Instructions to use prithivMLmods/Arch-Router-1.5B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Arch-Router-1.5B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Arch-Router-1.5B-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Arch-Router-1.5B-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Arch-Router-1.5B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Arch-Router-1.5B-GGUF",
	filename=" Arch-Router-1.5B.BF16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use prithivMLmods/Arch-Router-1.5B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Arch-Router-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Arch-Router-1.5B-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 prithivMLmods/Arch-Router-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Arch-Router-1.5B-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/Arch-Router-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Arch-Router-1.5B-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/Arch-Router-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Arch-Router-1.5B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Arch-Router-1.5B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Arch-Router-1.5B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Arch-Router-1.5B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Arch-Router-1.5B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/prithivMLmods/Arch-Router-1.5B-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Arch-Router-1.5B-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/Arch-Router-1.5B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Arch-Router-1.5B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/Arch-Router-1.5B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Arch-Router-1.5B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use prithivMLmods/Arch-Router-1.5B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Arch-Router-1.5B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use prithivMLmods/Arch-Router-1.5B-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/Arch-Router-1.5B-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/Arch-Router-1.5B-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/Arch-Router-1.5B-GGUF to start chatting

Docker Model Runner
How to use prithivMLmods/Arch-Router-1.5B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Arch-Router-1.5B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Arch-Router-1.5B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Arch-Router-1.5B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Arch-Router-1.5B-GGUF-Q4_K_M

List all available models

lemonade list

Arch-Router-1.5B

Arch-Router-1.5B introduces a preference-aligned routing framework that guides model selection by matching queries to user-defined domains (e.g., travel) or action types (e.g., image editing) -- offering a practical mechanism to encode preferences in routing decisions. Specifically, we introduce Arch-Router, a compact 1.5B model that learns to map queries to domain-action preferences for model routing decisions. Experiments on conversational datasets demonstrate that our approach achieves state-of-the-art (SOTA) results in matching queries with human preferences, outperforming top proprietary models.

Model Files

File Name	Size	Type	Description
Arch-Router-1.5B.Q2_K.gguf	676 MB	Model	Q2_K quantized model (smallest)
Arch-Router-1.5B.Q3_K_S.gguf	761 MB	Model	Q3_K_S quantized model
Arch-Router-1.5B.Q3_K_M.gguf	824 MB	Model	Q3_K_M quantized model
Arch-Router-1.5B.Q3_K_L.gguf	880 MB	Model	Q3_K_L quantized model
Arch-Router-1.5B.Q4_K_S.gguf	940 MB	Model	Q4_K_S quantized model
Arch-Router-1.5B.Q4_K_M.gguf	986 MB	Model	Q4_K_M quantized model
Arch-Router-1.5B.Q5_K_S.gguf	1.1 GB	Model	Q5_K_S quantized model
Arch-Router-1.5B.Q5_K_M.gguf	1.13 GB	Model	Q5_K_M quantized model
Arch-Router-1.5B.Q6_K.gguf	1.27 GB	Model	Q6_K quantized model
Arch-Router-1.5B.Q8_0.gguf	1.65 GB	Model	Q8_0 quantized model
Arch-Router-1.5B.BF16.gguf	3.09 GB	Model	BF16 precision model
Arch-Router-1.5B.F16.gguf	3.09 GB	Model	F16 precision model
Arch-Router-1.5B.F32.gguf	6.18 GB	Model	F32 full precision model (largest)
.gitattributes	2.49 kB	Config	Git LFS configuration
config.json	31 Bytes	Config	Model configuration
README.md	173 Bytes	Documentation	Repository documentation

Quants Usage

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

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

Downloads last month: 104

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for prithivMLmods/Arch-Router-1.5B-GGUF

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Finetuned

katanemo/Arch-Router-1.5B

Quantized

(9)

this model