gguf endpoints_compatible region:us
---
{}
---
Quantization made by Richard Erkhov.

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

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

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


Mixsmol-4x400M-v0.1-epoch2 - GGUF
- Model creator: https://huggingface.co/vilm/
- Original model: https://huggingface.co/vilm/Mixsmol-4x400M-v0.1-epoch2/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Mixsmol-4x400M-v0.1-epoch2.Q2_K.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q2_K.gguf) | Q2_K | 0.62GB |
| [Mixsmol-4x400M-v0.1-epoch2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.IQ3_XS.gguf) | IQ3_XS | 0.7GB |
| [Mixsmol-4x400M-v0.1-epoch2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.IQ3_S.gguf) | IQ3_S | 0.73GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q3_K_S.gguf) | Q3_K_S | 0.73GB |
| [Mixsmol-4x400M-v0.1-epoch2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.IQ3_M.gguf) | IQ3_M | 0.74GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q3_K.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q3_K.gguf) | Q3_K | 0.8GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q3_K_M.gguf) | Q3_K_M | 0.8GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q3_K_L.gguf) | Q3_K_L | 0.87GB |
| [Mixsmol-4x400M-v0.1-epoch2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.IQ4_XS.gguf) | IQ4_XS | 0.9GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q4_0.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q4_0.gguf) | Q4_0 | 0.94GB |
| [Mixsmol-4x400M-v0.1-epoch2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.IQ4_NL.gguf) | IQ4_NL | 0.95GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q4_K_S.gguf) | Q4_K_S | 0.95GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q4_K.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q4_K.gguf) | Q4_K | 1.01GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q4_K_M.gguf) | Q4_K_M | 1.01GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q4_1.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q4_1.gguf) | Q4_1 | 1.04GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q5_0.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q5_0.gguf) | Q5_0 | 1.14GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q5_K_S.gguf) | Q5_K_S | 1.14GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q5_K.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q5_K.gguf) | Q5_K | 1.18GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q5_K_M.gguf) | Q5_K_M | 1.18GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q5_1.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q5_1.gguf) | Q5_1 | 1.24GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q6_K.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q6_K.gguf) | Q6_K | 1.36GB |
| [Mixsmol-4x400M-v0.1-epoch2.Q8_0.gguf](https://huggingface.co/RichardErkhov/vilm_-_Mixsmol-4x400M-v0.1-epoch2-gguf/blob/main/Mixsmol-4x400M-v0.1-epoch2.Q8_0.gguf) | Q8_0 | 1.76GB |




Original model description:
---
license: apache-2.0
widget:
- text: My name is El Microondas the Wise, and
  example_title: El Microondas
- text: Kennesaw State University is a public
  example_title: Kennesaw State University
- text: Bungie Studios is an American video game developer. They are most famous for
    developing the award winning Halo series of video games. They also made Destiny.
    The studio was founded
  example_title: Bungie
- text: The Mona Lisa is a world-renowned painting created by
  example_title: Mona Lisa
- text: The Harry Potter series, written by J.K. Rowling, begins with the book titled
  example_title: Harry Potter Series
- text: 'Question: I have cities, but no houses. I have mountains, but no trees. I
    have water, but no fish. What am I?

    Answer:'
  example_title: Riddle
- text: The process of photosynthesis involves the conversion of
  example_title: Photosynthesis
- text: Jane went to the store to buy some groceries. She picked up apples, oranges,
    and a loaf of bread. When she got home, she realized she forgot
  example_title: Story Continuation
- text: 'Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph,
    and another train leaves Station B at 10:00 AM and travels at 80 mph, when will
    they meet if the distance between the stations is 300 miles?

    To determine'
  example_title: Math Problem
- text: In the context of computer programming, an algorithm is
  example_title: Algorithm Definition
---
# Mixsmol-4x400M-v0.1 by Ontocord
This is the first checkpoint (Epoch 1) of Mixsmol-4x400M-v0.1
Note that this is an experimental in data mixing. Therefore, we only trained the model on 50B tokens (95% English and 5% Vietnamese) to test the following:
- Reasoining capabilities through high-quality synthetic textbooks data pretraining 
- Crosslingual understanding through machine translation and multilingual + multiple tasks pretraining

After verifying our hypothesis with this run, we will schedule a second run on bigger data and compute for it to achieve its maximum capability.

## Data
- Synthetic Textbooks: 8M samples
- RefinedWeb: 1M samples
- RedPajama-v2: 500K samples
- MathPile: Everything
- ThePile: MiniPile Subset
- GoodWiki
- The Stack Smol XL
- The Vault: train_small split
- Instruction Pretraining: 250k samples

