Dolphin3.0-Llama3.2-1B-GGUF Open Source Model - Multi-Quantized Versions Support Text Generation Tasks

Dolphin3.0 Llama3.2 1B GGUF

Developed by bartowski

A 1B-parameter quantized model based on Llama3.2 architecture, supporting text generation tasks with multiple quantization version options

Large Language Model English#Multitask Text Generation #Efficient Quantized Deployment #Code and Mathematical Reasoning

Downloads 1,134

Release Time : 1/5/2025

Model Overview

This is a quantized text generation model based on cognitivecomputations/Dolphin3.0-Llama3.2-1B, processed using llama.cpp quantization tools. The model supports multiple quantization levels for different hardware environments.

Model Features

Multiple Quantization Versions

Offers various quantization versions from F32 to Q2_K to meet different hardware and performance needs

imatrix Quantization

Uses llama.cpp's imatrix option for quantization to improve quality

ARM/AVX Optimization

Supports online repacking for ARM and AVX CPUs to optimize inference performance

Embedding/Output Weight Optimization

Some quantized versions use Q8_0 quantization for embedding and output weights to enhance model quality

Model Capabilities

Text Generation

Instruction Following

Code Generation

Mathematical Problem Solving

Use Cases

Programming Assistance

Code Generation

Generates code snippets from natural language descriptions

Code Feedback

Provides code improvement suggestions and feedback

Education

Mathematical Problem Solving

Solves applied math problems and calculations

General AI Assistant

Conversational Interaction

Engages in natural language dialogue as an intelligent assistant

🚀 Llamacpp imatrix Quantizations of Dolphin3.0-Llama3.2-1B

This project provides Llama.cpp imatrix quantizations of the Dolphin3.0-Llama3.2-1B model, enabling efficient deployment and inference on various hardware platforms.

📋 Metadata

Property	Details
Quantized By	bartowski
Pipeline Tag	text-generation
Language	en
Base Model	cognitivecomputations/Dolphin3.0-Llama3.2-1B
License	llama3.2
Training Data	OpenCoder-LLM/opc-sft-stage1, OpenCoder-LLM/opc-sft-stage2, microsoft/orca-agentinstruct-1M-v1, microsoft/orca-math-word-problems-200k, NousResearch/hermes-function-calling-v1, AI-MO/NuminaMath-CoT, AI-MO/NuminaMath-TIR, allenai/tulu-3-sft-mixture, cognitivecomputations/dolphin-coder, HuggingFaceTB/smoltalk, cognitivecomputations/samantha-data, m-a-p/CodeFeedback-Filtered-Instruction, m-a-p/Code-Feedback

🚀 Quick Start

Quantization: Using llama.cpp release b4418 for quantization.
Original Model: https://huggingface.co/cognitivecomputations/Dolphin3.0-Llama3.2-1B
Running in LM Studio: You can run these quantized models in LM Studio.

✨ Features

Prompt Format

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Download Options

You can download a specific file (not the whole branch) from the following table:

