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Mithra-2B-GGUF is an open-weight model for robotics from Team Mradermacher, released under MIT License. Its published files total 14.2 GB.
static quants of https://huggingface.co/Bedirhn77/Mithra-2B For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time.
By Team Mradermacher, published under mit, revision 1548e1afd54d.
static quants of https://huggingface.co/Bedirhn77/Mithra-2B For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. 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. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And…
static quants of https://huggingface.co/Bedirhn77/Mithra-2B
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
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.
(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 | 0.8 | |
| GGUF | Q3_K_S | 0.9 | |
| GGUF | Q3_K_M | 0.9 | lower quality |
| GGUF | Q3_K_L | 1.0 | |
| GGUF | IQ4_XS | 1.0 | |
| GGUF | Q4_K_S | 1.0 | fast, recommended |
| GGUF | Q4_K_M | 1.1 | fast, recommended |
| GGUF | Q5_K_S | 1.2 | |
| GGUF | Q5_K_M | 1.2 | |
| GGUF | Q6_K | 1.4 | very good quality |
| GGUF | Q8_0 | 1.7 | fast, best quality |
| GGUF | f16 | 3.2 | 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
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
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.
14 files, 14.2 GB in total. The weights are 12 files totalling 14.2 GB in gguf.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| Mithra-2B.IQ4_XS.gguf | Weights | 902.2 MB | 544f02167353 |
| Mithra-2B.Q2_K.gguf | Weights | 676.3 MB | 2e03b5e674bb |
| Mithra-2B.Q3_K_L.gguf | Weights | 880.2 MB | 0be29f567417 |
| Mithra-2B.Q3_K_M.gguf | Weights | 824.2 MB | 7747e30a61c4 |
| Mithra-2B.Q3_K_S.gguf | Weights | 760.9 MB | 173e3f76d3a1 |
| Mithra-2B.Q4_K_M.gguf | Weights | 986.0 MB | efb4674b92fc |
| Mithra-2B.Q4_K_S.gguf | Weights | 940.3 MB | 3a0a61854dd6 |
| Mithra-2B.Q5_K_M.gguf | Weights | 1.1 GB | 2c0758e809ee |
| Mithra-2B.Q5_K_S.gguf | Weights | 1.1 GB | 8557c327f1e6 |
| Mithra-2B.Q6_K.gguf | Weights | 1.3 GB | a8368d07a997 |
| Mithra-2B.Q8_0.gguf | Weights | 1.6 GB | f5677795e099 |
| Mithra-2B.f16.gguf | Weights | 3.1 GB | 1d9e9bd174bd |
| README.md | Documentation | 3.5 KB | — |
| .gitattributes | Repository | 2.2 KB | — |
Released by Team Mradermacher through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 14.2 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Yes. Mithra-2B-GGUF is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
LIBERO 4in1（liberospatial / liberoobject / liberogoal / libero10）共 53.19 GB 的 Wan2.2-VAE 编码 latent 缓存： 训练 LIBERO policy（action head / VLA）时直接读取 latent 缓存，避免重复 VAE 编码。 - 窗口模式: windowed（--windowed） - 输出:.pt 文件，每 episode 一个 - dataset（推荐）: https://huggingface.co/datasets/MangoGoes/libero4in1wan2.2vaelatentdataset - model（本仓库）: https://huggingface.co/MangoGoes/libero4in1wan2.2vaelatentcosmosstyle
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weighted/imatrix quants of https://huggingface.co/DeepCybo/PhysBrain1.5-8B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/PhysBrain1.5-8B-GGUF This is a vision model - mmproj files (if any) will be in the static repository. 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. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter…
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