |
| [autotrain-mixtral7x8b-math.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.IQ4_NL.gguf) | IQ4_NL | 24.91GB |
| [autotrain-mixtral7x8b-math.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q4_K_S.gguf) | Q4_K_S | 24.91GB |
| [autotrain-mixtral7x8b-math.Q4_K.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q4_K.gguf) | Q4_K | 26.49GB |
| [autotrain-mixtral7x8b-math.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q4_K_M.gguf) | Q4_K_M | 26.49GB |
| [autotrain-mixtral7x8b-math.Q4_1.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q4_1.gguf) | Q4_1 | 27.32GB |
| [autotrain-mixtral7x8b-math.Q5_0.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q5_0.gguf) | Q5_0 | 30.02GB |
| [autotrain-mixtral7x8b-math.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q5_K_S.gguf) | Q5_K_S | 30.02GB |
| [autotrain-mixtral7x8b-math.Q5_K.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q5_K.gguf) | Q5_K | 30.95GB |
| [autotrain-mixtral7x8b-math.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q5_K_M.gguf) | Q5_K_M | 30.95GB |
| [autotrain-mixtral7x8b-math.Q5_1.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q5_1.gguf) | Q5_1 | 32.71GB |
| [autotrain-mixtral7x8b-math.Q6_K.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/blob/main/autotrain-mixtral7x8b-math.Q6_K.gguf) | Q6_K | 35.74GB |
| [autotrain-mixtral7x8b-math.Q8_0.gguf](https://huggingface.co/RichardErkhov/abhishek_-_autotrain-mixtral7x8b-math-gguf/tree/main/) | Q8_0 | 46.22GB |




Original model description:
---
tags:
- autotrain
- text-generation-inference
- text-generation
library_name: transformers
widget:
  - messages:
      - role: user
        content: What is your favorite condiment?
license: other
---

# Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).

# Usage

```python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "PATH_TO_THIS_REPO"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()

# Prompt content: "hi"
messages = [
    {"role": "user", "content": "hi"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

# Model response: "Hello! How can I assist you today?"
print(response)
```