What are the hardware requirements for Mistral Instruct SCQA?

Mistral Instruct SCQA requires approximately 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 Mistral Instruct SCQA and how large is it?

Mistral Instruct SCQA is developed by codegood. 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 Mistral Instruct SCQA?

Mistral Instruct SCQA is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mistral Instruct SCQA by codegood — VRAM 5GB, 32K context

Name: Mistral Instruct SCQA
Author: codegood

Mistral Instruct SCQA is an open-source language model by codegood. Features: LLM, VRAM: 5GB, Context: 32K, Instruction-Based, LLM Explorer Score: 0.1.

Endpoints compatible Instruct Mistral Pytorch Region:us

Model Card on HF 🤗: https://huggingface.co/codegood/Mistral_Instruct_SCQA

Mistral Instruct SCQA Parameters and Internals

LLM Name	Mistral Instruct SCQA
Repository 🤗	https://huggingface.co/codegood/Mistral_Instruct_SCQA
Required VRAM	5 GB
Updated	2026-08-03
Maintainer	codegood
Model Type	mistral
Instruction-Based	Yes
Model Files	5.0 GB
Model Architecture	MistralForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.34.0.dev0
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Mistral Instruct SCQA 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 Mistral Instruct SCQA

Best Alternatives	Context / RAM	Downloads	Likes
Krutrim 2 Instruct	1000K / 49.3 GB	866	38
Dolphin 2.9.2 Phi 3 Medium	4K / 28 GB	9292	24
Dolphin 2.9.2 Phi 3 Medium	4K / 28 GB	8209	26
Lora Phi3 Mini	4K / 5 GB	7	0
...eZephir Sft Instruct Ead 16bit	32K / 14.4 GB	56	0
... Instruct Unsloth Merged 16bit	4K / 7.6 GB	6	1
... Mini 4K Instruct Bnb 4bit Ita	4K / 7.6 GB	1181	0
... Mini 4K Instruct Bnb 4bit Ita	4K / 7.6 GB	1527	0
...ini New Model With Lora Merged	4K / 7.6 GB	16	0
Phired	4K / 7.6 GB	12	0

Note: green Score (e.g. "73.2") means that the model is better than codegood/Mistral_Instruct_SCQA.