Instructions to use prithivMLmods/Open-Xi-Math-Preview-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Open-Xi-Math-Preview-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Open-Xi-Math-Preview-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Open-Xi-Math-Preview-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/Open-Xi-Math-Preview-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 prithivMLmods/Open-Xi-Math-Preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Open-Xi-Math-Preview-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 prithivMLmods/Open-Xi-Math-Preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Open-Xi-Math-Preview-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 prithivMLmods/Open-Xi-Math-Preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Open-Xi-Math-Preview-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 prithivMLmods/Open-Xi-Math-Preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Open-Xi-Math-Preview-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Open-Xi-Math-Preview-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Open-Xi-Math-Preview-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Open-Xi-Math-Preview-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": "prithivMLmods/Open-Xi-Math-Preview-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Open-Xi-Math-Preview-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Open-Xi-Math-Preview-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 "prithivMLmods/Open-Xi-Math-Preview-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": "prithivMLmods/Open-Xi-Math-Preview-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 "prithivMLmods/Open-Xi-Math-Preview-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": "prithivMLmods/Open-Xi-Math-Preview-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/Open-Xi-Math-Preview-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Open-Xi-Math-Preview-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/Open-Xi-Math-Preview-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 prithivMLmods/Open-Xi-Math-Preview-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 prithivMLmods/Open-Xi-Math-Preview-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Open-Xi-Math-Preview-GGUF to start chatting

Docker Model Runner
How to use prithivMLmods/Open-Xi-Math-Preview-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Open-Xi-Math-Preview-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Open-Xi-Math-Preview-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Open-Xi-Math-Preview-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Open-Xi-Math-Preview-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Open-Xi-Math-Preview-GGUF / README.md

prithivMLmods

Update README.md

b2a18b4 verified about 1 year ago

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	---
	license: apache-2.0
	language:
	- en
	base_model:
	- prithivMLmods/Open-Xi-Math-Preview
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- text-generation-inference
	- math
	---

	# Open-Xi-Math-Preview-GGUF

	> Open-Xi-Math-Preview is a mathematics-focused reasoning model fine-tuned on Qwen2-1.5B-Instruct, utilizing a modular dataset designed for enhancing mathematical thinking. It provides robust capabilities in symbolic reasoning, structured deduction, and compact coding — optimized for edge deployment on resource-constrained devices.

	## Model Files

	\| File Name \| Size \| Quantization \| Format \| Description \|
	\| ---------------------------------- \| ------- \| ------------ \| ------ \| ----------------------------- \|
	\| `Open-Xi-Math-Preview.BF16.gguf` \| 3.56 GB \| BF16 \| GGUF \| BFloat16 precision version \|
	\| `Open-Xi-Math-Preview.F16.gguf` \| 3.56 GB \| FP16 \| GGUF \| Float16 precision version \|
	\| `Open-Xi-Math-Preview.F32.gguf` \| 7.11 GB \| FP32 \| GGUF \| Float32 precision version \|
	\| `Open-Xi-Math-Preview.Q2_K.gguf` \| 753 MB \| Q2\_K \| GGUF \| 2-bit quantized (K variant) \|
	\| `Open-Xi-Math-Preview.Q3_K_M.gguf` \| 924 MB \| Q3\_K\_M \| GGUF \| 3-bit quantized (K M variant) \|
	\| `Open-Xi-Math-Preview.Q4_K_M.gguf` \| 1.12 GB \| Q4\_K\_M \| GGUF \| 4-bit quantized (K M variant) \|
	\| `Open-Xi-Math-Preview.Q4_K_S.gguf` \| 1.07 GB \| Q4\_K\_S \| GGUF \| 4-bit quantized (K S variant) \|
	\| `Open-Xi-Math-Preview.Q5_K_M.gguf` \| 1.29 GB \| Q5\_K\_M \| GGUF \| 5-bit quantized (K M variant) \|
	\| `Open-Xi-Math-Preview.Q5_K_S.gguf` \| 1.26 GB \| Q5\_K\_S \| GGUF \| 5-bit quantized (K S variant) \|
	\| `Open-Xi-Math-Preview.Q6_K.gguf` \| 1.46 GB \| Q6\_K \| GGUF \| 6-bit quantized (K variant) \|
	\| `Open-Xi-Math-Preview.Q8_0.gguf` \| 1.89 GB \| Q8\_0 \| GGUF \| 8-bit quantized \|
	\| `.gitattributes` \| 2.39 kB \| — \| — \| Git LFS tracking file \|
	\| `config.json` \| 31 B \| — \| — \| Configuration file \|
	\| `README.md` \| 4.29 kB \| — \| — \| Model documentation \|

