base_model: ValiantLabs/Qwen3-4B-Esper3
datasets:
- sequelbox/Titanium2.1-DeepSeek-R1
- sequelbox/Tachibana2-DeepSeek-R1
- sequelbox/Raiden-DeepSeek-R1
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- esper
- esper-3
- valiant
- valiant-labs
- qwen
- qwen-3
- qwen-3-4b
- 4b
- reasoning
- code
- code-instruct
- python
- javascript
- dev-ops
- jenkins
- terraform
- scripting
- powershell
- azure
- aws
- gcp
- cloud
- problem-solving
- architect
- engineer
- developer
- creative
- analytical
- expert
- rationality
- conversational
- chat
- instruct
About
static quants of https://huggingface.co/ValiantLabs/Qwen3-4B-Esper3
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-4B-Esper3-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 | 1.8 | |
| GGUF | Q3_K_S | 2.0 | |
| GGUF | Q3_K_M | 2.2 | lower quality |
| GGUF | Q3_K_L | 2.3 | |
| GGUF | IQ4_XS | 2.4 | |
| GGUF | Q4_K_S | 2.5 | fast, recommended |
| GGUF | Q4_K_M | 2.6 | fast, recommended |
| GGUF | Q5_K_S | 2.9 | |
| GGUF | Q5_K_M | 3.0 | |
| GGUF | Q6_K | 3.4 | very good quality |
| GGUF | Q8_0 | 4.4 | fast, best quality |
| GGUF | f16 | 8.2 | 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.