Instructions to use QuantFactory/Typst-Coder-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Typst-Coder-9B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/Typst-Coder-9B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Typst-Coder-9B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Typst-Coder-9B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Typst-Coder-9B-GGUF",
	filename="Typst-Coder-9B.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Typst-Coder-9B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Typst-Coder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Typst-Coder-9B-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 QuantFactory/Typst-Coder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Typst-Coder-9B-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 QuantFactory/Typst-Coder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Typst-Coder-9B-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 QuantFactory/Typst-Coder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Typst-Coder-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Typst-Coder-9B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Typst-Coder-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Typst-Coder-9B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Typst-Coder-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Typst-Coder-9B-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Typst-Coder-9B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/Typst-Coder-9B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Typst-Coder-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/Typst-Coder-9B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Typst-Coder-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Typst-Coder-9B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Typst-Coder-9B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Typst-Coder-9B-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 QuantFactory/Typst-Coder-9B-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 QuantFactory/Typst-Coder-9B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Typst-Coder-9B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Typst-Coder-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Typst-Coder-9B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Typst-Coder-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Typst-Coder-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Typst-Coder-9B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Typst-Coder-9B-GGUF

This is quantized version of TechxGenus/Typst-Coder-9B created using llama.cpp

Original Model Card

Typst-Coder

[🤖Models] | [🛠️Code] | [📊Data] |

Typst-Coder
- Introduction
- Usage

Introduction

While working with Typst documents, we noticed that AI programming assistants often generate poor results. I understand that these assistants may perform better in languages like Python and JavaScript, which benefit from more extensive datasets and feedback signals from executable code, unlike HTML or Markdown. However, current LLMs even frequently struggle to produce accurate Typst syntax, including models like GPT-4o and Claude-3.5-Sonnet.

Upon further investigation, we found that because Typst is a relatively new language, training data for it is scarce. GitHub's search tool doesn't categorize it as a language for code yet, and The Stack v1/v2 don’t include Typst. No open code LLMs currently list it as a supported language, either. To address this, we developed this project aimed at collecting relevant data and training models to improve Typst support in AI programming tools.

Usage

An example script is shown below:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TechxGenus/Typst-Coder-9B")
model = AutoModelForCausalLM.from_pretrained(
    "TechxGenus/Typst-Coder-9B",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Hi!"},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512)
print(tokenizer.decode(outputs[0]))

Downloads last month: 60

GGUF

Model size

9B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Typst-Coder-9B-GGUF

Base model

01-ai/Yi-Coder-9B

Quantized

(6)

this model