base_model: Blackfrost-AI/LING-3.0-FLASH-ABLITERATED
language:
- en
- zh
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags: - bailing-moe-v3
- ling
- derisked
- moe
- hybrid-attention
- mla
- residual-intervention
- conversational
- long-context
- reasoning
- gated
- research
- security
- cybersecurity
- red-teaming
- adversarial-testing
- evaluation
- not-for-all-audiences
About
static quants of https://huggingface.co/Blackfrost-AI/LING-3.0-FLASH-ABLITERATED
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/LING-3.0-FLASH-ABLITERATED-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 | 46.6 | |
| GGUF | Q3_K_S | 55.2 | |
| GGUF | Q3_K_M | 60.9 | lower quality |
| GGUF | Q3_K_L | 66.1 | |
| GGUF | IQ4_XS | 68.6 | |
| GGUF | Q4_K_S | 72.5 | fast, recommended |
| GGUF | Q4_K_M | 77.1 | fast, recommended |
| GGUF | Q5_K_S | 88.0 | |
| GGUF | Q5_K_M | 90.6 | |
| PART 1 PART 2 PART 3 | Q6_K | 104.9 | very good quality |
| PART 1 PART 2 PART 3 | Q8_0 | 135.7 | fast, best quality |
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.