What are the hardware requirements for Barely Regal 10.7B GGUF?

Barely Regal 10.7B GGUF requires approximately 6.5 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Barely Regal 10.7B GGUF and how large is it?

Barely Regal 10.7B GGUF is developed by sydonayrex, a model with 10.7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Barely Regal 10.7B GGUF?

Barely Regal 10.7B GGUF is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Barely Regal 10.7B GGUF by sydonayrex — VRAM 6.5GB, 32K context

Name: Barely Regal 10.7B GGUF
Author: sydonayrex

Barely Regal 10.7B GGUF is an open-source language model by sydonayrex. Features: 10.7b LLM, VRAM: 6.5GB, Context: 32K, License: apache-2.0, Quantized, LLM Explorer Score: 0.13.

Autotrain compatible Base model:quantized:sydonayre... Base model:sydonayrex/ai-m3-10... Conversational En Endpoints compatible Gguf Mistral Quantized Region:us Unsloth

Model Card on HF 🤗: https://huggingface.co/sydonayrex/Barely-Regal-10.7B-GGUF

Barely Regal 10.7B GGUF Parameters and Internals

Model Type

text generation

Additional Notes

If issues occur, enable flash attn for corrections.

Training Details

Methodology:

Layer merging and fine-tuning with SFT using Unsloth

LLM Name	Barely Regal 10.7B GGUF
Repository 🤗	https://huggingface.co/sydonayrex/Barely-Regal-10.7B-GGUF
Base Model(s)	sydonayrex/AI-M3-10.7Bv2 sydonayrex/AI-M3-10.7Bv2
Model Size	10.7b
Required VRAM	6.5 GB
Updated	2024-12-13
Maintainer	sydonayrex
Model Type	mistral
Model Files	6.5 GB 11.4 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.42.0.dev0
Vocabulary Size	32768
Torch Data Type	bfloat16

Rank the Barely Regal 10.7B GGUF capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Barely Regal 10.7B GGUF

Best Alternatives	Context / RAM	Downloads	Likes
BetaMonarch 10.7B	32K / 21.5 GB	22	2
...l Maid 32K V0.2 A EXL2 Bpw 6.0	32K / 8.2 GB	7	1
Barely Regal 10.7B	32K / 21.5 GB	19	0
Quintellect 10.7B	32K / 21.4 GB	22	3
Chikuma 10.7B	32K / 21.4 GB	131	5
PiVoT 10.7B Mistral V0.2	32K / 21.4 GB	76	5
Longcat 10.7B	32K / 21.5 GB	8	3
Multi Verse Model 10.7B	32K / 21.5 GB	2	1
PrimaSumika 10.7B 128K	32K / 21.5 GB	3	1
... Loyal Mistral Maid 32K V0.2 A	32K / 21.5 GB	3	1