Transformers
GGUF
English
shining-valiant
shining-valiant-2
valiant
valiant-labs
llama
llama-3.2
llama-3.2-instruct
llama-3.2-instruct-3b
llama-3
llama-3-instruct
llama-3-instruct-3b
3b
science
physics
biology
chemistry
compsci
computer-science
engineering
technical
conversational
chat
instruct
Inference Endpoints
File size: 4,092 Bytes
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---
base_model: ValiantLabs/Llama3.2-3B-ShiningValiant2
datasets:
- sequelbox/Celestia
- sequelbox/Spurline
- sequelbox/Supernova
language:
- en
library_name: transformers
license: llama3.2
model_type: llama
quantized_by: mradermacher
tags:
- shining-valiant
- shining-valiant-2
- valiant
- valiant-labs
- llama
- llama-3.2
- llama-3.2-instruct
- llama-3.2-instruct-3b
- llama-3
- llama-3-instruct
- llama-3-instruct-3b
- 3b
- science
- physics
- biology
- chemistry
- compsci
- computer-science
- engineering
- technical
- conversational
- chat
- instruct
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/ValiantLabs/Llama3.2-3B-ShiningValiant2
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama3.2-3B-ShiningValiant2-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/Llama3.2-3B-ShiningValiant2-GGUF/resolve/main/Llama3.2-3B-ShiningValiant2.Q2_K.gguf) | Q2_K | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3.2-3B-ShiningValiant2-GGUF/resolve/main/Llama3.2-3B-ShiningValiant2.Q3_K_S.gguf) | Q3_K_S | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3.2-3B-ShiningValiant2-GGUF/resolve/main/Llama3.2-3B-ShiningValiant2.Q3_K_M.gguf) | Q3_K_M | 1.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3.2-3B-ShiningValiant2-GGUF/resolve/main/Llama3.2-3B-ShiningValiant2.Q3_K_L.gguf) | Q3_K_L | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3.2-3B-ShiningValiant2-GGUF/resolve/main/Llama3.2-3B-ShiningValiant2.IQ4_XS.gguf) | IQ4_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3.2-3B-ShiningValiant2-GGUF/resolve/main/Llama3.2-3B-ShiningValiant2.Q4_0_4_4.gguf) | Q4_0_4_4 | 2.0 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3.2-3B-ShiningValiant2-GGUF/resolve/main/Llama3.2-3B-ShiningValiant2.Q4_K_S.gguf) | Q4_K_S | 2.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3.2-3B-ShiningValiant2-GGUF/resolve/main/Llama3.2-3B-ShiningValiant2.Q4_K_M.gguf) | Q4_K_M | 2.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3.2-3B-ShiningValiant2-GGUF/resolve/main/Llama3.2-3B-ShiningValiant2.Q5_K_S.gguf) | Q5_K_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3.2-3B-ShiningValiant2-GGUF/resolve/main/Llama3.2-3B-ShiningValiant2.Q5_K_M.gguf) | Q5_K_M | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3.2-3B-ShiningValiant2-GGUF/resolve/main/Llama3.2-3B-ShiningValiant2.Q6_K.gguf) | Q6_K | 2.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3.2-3B-ShiningValiant2-GGUF/resolve/main/Llama3.2-3B-ShiningValiant2.Q8_0.gguf) | Q8_0 | 3.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3.2-3B-ShiningValiant2-GGUF/resolve/main/Llama3.2-3B-ShiningValiant2.f16.gguf) | f16 | 6.5 | 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](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
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