Instructions to use RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-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/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-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/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf:Q4_K_M
```

Unsloth Studio

How to use RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-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/scb10x_-_typhoon-7b-instruct-02-19-2024-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/scb10x_-_typhoon-7b-instruct-02-19-2024-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/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf:Q4_K_M

Run and chat with the model

lemonade run user.scb10x_-_typhoon-7b-instruct-02-19-2024-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

typhoon-7b-instruct-02-19-2024 - GGUF

Model creator: https://huggingface.co/scb10x/
Original model: https://huggingface.co/scb10x/typhoon-7b-instruct-02-19-2024/

Name	Quant method	Size
typhoon-7b-instruct-02-19-2024.Q2_K.gguf	Q2_K	2.55GB
typhoon-7b-instruct-02-19-2024.IQ3_XS.gguf	IQ3_XS	2.83GB
typhoon-7b-instruct-02-19-2024.IQ3_S.gguf	IQ3_S	2.98GB
typhoon-7b-instruct-02-19-2024.Q3_K_S.gguf	Q3_K_S	2.96GB
typhoon-7b-instruct-02-19-2024.IQ3_M.gguf	IQ3_M	3.07GB
typhoon-7b-instruct-02-19-2024.Q3_K.gguf	Q3_K	3.29GB
typhoon-7b-instruct-02-19-2024.Q3_K_M.gguf	Q3_K_M	3.29GB
typhoon-7b-instruct-02-19-2024.Q3_K_L.gguf	Q3_K_L	3.57GB
typhoon-7b-instruct-02-19-2024.IQ4_XS.gguf	IQ4_XS	3.69GB
typhoon-7b-instruct-02-19-2024.Q4_0.gguf	Q4_0	3.84GB
typhoon-7b-instruct-02-19-2024.IQ4_NL.gguf	IQ4_NL	3.89GB
typhoon-7b-instruct-02-19-2024.Q4_K_S.gguf	Q4_K_S	3.87GB
typhoon-7b-instruct-02-19-2024.Q4_K.gguf	Q4_K	4.09GB
typhoon-7b-instruct-02-19-2024.Q4_K_M.gguf	Q4_K_M	4.09GB
typhoon-7b-instruct-02-19-2024.Q4_1.gguf	Q4_1	4.26GB
typhoon-7b-instruct-02-19-2024.Q5_0.gguf	Q5_0	4.67GB
typhoon-7b-instruct-02-19-2024.Q5_K_S.gguf	Q5_K_S	4.67GB
typhoon-7b-instruct-02-19-2024.Q5_K.gguf	Q5_K	4.8GB
typhoon-7b-instruct-02-19-2024.Q5_K_M.gguf	Q5_K_M	4.8GB
typhoon-7b-instruct-02-19-2024.Q5_1.gguf	Q5_1	5.09GB
typhoon-7b-instruct-02-19-2024.Q6_K.gguf	Q6_K	5.55GB
typhoon-7b-instruct-02-19-2024.Q8_0.gguf	Q8_0	7.19GB

Original model description:

license: apache-2.0 pipeline_tag: text-generation

Typhoon-0219: Thai Large Language Model (Instruct)

Typhoon-0219 is a instruct Thai 🇹🇭 large language model with 7 billion parameters, and it is based on Typhoon 7B. It is the second-generation instruct model version that serves opentyphoon.ai. It is trained on a diverse instruction tuning dataset with more than 1 million rows, similar to OpenHermes, and supports using system prompts.

Model Description

Model type: A 7B instruct decoder-only model based on Mistral architecture.
Requirement: transformers 4.38.0 or newer.
Primary Language(s): Thai 🇹🇭 and English 🇬🇧
License: Apache-2.0

Intended Uses & Limitations

This model is an instructional model. However, it’s still undergoing development. It incorporates some level of guardrails, but it still may produce answers that are inaccurate, biased, or otherwise objectionable in response to user prompts. We recommend that developers assess these risks in the context of their use case.

Production Deployment

We suggest using the OpenAI-compatible API server from the vLLM project.

python -m vllm.entrypoints.openai.api_server --port 8080 --model scb10x/typhoon-7b-instruct-02-19-2024 --max-num-batched-tokens 8192 --max-model-len 8192 --served-model-name typhoon-instruct

Chat Template

We use chatml chat-template.

{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|im_end|>' + '\n'}}{% endif %}{% endfor %}
{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\n' }}{% endif %}

https://twitter.com/opentyphoon

Support

https://discord.gg/CqyBscMFpg

SCB10X AI Team

Kunat Pipatanakul, Potsawee Manakul, Sittipong Sripaisarnmongkol, Pathomporn Chokchainant, Kasima Tharnpipitchai
If you find Typhoon useful for your work, please cite it using:

@article{pipatanakul2023typhoon,
    title={Typhoon: Thai Large Language Models}, 
    author={Kunat Pipatanakul and Phatrasek Jirabovonvisut and Potsawee Manakul and Sittipong Sripaisarnmongkol and Ruangsak Patomwong and Pathomporn Chokchainant and Kasima Tharnpipitchai},
    year={2023},
    journal={arXiv preprint arXiv:2312.13951},
    url={https://arxiv.org/abs/2312.13951}
}

Contact Us

General & Collaboration: [email protected], [email protected]
Technical: [email protected]

Downloads last month: 809

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

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Paper for RichardErkhov/scb10x_-_typhoon-7b-instruct-02-19-2024-gguf

Typhoon: Thai Large Language Models

Paper • 2312.13951 • Published Dec 21, 2023 • 7

RichardErkhov
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scb10x_-_typhoon-7b-instruct-02-19-2024-gguf