	## Quants Usage

	(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

	\| Link \| Type \| Size/GB \| Notes \|
	\|:-----\|:-----\|--------:\|:------\|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q2_K.gguf) \| Q2_K \| 0.4 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_S.gguf) \| Q3_K_S \| 0.5 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_M.gguf) \| Q3_K_M \| 0.5 \| lower quality \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_L.gguf) \| Q3_K_L \| 0.5 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.IQ4_XS.gguf) \| IQ4_XS \| 0.6 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q4_K_S.gguf) \| Q4_K_S \| 0.6 \| fast, recommended \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q4_K_M.gguf) \| Q4_K_M \| 0.6 \| fast, recommended \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q5_K_S.gguf) \| Q5_K_S \| 0.6 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q5_K_M.gguf) \| Q5_K_M \| 0.7 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q6_K.gguf) \| Q6_K \| 0.7 \| very good quality \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q8_0.gguf) \| Q8_0 \| 0.9 \| fast, best quality \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.f16.gguf) \| f16 \| 1.6 \| 16 bpw, overkill \|

	Here is a handy graph by ikawrakow comparing some lower-quality quant
	types (lower is better):

	![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

	---
	license: apache-2.0
	language:
	- en
	base_model:
	- prithivMLmods/Open-Xi-Math-Preview
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- text-generation-inference
	- math
	---

	# Open-Xi-Math-Preview-GGUF

	> Open-Xi-Math-Preview is a mathematics-focused reasoning model fine-tuned on Qwen2-1.5B-Instruct, utilizing a modular dataset designed for enhancing mathematical thinking. It provides robust capabilities in symbolic reasoning, structured deduction, and compact coding — optimized for edge deployment on resource-constrained devices.

	## Model Files

	\| File Name \| Size \| Quantization \| Format \| Description \|
	\| ---------------------------------- \| ------- \| ------------ \| ------ \| ----------------------------- \|
	\| `Open-Xi-Math-Preview.BF16.gguf` \| 3.56 GB \| BF16 \| GGUF \| BFloat16 precision version \|
	\| `Open-Xi-Math-Preview.F16.gguf` \| 3.56 GB \| FP16 \| GGUF \| Float16 precision version \|
	\| `Open-Xi-Math-Preview.F32.gguf` \| 7.11 GB \| FP32 \| GGUF \| Float32 precision version \|
	\| `Open-Xi-Math-Preview.Q2_K.gguf` \| 753 MB \| Q2\_K \| GGUF \| 2-bit quantized (K variant) \|
	\| `Open-Xi-Math-Preview.Q3_K_M.gguf` \| 924 MB \| Q3\_K\_M \| GGUF \| 3-bit quantized (K M variant) \|
	\| `Open-Xi-Math-Preview.Q4_K_M.gguf` \| 1.12 GB \| Q4\_K\_M \| GGUF \| 4-bit quantized (K M variant) \|
	\| `Open-Xi-Math-Preview.Q4_K_S.gguf` \| 1.07 GB \| Q4\_K\_S \| GGUF \| 4-bit quantized (K S variant) \|
	\| `Open-Xi-Math-Preview.Q5_K_M.gguf` \| 1.29 GB \| Q5\_K\_M \| GGUF \| 5-bit quantized (K M variant) \|
	\| `Open-Xi-Math-Preview.Q5_K_S.gguf` \| 1.26 GB \| Q5\_K\_S \| GGUF \| 5-bit quantized (K S variant) \|
	\| `Open-Xi-Math-Preview.Q6_K.gguf` \| 1.46 GB \| Q6\_K \| GGUF \| 6-bit quantized (K variant) \|
	\| `Open-Xi-Math-Preview.Q8_0.gguf` \| 1.89 GB \| Q8\_0 \| GGUF \| 8-bit quantized \|
	\| `.gitattributes` \| 2.39 kB \| — \| — \| Git LFS tracking file \|
	\| `config.json` \| 31 B \| — \| — \| Configuration file \|
	\| `README.md` \| 4.29 kB \| — \| — \| Model documentation \|

	## Quants Usage

	(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

	\| Link \| Type \| Size/GB \| Notes \|
	\|:-----\|:-----\|--------:\|:------\|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q2_K.gguf) \| Q2_K \| 0.4 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_S.gguf) \| Q3_K_S \| 0.5 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_M.gguf) \| Q3_K_M \| 0.5 \| lower quality \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_L.gguf) \| Q3_K_L \| 0.5 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.IQ4_XS.gguf) \| IQ4_XS \| 0.6 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q4_K_S.gguf) \| Q4_K_S \| 0.6 \| fast, recommended \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q4_K_M.gguf) \| Q4_K_M \| 0.6 \| fast, recommended \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q5_K_S.gguf) \| Q5_K_S \| 0.6 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q5_K_M.gguf) \| Q5_K_M \| 0.7 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q6_K.gguf) \| Q6_K \| 0.7 \| very good quality \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q8_0.gguf) \| Q8_0 \| 0.9 \| fast, best quality \|
	\| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.f16.gguf) \| f16 \| 1.6 \| 16 bpw, overkill \|

	Here is a handy graph by ikawrakow comparing some lower-quality quant
	types (lower is better):

	![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)