Filename	Quant type	File Size	Split	Description
Dolphin3.0-Llama3.2-1B-f32.gguf	f32	4.95GB	false	Full F32 weights.
Dolphin3.0-Llama3.2-1B-f16.gguf	f16	2.48GB	false	Full F16 weights.
Dolphin3.0-Llama3.2-1B-Q8_0.gguf	Q8_0	1.32GB	false	Extremely high quality, generally unneeded but max available quant.
Dolphin3.0-Llama3.2-1B-Q6_K_L.gguf	Q6_K_L	1.09GB	false	Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
Dolphin3.0-Llama3.2-1B-Q6_K.gguf	Q6_K	1.02GB	false	Very high quality, near perfect, recommended.
Dolphin3.0-Llama3.2-1B-Q5_K_L.gguf	Q5_K_L	0.98GB	false	Uses Q8_0 for embed and output weights. High quality, recommended.
Dolphin3.0-Llama3.2-1B-Q5_K_M.gguf	Q5_K_M	0.91GB	false	High quality, recommended.
Dolphin3.0-Llama3.2-1B-Q5_K_S.gguf	Q5_K_S	0.89GB	false	High quality, recommended.
Dolphin3.0-Llama3.2-1B-Q4_K_L.gguf	Q4_K_L	0.87GB	false	Uses Q8_0 for embed and output weights. Good quality, recommended.
Dolphin3.0-Llama3.2-1B-Q4_1.gguf	Q4_1	0.83GB	false	Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
Dolphin3.0-Llama3.2-1B-Q4_K_M.gguf	Q4_K_M	0.81GB	false	Good quality, default size for most use cases, recommended.
Dolphin3.0-Llama3.2-1B-Q3_K_XL.gguf	Q3_K_XL	0.80GB	false	Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
Dolphin3.0-Llama3.2-1B-Q4_K_S.gguf	Q4_K_S	0.78GB	false	Slightly lower quality with more space savings, recommended.
Dolphin3.0-Llama3.2-1B-Q4_0.gguf	Q4_0	0.77GB	false	Legacy format, offers online repacking for ARM and AVX CPU inference.
Dolphin3.0-Llama3.2-1B-IQ4_NL.gguf	IQ4_NL	0.77GB	false	Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference.
Dolphin3.0-Llama3.2-1B-IQ4_XS.gguf	IQ4_XS	0.74GB	false	Decent quality, smaller than Q4_K_S with similar performance, recommended.
Dolphin3.0-Llama3.2-1B-Q3_K_L.gguf	Q3_K_L	0.73GB	false	Lower quality but usable, good for low RAM availability.
Dolphin3.0-Llama3.2-1B-Q3_K_M.gguf	Q3_K_M	0.69GB	false	Low quality.
Dolphin3.0-Llama3.2-1B-IQ3_M.gguf	IQ3_M	0.66GB	false	Medium-low quality, new method with decent performance comparable to Q3_K_M.
Dolphin3.0-Llama3.2-1B-Q3_K_S.gguf	Q3_K_S	0.64GB	false	Low quality, not recommended.
Dolphin3.0-Llama3.2-1B-Q2_K_L.gguf	Q2_K_L	0.64GB	false	Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
Dolphin3.0-Llama3.2-1B-IQ3_XS.gguf	IQ3_XS	0.62GB	false	Lower quality, new method with decent performance, slightly better than Q3_K_S.
Dolphin3.0-Llama3.2-1B-Q2_K.gguf	Q2_K	0.58GB	false	Very low quality but surprisingly usable.
Dolphin3.0-Llama3.2-1B-IQ2_M.gguf	IQ2_M	0.52GB	false	Relatively low quality, uses SOTA techniques to be surprisingly usable.

🔧 Technical Details

Embed/output weights

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

ARM/AVX information

Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.

Now, however, there is something called "online repacking" for weights. details in this PR. If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.

As of llama.cpp build b4282 you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.

Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to this PR which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.

Click to view Q4_0_X_X information (deprecated)

I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.

Click to view benchmarks on an AVX2 system (EPYC7702)

model	size	params	backend	threads	test	t/s	% (vs Q4_0)
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	pp512	204.03 ± 1.03	100%
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	pp1024	282.92 ± 0.19	100%
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	pp2048	259.49 ± 0.44	100%
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	tg128	39.12 ± 0.27	100%
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	tg256	39.31 ± 0.69	100%
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	tg512	40.52 ± 0.03	100%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	pp512	301.02 ± 1.74	147%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	pp1024	287.23 ± 0.20	101%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	pp2048	262.77 ± 1.81	101%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	tg128	18.80 ± 0.99	48%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	tg256	24.46 ± 3.04	83%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	tg512	36.32 ± 3.59	90%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	pp512	271.71 ± 3.53	133%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	pp1024	279.86 ± 45.63	100%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	pp2048	320.77 ± 5.00	124%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	tg128	43.51 ± 0.05	111%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	tg256	43.35 ± 0.09	110%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	tg512	42.60 ± 0.31	105%

Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation

📦 Installation

Downloading using huggingface-cli

Click to view download instructions

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download bartowski/Dolphin3.0-Llama3.2-1B-GGUF --include "Dolphin3.0-Llama3.2-1B-Q4_K_M.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download bartowski/Dolphin3.0-Llama3.2-1B-GGUF --include "Dolphin3.0-Llama3.2-1B-Q8_0/*" --local-dir ./

You can either specify a new local-dir (Dolphin3.0-Llama3.2-1B-Q8_0) or download them all in place (./)

📚 Documentation

Which file should I choose?

Click here for details

A great write up with charts showing various performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-qua

📄 License

This project is licensed under the llama3.2 license.

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