What are the hardware requirements for Japanese Stablelm Base Gamma 7B?

Japanese Stablelm Base Gamma 7B requires approximately 14.4 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 Japanese Stablelm Base Gamma 7B and how large is it?

Japanese Stablelm Base Gamma 7B is developed by stabilityai, 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 Japanese Stablelm Base Gamma 7B perform on standard benchmarks?

Japanese Stablelm Base Gamma 7B has the following published scores: MMLU 54.75. Compare against reference models on this page or on the LLM Explorer leaderboards.

Japanese Stablelm Base Gamma 7B by stabilityai — VRAM 14.4GB, 4K context

Name: Japanese Stablelm Base Gamma 7B
Author: stabilityai

Japanese Stablelm Base Gamma 7B is an open-source language model by stabilityai. Features: 7b LLM, VRAM: 14.4GB, Context: 4K, License: apache-2.0, LLM Explorer Score: 0.13, Arc: 50.3, HellaSwag: 77.5, MMLU: 54.8, GSM8K: 17.8.

Arxiv:2310.06825 Dataset:cc100 Dataset:cerebras/slimpajama-62... Dataset:mc4 Dataset:oscar-corpus/oscar-220... Dataset:oscar-corpus/oscar-230... Dataset:wikipedia Deploy:azure Endpoints compatible Ja Japanese-stablelm Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/stabilityai/japanese-stablelm-base-gamma-7b

Japanese Stablelm Base Gamma 7B Benchmarks

ARC: 50.34 vs 96.7 (so35)^-47.9%

HellaSwag: 77.47 vs 95.3 (gpt4)^-18.7%

MMLU: 54.75 vs 88.3 (so35)^-38%

GSM8K: 17.82 vs 96.4 (so35)^-81.5%

LLME Score: 0.12782

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Japanese Stablelm Base Gamma 7B (stabilityai/japanese-stablelm-base-gamma-7b)

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Japanese Stablelm Base Gamma 7B Parameters and Internals

Model Type

text generation, causal-lm

Use Cases

Areas:

research, commercial applications

Limitations:

pre-training dataset may have contained offensive or inappropriate content

Considerations:

Exercise reasonable caution when using models in production systems; Avoid applications that may cause harm or distress to individuals or groups.

Supported Languages

Japanese (High proficiency)

Training Details

Data Sources:

wikipedia, mc4, cc100, oscar-corpus/OSCAR-2301, oscar-corpus/OSCAR-2201, cerebras/SlimPajama-627B

Data Volume:

100B tokens

Methodology:

Continued pretraining on Japanese data

Model Architecture:

transformer decoder architecture

Input Output

Accepted Modalities:

text

LLM Name	Japanese Stablelm Base Gamma 7B
Repository 🤗	https://huggingface.co/stabilityai/japanese-stablelm-base-gamma-7b
Model Size	7b
Required VRAM	14.4 GB
Updated	2026-05-26
Maintainer	stabilityai
Model Type	mistral
Model Files	9.9 GB: 1-of-2 4.5 GB: 2-of-2
Supported Languages	ja
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Transformers Version	4.34.0.dev0
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	bfloat16

Quantized Models of the Japanese Stablelm Base Gamma 7B

Model	Likes	Downloads	VRAM
Gamma Translation Gguf	1	166	0 GB

Best Alternatives to Japanese Stablelm Base Gamma 7B

Best Alternatives	Context / RAM	Downloads	Likes
...Nemo Instruct 2407 Abliterated	1000K / 24.5 GB	254	20
MegaBeam Mistral 7B 512K	512K / 14.4 GB	8623	54
SpydazWeb AI HumanAI RP	512K / 14.4 GB	24	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	8131	17
Hebrew Mistral 7B 200K	256K / 30 GB	1378	15
Astral 256K 7B V2	250K / 14.4 GB	5	0
Astral 256K 7B	250K / 14.4 GB	5	0

Note: green Score (e.g. "73.2") means that the model is better than stabilityai/japanese-stablelm-base-gamma-7b.

Rank the Japanese Stablelm Base Gamma 7B Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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

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Release v20260328a

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Japanese Stablelm Base Gamma 7B by stabilityai