What are the hardware requirements for AutogenJune2000?

AutogenJune2000 requires approximately 33.8 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed AutogenJune2000 and how large is it?

AutogenJune2000 is developed by Jass07, a model with 8b 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 AutogenJune2000?

AutogenJune2000 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

AutogenJune2000 by Jass07 — VRAM 33.8GB, 8K context

Name: AutogenJune2000
Author: Jass07

AutogenJune2000 is an open-source language model by Jass07. Features: 8b LLM, VRAM: 33.8GB, Context: 8K, LLM Explorer Score: 0.13.

Arxiv:1910.09700 Endpoints compatible Llama Lora Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Jass07/autogenJune2000

AutogenJune2000 Parameters and Internals

LLM Name	AutogenJune2000
Repository 🤗	https://huggingface.co/Jass07/autogenJune2000
Model Size	8b
Required VRAM	33.8 GB
Updated	2026-08-02
Maintainer	Jass07
Model Type	llama
Model Files	0.1 GB 5.0 GB: 1-of-4 1.1 GB: 1-of-17 4.9 GB: 2-of-4 1.0 GB: 2-of-17 4.9 GB: 3-of-4 1.0 GB: 3-of-17 2.6 GB: 4-of-4 1.0 GB: 4-of-17 1.0 GB: 5-of-17 1.0 GB: 6-of-17 1.0 GB: 7-of-17 1.0 GB: 8-of-17 1.0 GB: 9-of-17 1.0 GB: 10-of-17 1.0 GB: 11-of-17 1.0 GB: 12-of-17 1.0 GB: 13-of-17 1.0 GB: 14-of-17 1.0 GB: 15-of-17 1.1 GB: 16-of-17 0.2 GB: 17-of-17
Model Architecture	AutoModelForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.42.0.dev0
Is Biased	none
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|end_of_text\|>
Vocabulary Size	128256
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	q_proj\|v_proj
LoRA Alpha	16
LoRA Dropout	0.1
R Param	64
Torch Data Type	float16

Rank the AutogenJune2000 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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Note: green Score (e.g. "73.2") means that the model is better than Jass07/autogenJune2000.