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chargoddard_-_mixtralnt-4x7b-test-gguf
RichardErkhov/chargoddard_-_mixtralnt-4x7b-test-gguf
chargoddard_-_mixtralnt-4x7b-test-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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.gguf316 GB · 100%
At a glance
- Access
- Public
- Created
- Aug 28, 2024
- Updated
- Aug 28, 2024
- SHA
- 06baaf44
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
mixtralnt-4x7b-test - GGUF
- Model creator: https://huggingface.co/chargoddard/
- Original model: https://huggingface.co/chargoddard/mixtralnt-4x7b-test/
| Name | Quant method | Size |
|---|---|---|
| mixtralnt-4x7b-test.Q2_K.gguf | Q2_K | 8.24GB |
| mixtralnt-4x7b-test.IQ3_XS.gguf | IQ3_XS | 9.21GB |
| mixtralnt-4x7b-test.IQ3_S.gguf | IQ3_S | 9.73GB |
| mixtralnt-4x7b-test.Q3_K_S.gguf | Q3_K_S | 9.72GB |
| mixtralnt-4x7b-test.IQ3_M.gguf | IQ3_M | 9.92GB |
| mixtralnt-4x7b-test.Q3_K.gguf | Q3_K | 10.79GB |
| mixtralnt-4x7b-test.Q3_K_M.gguf | Q3_K_M | 10.79GB |
| mixtralnt-4x7b-test.Q3_K_L.gguf | Q3_K_L | 11.68GB |
| mixtralnt-4x7b-test.IQ4_XS.gguf | IQ4_XS | 12.15GB |
| mixtralnt-4x7b-test.Q4_0.gguf | Q4_0 | 12.69GB |
| mixtralnt-4x7b-test.IQ4_NL.gguf | IQ4_NL | 12.81GB |
| mixtralnt-4x7b-test.Q4_K_S.gguf | Q4_K_S | 12.8GB |
| mixtralnt-4x7b-test.Q4_K.gguf | Q4_K | 13.61GB |
| mixtralnt-4x7b-test.Q4_K_M.gguf | Q4_K_M | 13.61GB |
| mixtralnt-4x7b-test.Q4_1.gguf | Q4_1 | 14.09GB |
| mixtralnt-4x7b-test.Q5_0.gguf | Q5_0 | 15.48GB |
| mixtralnt-4x7b-test.Q5_K_S.gguf | Q5_K_S | 15.48GB |
| mixtralnt-4x7b-test.Q5_K.gguf | Q5_K | 15.96GB |
| mixtralnt-4x7b-test.Q5_K_M.gguf | Q5_K_M | 15.96GB |
| mixtralnt-4x7b-test.Q5_1.gguf | Q5_1 | 16.88GB |
| mixtralnt-4x7b-test.Q6_K.gguf | Q6_K | 18.46GB |
| mixtralnt-4x7b-test.Q8_0.gguf | Q8_0 | 23.9GB |
Original model description:
license: cc-by-nc-4.0
Mixtraln't 4x7B
Oh boy, a new model architecture in Transformers! Time to do profane things with it.
What if instead of training a MoE from scratch, we took some pre-trained Mistral models and shoved them in a little clown car?
Uses parts from the following models:
- Q-bert/MetaMath-Cybertron-Starling
- NeverSleep/Noromaid-7b-v0.1.1
- teknium/Mistral-Trismegistus-7B
- meta-math/MetaMath-Mistral-7B
- PocketDoc/Dans-AdventurousWinds-Mk2-7b
Works and generates coherent text. The big question here is if the hack I used to populate the MoE gates works well enough to take advantage of all of the experts. Let's find out!
Prompt format: maybe alpaca??? or chatml??? life is full of mysteries
- Created
- Aug 28, 2024
- Updated
- Aug 28, 2024