What are the hardware requirements for Tess 7B V2.0 AWQ?

Tess 7B V2.0 AWQ requires approximately 4.2 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 Tess 7B V2.0 AWQ and how large is it?

Tess 7B V2.0 AWQ is developed by solidrust, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Tess 7B V2.0 AWQ perform on standard benchmarks?

Tess 7B V2.0 AWQ has the following published scores: MMLU 52.05. Compare against reference models on this page or on the LLM Explorer leaderboards.

Tess 7B V2.0 AWQ by solidrust — VRAM 4.2GB, 32K context

Name: Tess 7B V2.0 AWQ
Author: solidrust

Tess 7B V2.0 AWQ is an open-source language model by solidrust. Features: 7b LLM, VRAM: 4.2GB, Context: 32K, License: apache-2.0, Quantized, LLM Explorer Score: 0.14, Arc: 55.9, HellaSwag: 76.7, MMLU: 52.1, GSM8K: 24.9.

4-bit Autotrain compatible Awq Base model:migtissera/tess-7b-... Base model:quantized:migtisser... Chatml Conversational En Endpoints compatible Mistral Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/solidrust/Tess-7B-v2.0-AWQ

Tess 7B V2.0 AWQ Parameters and Internals

Model Type

mistral, text generation

Use Cases

Applications:

General purpose text generation

Limitations:

Can occasionally produce inaccurate or misleading results, Possibility of generating inappropriate, biased, or offensive content

Supported Languages

en (High)

Input Output

Input Format:

ChatML format with system and user prompts.

Accepted Modalities:

text

Output Format:

Text

Performance Tips:

Ensure appropriate hardware (NVIDIA GPU) for optimal performance.

LLM Name	Tess 7B V2.0 AWQ
Repository 🤗	https://huggingface.co/solidrust/Tess-7B-v2.0-AWQ
Model Name	Tess-7B-v2.0
Model Creator	migtissera
Base Model(s)	migtissera/Tess-7B-v2.0 migtissera/Tess-7B-v2.0
Model Size	7b
Required VRAM	4.2 GB
Updated	2026-07-28
Maintainer	solidrust
Model Type	mistral
Model Files	4.2 GB
Supported Languages	en
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.38.2
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	float16

Rank the Tess 7B V2.0 AWQ 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 Tess 7B V2.0 AWQ

Best Alternatives	Context / RAM	Downloads	Likes
Mistral 7B Instruct V0.2 AWQ	32K / 4.2 GB	6	0
Mistral 7B Instruct V0.2 AWQ	32K / 4.2 GB	298182	52
Mistral 7B Instruct V0.3 AWQ	32K / 4.2 GB	5633	10
Mistral 7B Instruct V0.1 AWQ	32K / 4.2 GB	15461	2
Ghost 7B Alpha AWQ	32K / 4.2 GB	14	0
OpenHermes 2.5 Mistral 7B AWQ	32K / 4.2 GB	23691	21
...reeze 7B 32K Instruct V1.0 AWQ	32K / 4.7 GB	9	0
Breeze 7B Instruct V1.0 AWQ	32K / 4.6 GB	5	0
NSFW DPO Noromaid 7B AWQ	32K / 4.2 GB	206	2
Mahou 1.3a Mistral 7B AWQ	32K / 4.2 GB	13	1

Note: green Score (e.g. "73.2") means that the model is better than solidrust/Tess-7B-v2.0-AWQ.