Instructions to use maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf",
	filename="stabilityai-japanese-stablelm-3b-4e1t-base-Q4_0.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-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 maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-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 maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-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 maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf:Q4_K_M

Use Docker

docker model run hf.co/maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf:Q4_K_M

Ollama
How to use maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf with Ollama:
```
ollama run hf.co/maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf:Q4_K_M
```

Unsloth Studio

How to use maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-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 maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-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 maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf with Docker Model Runner:
```
docker model run hf.co/maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf:Q4_K_M
```

Lemonade

How to use maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull maddes8cht/stabilityai-japanese-stablelm-3b-4e1t-base-gguf:Q4_K_M

Run and chat with the model

lemonade run user.stabilityai-japanese-stablelm-3b-4e1t-base-gguf-Q4_K_M

List all available models

lemonade list

⚠️ ARCHIVED / LEGACY MODEL NOTICE
This repository is part of a legacy collection quantized around 2023. To manage storage quotas and maintain active community projects, some rarely used quantization formats (e.g., Q2_K, Q3_K, Q4_1, Q5_1) have been permanently removed.

Only the most popular and stable formats (Q4_0, Q4_K_M, Q5_K_M, Q6_K, and Q8_0) remain available.

💡 Looking for something modern? If you are starting a new project, we highly recommend using newer architectures (like Llama 3, Mistral, or Qwen) provided by official maintainers or active community members (e.g., Bartowski, TheBloke legacy files, or official organization handles).

⚠️ This repository is no longer actively maintained. Existing files are provided "as is" for archival and legacy hardware purposes.

I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information

japanese-stablelm-3b-4e1t-base - GGUF

Model creator: stabilityai
Original model: japanese-stablelm-3b-4e1t-base

StableLM

This is a Model based on StableLM. Stablelm is a familiy of Language Models by Stability AI.

Note:

Current (as of 2023-11-15) implementations of Llama.cpp only support GPU offloading up to 34 Layers with these StableLM Models. The model will crash immediately if -ngl is larger than 34. The model works fine however without any gpu acceleration.

About GGUF format

gguf is the current file format used by the ggml library. A growing list of Software is using it and can therefore use this model. The core project making use of the ggml library is the llama.cpp project by Georgi Gerganov

Quantization variants

There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you:

Legacy quants

Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are legacy quantization types. Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.

Note:

Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not real K-quants. More details can be found in affected model descriptions. (This mainly refers to Falcon 7b and Starcoder models)

K-quants

K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load. So, if possible, use K-quants. With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences.

Original Model Card:

Japanese StableLM-3B-4E1T Base

Model Description

This is a 3B-parameter decoder-only language model with a focus on maximizing Japanese language modeling performance and Japanese downstream task performance. We conducted continued pretraining using Japanese data on the English language model, StableLM-3B-4E1T, to transfer the model's knowledge and capabilities to Japanese.

If you are looking for an instruction-following model, please check Japanese StableLM-3B-4E1T Instruct.

If you are in search of a larger model, please check Japanese Stable LM Base Gamma 7B.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/japanese-stablelm-3b-4e1t-base")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/japanese-stablelm-3b-4e1t-base",
  trust_remote_code=True,
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("AI で科学研究を加速するには、", return_tensors="pt").to("cuda")
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.75,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))

Model Details

Developed by: Stability AI
Model type: Japanese StableLM-3B-4E1T Base model is an auto-regressive language models based on the transformer decoder architecture.
Language(s): Japanese
License: This model is licensed under Apache License, Version 2.0.
Contact: For questions and comments about the model, please join Stable Community Japan. For future announcements / information about Stability AI models, research, and events, please follow https://twitter.com/StabilityAI_JP.

Model Architecture

The model is a decoder-only transformer similar to the LLaMA (Touvron et al., 2023) architecture with the following modifications:

Parameters	Hidden Size	Layers	Heads	Sequence Length
2,795,443,200	2560	32	32	4096

Position Embeddings: Rotary Position Embeddings (Su et al., 2021) applied to the first 25% of head embedding dimensions for improved throughput following Black et al. (2022).
Normalization: LayerNorm (Ba et al., 2016) with learned bias terms as opposed to RMSNorm (Zhang & Sennrich, 2019).
Tokenizer: GPT-NeoX (Black et al., 2022).

Training Dataset

Around 100B tokens from a mixture of the following corpora were used for the continued pretraining.

Use and Limitations

Intended Use

The model is intended to be used by all individuals as a foundational model for application-specific fine-tuning without strict limitations on commercial use.

Limitations and bias

The pre-training dataset may have contained offensive or inappropriate content even after applying data cleansing filters which can be reflected in the model-generated text. We recommend users exercise reasonable caution when using these models in production systems. Do not use the model for any applications that may cause harm or distress to individuals or groups.

Credits

The continued pre-training was carried out by Takuya Akiba. Other aspects, including data preparation and evaluation, were handled by the Language Team of Stability AI Japan, notably Meng Lee, Fujiki Nakamura, Makoto Shing, Paul McCann, and Naoki Orii.

Acknowledgements

We are grateful for the contributions of the EleutherAI Polyglot-JA team in helping us to collect a large amount of pre-training data in Japanese. Polyglot-JA members includes Hyunwoong Ko (Project Lead), Fujiki Nakamura (originally started this project when he commited to the Polyglot team), Yunho Mo, Minji Jung, KeunSeok Im, and Su-Kyeong Jang.

We are also appreciative of AI Novelist/Sta (Bit192, Inc.) and the numerous contributors from Stable Community Japan for assisting us in gathering a large amount of high-quality Japanese textual data for model training.

End of original Model File

Please consider to support my work

Coming Soon: I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community.