base_model: mosaicml/mpt-7b-8k-chat
datasets:
- camel-ai/code
- ehartford/wizard_vicuna_70k_unfiltered
- anon8231489123/ShareGPT_Vicuna_unfiltered
- timdettmers/openassistant-guanaco
- camel-ai/math
- camel-ai/biology
- camel-ai/chemistry
- camel-ai/ai_society
- jondurbin/airoboros-gpt4-1.2
- LongConversations
- camel-ai/physics
language:
- en
library_name: transformers
license: cc-by-nc-sa-4.0
quantized_by: mradermacher
tags:
- Composer
- MosaicML
- llm-foundry
About
static quants of https://huggingface.co/mosaicml/mpt-7b-8k-chat
weighted/imatrix quants are available at https://huggingface.co/mradermacher/mpt-7b-8k-chat-i1-GGUF
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| GGUF | Q2_K | 2.7 | |
| GGUF | IQ3_XS | 3.0 | |
| GGUF | IQ3_S | 3.0 | beats Q3_K* |
| GGUF | Q3_K_S | 3.0 | |
| GGUF | IQ3_M | 3.4 | |
| GGUF | Q3_K_M | 3.6 | lower quality |
| GGUF | IQ4_XS | 3.7 | |
| GGUF | Q4_K_S | 3.9 | fast, recommended |
| GGUF | Q3_K_L | 3.9 | |
| GGUF | Q4_K_M | 4.4 | fast, recommended |
| GGUF | Q5_K_S | 4.7 | |
| GGUF | Q5_K_M | 5.1 | |
| GGUF | Q6_K | 5.6 | very good quality |
| GGUF | Q8_0 | 7.2 | fast, best quality |
| GGUF | f16 | 13.4 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
Thanks
I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.