Instructions to use kd13/Type-o1-mini-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kd13/Type-o1-mini-instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="kd13/Type-o1-mini-instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kd13/Type-o1-mini-instruct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kd13/Type-o1-mini-instruct-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 kd13/Type-o1-mini-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kd13/Type-o1-mini-instruct-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 kd13/Type-o1-mini-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kd13/Type-o1-mini-instruct-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 kd13/Type-o1-mini-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kd13/Type-o1-mini-instruct-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 kd13/Type-o1-mini-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kd13/Type-o1-mini-instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/kd13/Type-o1-mini-instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use kd13/Type-o1-mini-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kd13/Type-o1-mini-instruct-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": "kd13/Type-o1-mini-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kd13/Type-o1-mini-instruct-GGUF:Q4_K_M

SGLang

How to use kd13/Type-o1-mini-instruct-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 "kd13/Type-o1-mini-instruct-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": "kd13/Type-o1-mini-instruct-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 "kd13/Type-o1-mini-instruct-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": "kd13/Type-o1-mini-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use kd13/Type-o1-mini-instruct-GGUF with Ollama:
```
ollama run hf.co/kd13/Type-o1-mini-instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use kd13/Type-o1-mini-instruct-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 kd13/Type-o1-mini-instruct-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 kd13/Type-o1-mini-instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kd13/Type-o1-mini-instruct-GGUF to start chatting

Docker Model Runner
How to use kd13/Type-o1-mini-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/kd13/Type-o1-mini-instruct-GGUF:Q4_K_M
```

Lemonade

How to use kd13/Type-o1-mini-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kd13/Type-o1-mini-instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Type-o1-mini-instruct-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Type-o1-mini-instruct-GGUF / README.md

kd13

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	---
	base_model: kd13/Type-o1-mini-instruct
	library_name: transformers
	pipeline_tag: text-generation
	license: mit
	language:
	- en
	- hi
	tags:
	- gguf
	- llama.cpp
	- quantized
	---

	# Type-o1-mini-instruct - GGUF

	GGUF quantizations of [kd13/Type-o1-mini-instruct](https://huggingface.co/kd13/Type-o1-mini-instruct), a compact general-purpose instruct model (~1B parameters) for everyday assistant use.

	Converted with [llama.cpp](https://github.com/ggml-org/llama.cpp).
	The IQ quant was produced with an importance matrix; the rest are static quants.

	> Read the Usage section before running these files. This model uses a custom chat
	> template, so llama.cpp requires the `--jinja` flag. Without it you will get
	> `this custom template is not supported`.

	## Provided quants

	Sorted by size, which is not the same as sorted by quality. IQ-quants are often preferable to non-IQ quants of a similar size.

	\| Link \| Type \| Size/GB \| Notes \|
	\|:-----\|:-----\|--------:\|:------\|
	\| [GGUF](https://huggingface.co/kd13/Type-o1-mini-instruct-GGUF/resolve/main/Type-o1-mini-instruct.Q2_K.gguf) \| Q2_K \| 0.6 \| \|
	\| [GGUF](https://huggingface.co/kd13/Type-o1-mini-instruct-GGUF/resolve/main/Type-o1-mini-instruct.Q3_K_S.gguf) \| Q3_K_S \| 0.6 \| \|
	\| [GGUF](https://huggingface.co/kd13/Type-o1-mini-instruct-GGUF/resolve/main/Type-o1-mini-instruct.Q3_K_M.gguf) \| Q3_K_M \| 0.7 \| lower quality \|
	\| [GGUF](https://huggingface.co/kd13/Type-o1-mini-instruct-GGUF/resolve/main/Type-o1-mini-instruct.Q3_K_L.gguf) \| Q3_K_L \| 0.7 \| \|
	\| [GGUF](https://huggingface.co/kd13/Type-o1-mini-instruct-GGUF/resolve/main/Type-o1-mini-instruct.IQ4_XS.gguf) \| IQ4_XS \| 0.7 \| \|
	\| [GGUF](https://huggingface.co/kd13/Type-o1-mini-instruct-GGUF/resolve/main/Type-o1-mini-instruct.Q4_K_S.gguf) \| Q4_K_S \| 0.8 \| fast, recommended \|
	\| [GGUF](https://huggingface.co/kd13/Type-o1-mini-instruct-GGUF/resolve/main/Type-o1-mini-instruct.Q4_K_M.gguf) \| Q4_K_M \| 0.8 \| fast, recommended \|
	\| [GGUF](https://huggingface.co/kd13/Type-o1-mini-instruct-GGUF/resolve/main/Type-o1-mini-instruct.Q5_K_S.gguf) \| Q5_K_S \| 0.9 \| \|
	\| [GGUF](https://huggingface.co/kd13/Type-o1-mini-instruct-GGUF/resolve/main/Type-o1-mini-instruct.Q5_K_M.gguf) \| Q5_K_M \| 0.9 \| \|
	\| [GGUF](https://huggingface.co/kd13/Type-o1-mini-instruct-GGUF/resolve/main/Type-o1-mini-instruct.Q6_K.gguf) \| Q6_K \| 1.0 \| very good quality \|
	\| [GGUF](https://huggingface.co/kd13/Type-o1-mini-instruct-GGUF/resolve/main/Type-o1-mini-instruct.Q8_0.gguf) \| Q8_0 \| 1.3 \| fast, best quality \|
	\| [GGUF](https://huggingface.co/kd13/Type-o1-mini-instruct-GGUF/resolve/main/Type-o1-mini-instruct.f16.gguf) \| f16 \| 2.5 \| 16 bpw, overkill \|

	## Which one should I pick?

	For a model this small the practical range is Q4_K_M through Q8_0. A 1B model has little redundancy to give up, so the very low-bit quants lose more than they would on a 7B. `Q2_K` and `Q3_K_S` are included for completeness rather than as recommendations.

	## Chat template

	This model was fine-tuned on a custom template, not the standard Llama 3 header format.
	Each message is wrapped as:

	'''
	<\|begin_of_text\|>{role}
	{content}<\|end_of_text\|>
	'''

	and generation is prompted with a trailing `<\|begin_of_text\|>assistant\n`. The full Jinja template is embedded in every GGUF file in this repo, so any runtime with Jinja support applies it automatically.

	Because this format is not one of llama.cpp's built-in recognised templates, its C++ template matcher will reject it. Passing `--jinja` tells llama.cpp to use the embedded Jinja template instead, which is what you want.

	## Usage

	### llama.cpp

	```bash
	llama-completion -m Type-o1-mini-instruct.Q4_K_M.gguf --jinja \
	-sys "You are a helpful assistant." \
	-p "Explain photosynthesis in two sentences."
	```

	Recent llama.cpp builds renamed `llama-cli` to `llama-completion`; on older builds use `llama-cli` with the same flags. For raw text completion with no template applied at all, add `-no-cnv` and drop `--jinja`.

	Server:

	```bash
	llama-server -m Type-o1-mini-instruct.Q4_K_M.gguf --jinja -c 4096
	```

	Omitting `--jinja` produces `this custom template is not supported` — that is a template-matching error, not a corrupt file.

	### Ollama

	```bash
	ollama run hf.co/kd13/Type-o1-mini-instruct-GGUF:Q4_K_M
	```