What are the hardware requirements for Empower Functions Medium?

Empower Functions Medium requires approximately 93.6 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 Empower Functions Medium and how large is it?

Empower Functions Medium is developed by empower-dev. 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 Empower Functions Medium?

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

Empower Functions Medium by empower-dev — VRAM 93.6GB, 32K context

Name: Empower Functions Medium
Author: empower-dev

Empower Functions Medium is an open-source language model by empower-dev. Features: LLM, VRAM: 93.6GB, Context: 32K, License: apache-2.0, MoE, Instruction-Based, LLM Explorer Score: 0.12.

Conversational Endpoints compatible Function Function-calling Instruct Mixtral Moe Pytorch Region:us Sharded Tool-using

Model Card on HF 🤗: https://huggingface.co/empower-dev/empower-functions-medium

Empower Functions Medium Parameters and Internals

Model Type

function, function-calling, tool-using

Use Cases

Primary Use Cases:

Automatic tool using, Parallel call, Sequential calling, Streaming

Additional Notes

v1.1 model family is fully compatible with existing prompts and offers better accuracy and a longer context window. Balance in accuracy and cost.

LLM Name	Empower Functions Medium
Repository 🤗	https://huggingface.co/empower-dev/empower-functions-medium
Required VRAM	93.6 GB
Updated	2026-07-23
Maintainer	empower-dev
Model Type	mixtral
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-19 5.0 GB: 2-of-19 5.0 GB: 3-of-19 4.9 GB: 4-of-19 5.0 GB: 5-of-19 5.0 GB: 6-of-19 4.9 GB: 7-of-19 5.0 GB: 8-of-19 5.0 GB: 9-of-19 4.9 GB: 10-of-19 5.0 GB: 11-of-19 5.0 GB: 12-of-19 5.0 GB: 13-of-19 4.9 GB: 14-of-19 5.0 GB: 15-of-19 5.0 GB: 16-of-19 4.9 GB: 17-of-19 5.0 GB: 18-of-19 4.2 GB: 19-of-19
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.38.0
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Empower Functions Medium 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 Empower Functions Medium

Best Alternatives	Context / RAM	Downloads	Likes
...ixtral 8x22B Instruct V0.1 FP8	64K / 140.9 GB	98	0
Dolphin 2.6 Mixtral 8x7b	32K / 93.6 GB	11285	211
Dolphin 2.6 Mixtral 8x7b	32K / 93.6 GB	7806	217
...eqlen 4096 Bs 4 Optimum 0 0 23	32K / GB	8	0
...eqlen 4096 Bs 4 Optimum 0 0 23	32K / GB	9	1
Dolphin 2.7 Mixtral 8x7b	32K / 93.6 GB	2178	170
Mixtral 8x7B Instruct V0.1	32K / GB	5	0
...ral 8x7b Instruct V0.1 Int4 Ov	32K / 0 GB	16	4
Dolphin 2.7 Mixtral 8x7b	32K / 93.6 GB	228	169
...ct V0.1 Agent Function Calling	32K / 44.3 GB	4	2

Note: green Score (e.g. "73.2") means that the model is better than empower-dev/empower-functions-medium.