What are the hardware requirements for SOLAR 10.7B Instruct V1.0 6.0bpw H6 EXL2 2?

SOLAR 10.7B Instruct V1.0 6.0bpw H6 EXL2 2 requires approximately 8.2 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed SOLAR 10.7B Instruct V1.0 6.0bpw H6 EXL2 2 and how large is it?

SOLAR 10.7B Instruct V1.0 6.0bpw H6 EXL2 2 is developed by LoneStriker, 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 SOLAR 10.7B Instruct V1.0 6.0bpw H6 EXL2 2?

SOLAR 10.7B Instruct V1.0 6.0bpw H6 EXL2 2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

SOLAR 10.7B Instruct V1.0 6.0bpw H6 EXL2 2 by LoneStriker — VRAM 8.2GB, 4K context

Name: SOLAR 10.7B Instruct V1.0 6.0bpw H6 EXL2 2
Author: LoneStriker

SOLAR 10.7B Instruct V1.0 6.0bpw H6 EXL2 2 is an open-source language model by LoneStriker. Features: 10.7b LLM, VRAM: 8.2GB, Context: 4K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.1.

Conversational Endpoints compatible Exl2 Instruct Llama Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/LoneStriker/SOLAR-10.7B-Instruct-v1.0-6.0bpw-h6-exl2-2

SOLAR 10.7B Instruct V1.0 6.0bpw H6 EXL2 2 Parameters and Internals

Model Type

language model, single-turn conversation

Use Cases

Areas:

single-turn conversation

Limitations:

less suitable for multi-turn conversations such as chat

Additional Notes

Detailed benchmark information to be updated soon.

Training Details

Methodology:

Depth Up-Scaling technique; incorporation of Mistral 7B weights

Context Length:

4096

Model Architecture:

Built on the Llama2 architecture with Upstage Depth Up-Scaling

Input Output

Input Format:

Single-turn conversation input

Accepted Modalities:

text

Output Format:

Text response

LLM Name	SOLAR 10.7B Instruct V1.0 6.0bpw H6 EXL2 2
Repository 🤗	https://huggingface.co/LoneStriker/SOLAR-10.7B-Instruct-v1.0-6.0bpw-h6-exl2-2
Model Size	10.7b
Required VRAM	8.2 GB
Updated	2026-08-02
Maintainer	LoneStriker
Model Type	llama
Instruction-Based	Yes
Model Files	8.2 GB
Quantization Type	exl2
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Transformers Version	4.35.0
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	float16

Rank the SOLAR 10.7B Instruct V1.0 6.0bpw H6 EXL2 2 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 SOLAR 10.7B Instruct V1.0 6.0bpw H6 EXL2 2

Best Alternatives	Context / RAM	Downloads	Likes
... 10.7B Instruct V1.0 128K EXL2	128K / 5.6 GB	5	1
Eclectus 1.1 Dedup	4K / 21.4 GB	5	0
Eclectus1.1	4K / 21.4 GB	6	0
Solar 10.7B SLERP EXL2	4K / 5.6 GB	11	4
...Instruct V1.0 3.0bpw H6 EXL2 2	4K / 4.3 GB	6	0
...Instruct V1.0 8.0bpw H8 EXL2 2	4K / 10.8 GB	3	1
SOLAR 10.7B Instruct V1.0 128K	128K / 21.4 GB	57	3
SOLAR 10.7B V1.0 Instruct 16K	16K / 21.4 GB	63	2
SauerkrautLM SOLAR Instruct	8K / 21.4 GB	62	46
MetaModelv2	8K / 21.4 GB	197	0

Note: green Score (e.g. "73.2") means that the model is better than LoneStriker/SOLAR-10.7B-Instruct-v1.0-6.0bpw-h6-exl2-2.