What are the hardware requirements for BioMistralMerged?

BioMistralMerged requires approximately 14.4 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 BioMistralMerged and how large is it?

BioMistralMerged is developed by skfrost19, 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 BioMistralMerged perform on standard benchmarks?

BioMistralMerged has the following published scores: MMLU 60.06. Compare against reference models on this page or on the LLM Explorer leaderboards.

BioMistralMerged by skfrost19 — VRAM 14.4GB, 32K context

Name: BioMistralMerged
Author: skfrost19

BioMistralMerged is an open-source language model by skfrost19. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, Merged, LLM Explorer Score: 0.14, Arc: 62.5, HellaSwag: 84, MMLU: 60.1, GSM8K: 0.1.

Merged Model Base model:biomistral/biomistr... Base model:quantized:biomistra... Conversational Deploy:azure Endpoints compatible Gguf Instruct Mistral Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/skfrost19/BioMistralMerged

BioMistralMerged Parameters and Internals

Model Type

pre-trained language model, merged model

Additional Notes

This model is a merge of two pre-trained language models using the SLERP method.

Training Details

Methodology:

The model was merged using the SLERP merge method using mergekit, involving configurations across layers from two source models.

LLM Name	BioMistralMerged
Repository 🤗	https://huggingface.co/skfrost19/BioMistralMerged
Base Model(s)	mohsenfayyaz/Mistral-7B-Instruct-v0.2_medical_bios_5000_5ep BioMistral 7B mohsenfayyaz/Mistral-7B-Instruct-v0.2_medical_bios_5000_5ep BioMistral/BioMistral-7B
Merged Model	Yes
Model Size	7b
Required VRAM	14.4 GB
Updated	2026-07-01
Maintainer	skfrost19
Model Type	mistral
Instruction-Based	Yes
Model Files	7.7 GB 2.0 GB: 1-of-8 1.9 GB: 2-of-8 2.0 GB: 3-of-8 2.0 GB: 4-of-8 1.9 GB: 5-of-8 1.9 GB: 6-of-8 1.9 GB: 7-of-8 0.8 GB: 8-of-8
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.40.0
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the BioMistralMerged 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 BioMistralMerged

Best Alternatives	Context / RAM	Downloads	Likes
Mistral 7B Instruct V0.2.gguf	32K / 14.5 GB	17	3
Mistral 7B Instruct V0.2 GGUF	32K / 4.4 GB	26	2
...andle Mistral 7B Instruct V0.2	32K / 14.4 GB	200	0
Tamil Mistral 7B Instruct V0.1	32K / 14.8 GB	350	14
Mistral V0.3 7B Cybersecurity	32K / 14.5 GB	52	1
...andle Mistral 7B Instruct V0.1	32K / 14.4 GB	1207	3
Mistral 7B Instruct V0.3 GGUF	32K / 2.7 GB	189	1
Mistral 7B Instruct V0.3 GGUF	32K / 2.7 GB	156	1
...stral 7B V0.3 Chinese Chat F16	32K / 14.5 GB	51	1
...tral 7B V0.3 Chinese Chat 4bit	32K / 4.1 GB	45	2

Note: green Score (e.g. "73.2") means that the model is better than skfrost19/BioMistralMerged.