What are the hardware requirements for Phoenix AWQ?

Phoenix AWQ requires approximately 4.2 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Phoenix AWQ and how large is it?

Phoenix AWQ is developed by DRXD1000, a model with 7.2b 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 Phoenix AWQ?

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

Phoenix AWQ by DRXD1000 — VRAM 4.2GB, 32K context

Name: Phoenix AWQ
Author: DRXD1000

Phoenix AWQ is an open-source language model by DRXD1000. Features: 7.2b LLM, VRAM: 4.2GB, Context: 32K, License: apache-2.0, Quantized, LLM Explorer Score: 0.1.

Arxiv:2401.10580 4-bit Alignment-handbook Awq Conversational De Dpo Endpoints compatible Mistral Quantization Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/DRXD1000/Phoenix-AWQ

Phoenix AWQ Parameters and Internals

Model Type

GPT-like, 7B model, DPO fine-tuned

Use Cases

Areas:

research, commercial applications

Additional Notes

Phoenix training procedure follows the process of alignment-handbook from Huggingface.

Supported Languages

German (High proficiency)

Training Details

Data Sources:

HuggingFaceH4/ultrachat_200k, argilla/ultrafeedback-binarized-preferences

Methodology:

Direct Preference Optimization (DPO)

Context Length:

4096

Hardware Used:

8 x A100 80GB

Model Architecture:

GPT-like

Input Output

Input Format:

<|system|> ~~ <|user|> {prompt}~~ <|assistant|>

Accepted Modalities:

text

Output Format:

text

Performance Tips:

Avoid system prompts as Phoenix does not react well to them.

LLM Name	Phoenix AWQ
Repository 🤗	https://huggingface.co/DRXD1000/Phoenix-AWQ
Model Size	7.2b
Required VRAM	4.2 GB
Updated	2026-08-25
Maintainer	DRXD1000
Model Type	mistral
Model Files	4.2 GB
Supported Languages	de
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.36.2
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	float16

Rank the Phoenix 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 Phoenix AWQ

Best Alternatives	Context / RAM	Downloads	Likes
Feedback Wizard 4bit AWQ	32K / 4.2 GB	6	0
Ryze AWQ 4bit	32K / 4.2 GB	5	0
...V3 Plot Chat Roleplay Chat AWQ	32K / 4.2 GB	7	0
...lot Engine Step Json Short AWQ	32K / 4.2 GB	5	0
MistralLite AWQ	32K / 4.2 GB	27	6
LemonadeRP 4.5.3 AWQ	32K / 4.2 GB	5	0
Functionary Small V2.4 AWQ	32K / 4.2 GB	1	1
Hc Mistral Alpaca Merged AWQ	32K / 4.2 GB	5	0
Garrulus AWQ	32K / 4.2 GB	7	2
HamSter 0.1 AWQ	32K / 4.2 GB	5	2

Note: green Score (e.g. "73.2") means that the model is better than DRXD1000/Phoenix-AWQ.