What are the hardware requirements for F1H10M 0000?

F1H10M 0000 requires approximately 13.9 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed F1H10M 0000 and how large is it?

F1H10M 0000 is developed by Ali-C137, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate F1H10M 0000?

F1H10M 0000 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

F1H10M 0000 by Ali-C137 — VRAM 13.9GB, 2K context

Name: F1H10M 0000
Author: Ali-C137

F1H10M 0000 is an open-source language model by Ali-C137. Features: 7b LLM, VRAM: 13.9GB, Context: 2K, License: apache-2.0, LLM Explorer Score: 0.15.

Arxiv:1910.09700 Autotrain compatible Custom code Endpoints compatible Falcon Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Ali-C137/F1H10M-0000

F1H10M 0000 Parameters and Internals

LLM Name	F1H10M 0000
Repository 🤗	https://huggingface.co/Ali-C137/F1H10M-0000
Model Size	7b
Required VRAM	13.9 GB
Updated	2024-08-30
Maintainer	Ali-C137
Model Type	falcon
Model Files	5.0 GB: 1-of-3 5.0 GB: 2-of-3 3.9 GB: 3-of-3
Model Architecture	FalconForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.41.2
Is Biased	0
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	65024
Torch Data Type	bfloat16

Rank the F1H10M 0000 capabilities

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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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