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AI Model Library/aws-prototyping/MegaBeam-Mistral-7B-512k
MODEL PROFILE

MegaBeam-Mistral-7B-512k

aws-prototyping · MegaBeam-Mistral-7B-512k

This model, presented in Scaling Context, Not Parameters: Training a Compact 7B Language Model for Efficient Long-Context Processing, is a Long-Context LLM that supports 524,288 tokens in its context. `MegaBeam-Mistral-7B-512k` was trained on Mistral-7B Instruct-v0.2, and can be deployed using various serving frameworks like vLLM and Amazon SageMaker's DJL endpoint. Please refer to our GitRepo for deployment and inference examples. We evaluated `MegaBeam-Mistral-7B-512k` on three long-context benchmarks. For each benchmark, we deployed the `MegaBeam-Mistral-7B-512k` model with vLLM (v0.5.1) on an EC2 instance and obtained LLM responses through the OpenAI API provided by vLLM. The Arize-ai NIAH varies the target random number and introduces a random city for each question, requiring the LLM to extract the random number from various selected context locations.

Text generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/30 12:09:37
OVERVIEW

Model overview

This model, presented in Scaling Context, Not Parameters: Training a Compact 7B Language Model for Efficient Long-Context Processing, is a Long-Context LLM that supports 524,288 tokens in its context. `MegaBeam-Mistral-7B-512k` was trained on Mistral-7B Instruct-v0.2, and can be deployed using various serving frameworks like vLLM and Amazon SageMaker's DJL endpoint. Please refer to our GitRepo for deployment and inference examples. We evaluated `MegaBeam-Mistral-7B-512k` on three long-context benchmarks. For each benchmark, we deployed the `MegaBeam-Mistral-7B-512k` model with vLLM (v0.5.1) on an EC2 instance and obtained LLM responses through the OpenAI API provided by vLLM. The Arize-ai NIAH varies the target random number and introduces a random city for each question, requiring the LLM to extract the random number from various selected context locations.

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

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Sources & traceability

huggingface · aws-prototyping/MegaBeam-Mistral-7B-512k ↗2026/10/29 23:03:05
huggingface · enrich:c61702d6-4396-4681-af3d-3def6a39f323 ↗2026/10/30 12:09:37

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。