What are the hardware requirements for YamShadow 7B?

YamShadow 7B requires approximately 14.5 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 YamShadow 7B and how large is it?

YamShadow 7B is developed by automerger, 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 YamShadow 7B perform on standard benchmarks?

YamShadow 7B has the following published scores: MMLU 64.64. Compare against reference models on this page or on the LLM Explorer leaderboards.

YamShadow 7B by automerger — VRAM 14.5GB, 32K context

Name: YamShadow 7B
Author: automerger

YamShadow 7B is an open-source language model by automerger. Features: 7b LLM, VRAM: 14.5GB, Context: 32K, License: apache-2.0, Merged, LLM Explorer Score: 0.12, Arc: 72.5, HellaSwag: 88.9, MMLU: 64.6, GSM8K: 70.2.

Merged Model Base model:corticalstack/shado... Base model:finetune:corticalst... Endpoints compatible Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/automerger/YamShadow-7B

YamShadow 7B Parameters and Internals

Training Details

Data Sources:

mayacinka/yam-jom-7B, CorticalStack/shadow-clown-7B-slerp

Methodology:

Automated merge with density: 0.53, weight: 0.6, merge_method: dare_ties

LLM Name	YamShadow 7B
Repository 🤗	https://huggingface.co/automerger/YamShadow-7B
Base Model(s)	Shadow Clown 7B Slerp CorticalStack/shadow-clown-7B-slerp
Merged Model	Yes
Model Size	7b
Required VRAM	14.5 GB
Updated	2026-08-07
Maintainer	automerger
Model Type	mistral
Model Files	9.8 GB: 1-of-2 4.7 GB: 2-of-2
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.38.2
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the YamShadow 7B 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

Quantized Models of the YamShadow 7B

Model	Likes	Downloads	VRAM
YamShadow 7B GGUF	1	526	2 GB

Best Alternatives to YamShadow 7B

Best Alternatives	Context / RAM	Downloads	Likes
...Nemo Instruct 2407 Abliterated	1000K / 24.5 GB	220	20
MegaBeam Mistral 7B 512K	512K / 14.4 GB	8470	54
SpydazWeb AI HumanAI RP	512K / 14.4 GB	16	1
SpydazWeb AI HumanAI 002	512K / 14.4 GB	18	1
...daz Web AI ChatML 512K Project	512K / 14.5 GB	12	0
MegaBeam Mistral 7B 300K	282K / 14.4 GB	3779	16
MegaBeam Mistral 7B 300K	282K / 14.4 GB	8018	17
Hebrew Mistral 7B 200K	256K / 30 GB	23	15
Astral 256K 7B	250K / 14.4 GB	9	0
Astral 256K 7B V2	250K / 14.4 GB	8	0

Note: green Score (e.g. "73.2") means that the model is better than automerger/YamShadow-7B.