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

prithivMLmods commited on Jul 1, 2025

Commit

e27d871

verified ·

1 Parent(s): 76f9543

Update README.md

Browse files

Files changed (1) hide show

README.md +31 -1

README.md CHANGED Viewed

@@ -12,4 +12,34 @@ tags:
 # **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.

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