MegaBeam-Mistral-7B-300k is a fine-tuned Mistral-7B-Instruct-v0.2 language model that supports input contexts up to 320k tokens. MegaBeam-Mistral-7B-300k can be deployed on a single AWS `g5.48xlarge` instance using serving frameworks such as vLLM, Sagemaker DJL endpoint, and others. Similarities and differences beween MegaBeam-Mistral-7B-300k and Mistral-7B-Instruct-v0.2 are summarized below: _InfiniteBench is a cutting-edge benchmark tailored for evaluating the capabilities of language models to process, understand, and reason over super long contexts (100k+ tokens)_. We therefore evaluated MegaBeam-Mistral-7B-300k, Mistral-7B-Instruct-v0.2, Llama-3-8B-Instruct-262k, and Llama3-70B-1M on InfiniteBench. The InfiniteBench authors also evaluated SOTA proprietary and open-source LLMs on InfiniteBench. We thus combined both results in the table below. The 12 evaluation tasks are summarized below (as per InfiniteBench)) | Task Name | Context | # Examples | Avg Input Tokens | Avg Output Tokens | Description | | -------------------- | ------------- | ---------- | ---------------- | ----------------- | ------------------------------------------------------------------------------------------- | | En.Sum | Fake Book | 103 | 171.5k | 1.1k | Summarization of a fake book created with core entity substitution. | | En.QA | Fake Book | 351 | 192.6k | 4.8 | Free-form question answering based on the fake book. | | En.MC | Fake Book | 229 | 184.4k | 5.3 | Multiple choice questions derived from the fake book. | | En.Dia | Script | 200 | 103.6k | 3.4 | Identification of talkers in partially anonymized scripts. | | Zh.QA | New Book | 175 | 2068.6k | 6.3 | Question answering on a set of newly collected books. | | Code.Debug | Code Document | 394 | 114.7k | 4.8 | Finding which function in a code repo contains an crashing error (in multiple choice form). | | Code.Run | Synthetic | 400 | 75.2k | 1.3 | Simulating execution of multiple simple, synthetic functions. | | Math.Calc | Synthetic | 50 | 43.9k | 43.9k | Calculations involving super-long arithmetic equations. | | Math.Find | Synthetic | 350 | 87.9k | 1.3 | Finding special integers in a lengthy list. | | Retrieve.PassKey | Syntheti

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MegaBeam-Mistral-7B-512k

aws-prototyping

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.

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Llama-3.3-70B-Instruct-bnb-4bit

axana-labs

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Llama-3.3-70B-Instruct-GGUF

axana-labs

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Qwen2.5-72B-Instruct-bnb-4bit

axana-labs

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splade-pt-br

AxelPCG

SPLADE (Sparse Lexical AnD Expansion) model fine-tuned for **Portuguese** text retrieval. Based on BERTimbau and trained on Portuguese question-answering datasets. SPLADE is a neural retrieval model that learns to expand queries and documents with contextually relevant terms while maintaining sparsity. Unlike dense retrievers, SPLADE produces sparse vectors (typically ~99% sparse) that are: - **Interpretable**: Each dimension corresponds to a vocabulary token - **Efficient**: Can use inverted indexes for fast retrieval - **Effective**: Combines lexical matching with semantic expansion - **Base Model**: `neuralmind/bert-base-portuguese-cased` (BERTimbau) - **Vocabulary Size**: 29,794 tokens (Portuguese-optimized) - **Training Iterations**: 150,000 - **Final Training Loss**: 0.000047 - **Sparsity**: ~99.5% (100-150 active dimensions per vector) - **Max Sequence Length**: 256 tokens

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