Ichigo-llama3.1-s-instruct-v0.4-i1-GGUF Open-source Model - A Good Helper for Speech and Language Processing in Resource-constrained Environments

Ichigo Llama3.1 S Instruct V0.4 I1 GGUF

Developed by mradermacher

This is the GGUF quantized version of the Menlo/Ichigo-llama3.1-s-instruct-v0.4 model, designed for speech and language processing tasks in resource-constrained environments.

Large Language Model EnglishOpen Source License:Apache-2.0 #Voice Command Understanding #Low-resource Quantization #English-specific

Downloads 429

Release Time : 11/8/2024

Model Overview

An instruction-tuned model based on the Llama3 architecture, specializing in speech-to-text tasks with multiple quantization options to meet various hardware requirements.

Model Features

Multiple Quantization Options

Offers 24 quantization versions from IQ1 to Q6_K to meet performance needs across different hardware conditions

Hardware Optimization

Specially optimized for ARM architecture devices to enhance mobile runtime efficiency

Speech Processing Capability

Trained on the homebrewltd/instruction-speech-whispervq-v2 dataset, excelling in voice command understanding

Model Capabilities

Speech-to-text

Command Understanding

English Speech Processing

Use Cases

Voice Interaction

Voice Assistant

Enables localized voice command processing on resource-constrained devices

Transcription Services

Real-time Speech Transcription

Converts English speech content to text in real time

base_model: Menlo/Ichigo-llama3.1-s-instruct-v0.4 datasets:

homebrewltd/instruction-speech-whispervq-v2 language:
en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags:
sound language model
audio-text-to-text
torchtune

About

weighted/imatrix quants of https://huggingface.co/Menlo/Ichigo-llama3.1-s-instruct-v0.4

static quants are available at https://huggingface.co/mradermacher/Ichigo-llama3.1-s-instruct-v0.4-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	i1-IQ1_S	2.1	for the desperate
GGUF	i1-IQ1_M	2.3	mostly desperate
GGUF	i1-IQ2_XXS	2.5
GGUF	i1-IQ2_XS	2.7
GGUF	i1-IQ2_S	2.9
GGUF	i1-IQ2_M	3.1
GGUF	i1-Q2_K	3.3	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	3.4	lower quality
GGUF	i1-IQ3_XS	3.6
GGUF	i1-Q3_K_S	3.8	IQ3_XS probably better
GGUF	i1-IQ3_S	3.8	beats Q3_K*
GGUF	i1-IQ3_M	3.9
GGUF	i1-Q3_K_M	4.1	IQ3_S probably better
GGUF	i1-Q3_K_L	4.4	IQ3_M probably better
GGUF	i1-IQ4_XS	4.6
GGUF	i1-Q4_0_4_4	4.8	fast on arm, low quality
GGUF	i1-Q4_0_4_8	4.8	fast on arm+i8mm, low quality
GGUF	i1-Q4_0_8_8	4.8	fast on arm+sve, low quality
GGUF	i1-Q4_0	4.8	fast, low quality
GGUF	i1-Q4_K_S	4.8	optimal size/speed/quality
GGUF	i1-Q4_K_M	5.0	fast, recommended
GGUF	i1-Q5_K_S	5.7
GGUF	i1-Q5_K_M	5.8
GGUF	i1-Q6_K	6.7	practically like static Q6_K

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

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