mradermacher/ringg-router-e2b-GGUF overview

---

base_model: RinggAI/ringg-router-e2b

datasets:

• mteb/amazon_massive_intent
• mteb/banking77
• clinc/clinc_oos
• bitext/Bitext-customer-support-llm-chatbot-training-dataset
• Process-Venue/IntentClassification_Dataset_for_AI_Assistant_Prompt_Routing_Hindi
• WillHeld/hinglish_top
• ZefanCai/Open-Jev-v1.1
• Praveenrajus/jev-bench
• SargeDev/jev-distill-corpus-v3
• tasksource/tasksource-jev-typed-decisions
• n4ze3m/typed-decisions-synth
• Divyanshu/indicxnli
• sarvamai/boolq-indic
• google/boolq
• nyu-mll/multi_nli
• OanaMariaCamburu/e-SNLI
• tasksource/ecqa
• ai4bharat/naamapadam
• cfilt/HiNER-original
• MultiCoNER/multiconer_v2
• ai4bharat/IndicQA
• AmazonScience/massive-agents
• nvidia/BFCL-Hi
• Team-ACE/ToolACE
• NousResearch/hermes-function-calling-v1
• MadeAgents/xlam-irrelevance-7.5k
• GEM/schema_guided_dialog
• DeepPavlov/XRISAWOZ

language:

• en
• hi
• bn
• te
• ta
• kn
• ml
• mr
• gu
• pa
• or
• ur

library_name: transformers

license: apache-2.0

mradermacher:

readme_rev: 1

quantized_by: mradermacher

tags:

• routing
• intent-classification
• function-calling
• information-extraction
• nli
• voice-agents
• indic
• code-mixed
• gemma4

---

About

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static quants of https://huggingface.co/RinggAI/ringg-router-e2b

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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.

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 | mmproj-Q8_0 | 0.7 | multi-modal supplement |

| GGUF | mmproj-f16 | 1.1 | multi-modal supplement |

| GGUF | Q2_K | 3.1 | |

| GGUF | Q3_K_S | 3.2 | |

| GGUF | Q3_K_M | 3.3 | lower quality |

| GGUF | Q3_K_L | 3.4 | |

| GGUF | IQ4_XS | 3.4 | |

| GGUF | Q4_K_S | 3.5 | fast, recommended |

| GGUF | Q4_K_M | 3.5 | fast, recommended |

| GGUF | Q5_K_S | 3.7 | |

| GGUF | Q5_K_M | 3.7 | |

| GGUF | Q6_K | 3.9 | very good quality |

| GGUF | Q8_0 | 5.0 | fast, best quality |

| GGUF | f16 | 9.4 | 16 bpw, overkill |

Here is a handy graph by ikawrakow comparing some lower-quality quant

types (lower is better):

!image.png

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.

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