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---
language:
- en
library_name: transformers
pipeline_tag: text-generation
quantized_by: mradermacher
tags:
- llama
- llama 2
---
## About
static quantize of https://huggingface.co/Doctor-Shotgun/lzlv-limarpv3-l2-70b
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/lzlv-limarpv3-l2-70b-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) 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](https://huggingface.co/mradermacher/lzlv-limarpv3-l2-70b-GGUF/resolve/main/lzlv-limarpv3-l2-70b.Q2_K.gguf) | Q2_K | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/lzlv-limarpv3-l2-70b-GGUF/resolve/main/lzlv-limarpv3-l2-70b.Q3_K_XS.gguf) | Q3_K_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/lzlv-limarpv3-l2-70b-GGUF/resolve/main/lzlv-limarpv3-l2-70b.Q3_K_S.gguf) | Q3_K_S | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/lzlv-limarpv3-l2-70b-GGUF/resolve/main/lzlv-limarpv3-l2-70b.Q3_K_M.gguf) | Q3_K_M | 33.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/lzlv-limarpv3-l2-70b-GGUF/resolve/main/lzlv-limarpv3-l2-70b.Q3_K_L.gguf) | Q3_K_L | 36.2 | |
| [GGUF](https://huggingface.co/mradermacher/lzlv-limarpv3-l2-70b-GGUF/resolve/main/lzlv-limarpv3-l2-70b.Q4_K_S.gguf) | Q4_K_S | 39.3 | fast, medium quality |
| [GGUF](https://huggingface.co/mradermacher/lzlv-limarpv3-l2-70b-GGUF/resolve/main/lzlv-limarpv3-l2-70b.Q4_K_M.gguf) | Q4_K_M | 41.5 | fast, medium quality |
| [GGUF](https://huggingface.co/mradermacher/lzlv-limarpv3-l2-70b-GGUF/resolve/main/lzlv-limarpv3-l2-70b.Q5_K_S.gguf) | Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/lzlv-limarpv3-l2-70b-GGUF/resolve/main/lzlv-limarpv3-l2-70b.Q5_K_M.gguf) | Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/lzlv-limarpv3-l2-70b-GGUF/resolve/main/lzlv-limarpv3-l2-70b.Q6_K.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/lzlv-limarpv3-l2-70b-GGUF/resolve/main/lzlv-limarpv3-l2-70b.Q6_K.gguf.split-ab) | Q6_K | 56.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/lzlv-limarpv3-l2-70b-GGUF/resolve/main/lzlv-limarpv3-l2-70b.Q8_0.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/lzlv-limarpv3-l2-70b-GGUF/resolve/main/lzlv-limarpv3-l2-70b.Q8_0.gguf.split-ab) | Q8_0 | 73.4 | 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
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