What are the hardware requirements for Openchat 3.6 8B 20240522 AWQ?

Openchat 3.6 8B 20240522 AWQ requires approximately 5.8 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 Openchat 3.6 8B 20240522 AWQ and how large is it?

Openchat 3.6 8B 20240522 AWQ is developed by vaclavkosar, 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 Openchat 3.6 8B 20240522 AWQ?

Openchat 3.6 8B 20240522 AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Openchat 3.6 8B 20240522 AWQ by vaclavkosar — VRAM 5.8GB, 8K context

Name: Openchat 3.6 8B 20240522 AWQ
Author: vaclavkosar

Openchat 3.6 8B 20240522 AWQ is an open-source language model by vaclavkosar. Features: 8b LLM, VRAM: 5.8GB, Context: 8K, License: llama3, Quantized, LLM Explorer Score: 0.12.

Arxiv:2309.11235 4-bit Awq Base model:meta-llama/meta-lla... Base model:quantized:meta-llam... C-rlft Conversational Endpoints compatible Llama Llama3 Openchat Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/vaclavkosar/openchat-3.6-8b-20240522-AWQ

Openchat 3.6 8B 20240522 AWQ Parameters and Internals

Model Type

text-generation

Use Cases

Areas:

research, commercial applications

Applications:

coding, chat, general tasks

Primary Use Cases:

coding, chat, general tasks

Limitations:

complex reasoning, mathematical and arithmetic tasks, programming challenges, hallucination of non-existent information, may generate harmful or biased responses

Considerations:

Users should verify critical information obtained from the model and apply additional AI safety measures as needed.

Additional Notes

This model is optimized for deployment using vLLM and can use tensor parallelism for performance improvements.

Input Output

Input Format:

JSON objects with message list and 'role' and 'content' fields

Accepted Modalities:

text

Output Format:

Text

Performance Tips:

Set `<|end_of_turn|>` as end of generation token for best performance.

LLM Name	Openchat 3.6 8B 20240522 AWQ
Repository 🤗	https://huggingface.co/vaclavkosar/openchat-3.6-8b-20240522-AWQ
Base Model(s)	Meta Llama 3 8B meta-llama/Meta-Llama-3-8B
Model Size	8b
Required VRAM	5.8 GB
Updated	2026-05-24
Maintainer	vaclavkosar
Model Type	llama
Model Files	4.7 GB: 1-of-2 1.1 GB: 2-of-2
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	LlamaForCausalLM
License	llama3
Context Length	8192
Model Max Length	8192
Transformers Version	4.41.0
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	128256
Torch Data Type	float16

Rank the Openchat 3.6 8B 20240522 AWQ capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Best Alternatives to Openchat 3.6 8B 20240522 AWQ

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...8B Instruct Gradient 1048K AWQ	1024K / 5.8 GB	8	0
...radient 1048K AWQ 4bit Smashed	1024K / 5.8 GB	7	1
...Instruct 262K AWQ 4bit Smashed	256K / 5.8 GB	5	4
...ta Llama 3 8B Instruct 64K AWQ	64K / 5.8 GB	6	0
... Instruct 8B 32K V0.1 4bit AWQ	64K / 5.8 GB	9	0
Llama 3 8B Instruct AWQ	8K / 5.8 GB	25458	31
Meta Llama 3 8B Instruct AWQ	8K / 5.8 GB	6	0
Meta Llama 3 8B Instruct AWQ	8K / 5.8 GB	563	0
Meta Llama 3 8B Instruct AWQ	8K / 5.8 GB	7	0
Meta Llama 3 8B Instruct AWQ	8K / 5.8 GB	401	5

Note: green Score (e.g. "73.2") means that the model is better than vaclavkosar/openchat-3.6-8b-20240522-AWQ.