Instructions to use RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf",
	filename="phi3-4x4b-v1.IQ3_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf:Q4_K_M
```

Unsloth Studio

How to use RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf to start chatting

Docker Model Runner
How to use RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Fizzarolli_-_phi3-4x4b-v1-gguf-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

phi3-4x4b-v1 - GGUF

Model creator: https://huggingface.co/Fizzarolli/
Original model: https://huggingface.co/Fizzarolli/phi3-4x4b-v1/

Name	Quant method	Size
phi3-4x4b-v1.Q2_K.gguf	Q2_K	3.79GB
phi3-4x4b-v1.IQ3_XS.gguf	IQ3_XS	4.23GB
phi3-4x4b-v1.IQ3_S.gguf	IQ3_S	4.47GB
phi3-4x4b-v1.Q3_K_S.gguf	Q3_K_S	4.47GB
phi3-4x4b-v1.IQ3_M.gguf	IQ3_M	4.59GB
phi3-4x4b-v1.Q3_K.gguf	Q3_K	4.97GB
phi3-4x4b-v1.Q3_K_M.gguf	Q3_K_M	4.97GB
phi3-4x4b-v1.Q3_K_L.gguf	Q3_K_L	5.39GB
phi3-4x4b-v1.IQ4_XS.gguf	IQ4_XS	5.56GB
phi3-4x4b-v1.Q4_0.gguf	Q4_0	5.83GB
phi3-4x4b-v1.IQ4_NL.gguf	IQ4_NL	5.87GB
phi3-4x4b-v1.Q4_K_S.gguf	Q4_K_S	5.88GB
phi3-4x4b-v1.Q4_K.gguf	Q4_K	6.25GB
phi3-4x4b-v1.Q4_K_M.gguf	Q4_K_M	6.25GB
phi3-4x4b-v1.Q4_1.gguf	Q4_1	6.46GB
phi3-4x4b-v1.Q5_0.gguf	Q5_0	7.1GB
phi3-4x4b-v1.Q5_K_S.gguf	Q5_K_S	7.1GB
phi3-4x4b-v1.Q5_K.gguf	Q5_K	7.32GB
phi3-4x4b-v1.Q5_K_M.gguf	Q5_K_M	7.32GB
phi3-4x4b-v1.Q5_1.gguf	Q5_1	7.74GB
phi3-4x4b-v1.Q6_K.gguf	Q6_K	8.46GB
phi3-4x4b-v1.Q8_0.gguf	Q8_0	10.96GB

Original model description:

license: mit tags: - phi3 - nlp - moe datasets: - BEE-spoke-data/gutenberg-en-v1-clean - NeelNanda/pile-10k

phi 3 4x4b

a continually pretrained phi3-mini sparse moe upcycle

benchmarks

ran locally

	Microsoft/phi-3-4k-instruct	Fizzarolli/phi3-4x4b-v1
MMLU acc. (0-shot)	0.6799	0.6781
Hellaswag acc. (0-shot)	0.6053	0.5962
ARC-E acc. (0-shot)	0.8325	0.8367
ARC-C acc. (0-shot)	0.5546	0.5606

honestly i was expecting it to do worse :p, but those are all within a margin of error! so it didn't lose any performance, at least

open llm leaderboard

todo!

support me on ko-fi!

~~please i need money to stay alive and keep making models~~

notes

not trained on instruct data. it's pretty likely that it won't be much different from phi 3 if you use it like that, if not worse due to any forgetting of instruct formats during the continued training.

future experiments

the datasets for this were literally chosen on a whim. perhaps experiment with a further filtered HuggingFaceFW/fineweb-edu?
actually freeze the gate layers next time (see Chen et. al, 2023), oops
MOAR TRAINING, this only went up to ~0.2 of an epoch because i ran out of dolar

Downloads last month: 288

GGUF

Model size

11B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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Paper for RichardErkhov/Fizzarolli_-_phi3-4x4b-v1-gguf

Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers

Paper • 2303.01610 • Published Mar 2, 2023