Instructions to use QuantFactory/Arch-Function-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Arch-Function-3B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/Arch-Function-3B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Arch-Function-3B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Arch-Function-3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Arch-Function-3B-GGUF",
	filename="Arch-Function-3B.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/Arch-Function-3B-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/Arch-Function-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Arch-Function-3B-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/Arch-Function-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Arch-Function-3B-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/Arch-Function-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Arch-Function-3B-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/Arch-Function-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Arch-Function-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Arch-Function-3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Arch-Function-3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Arch-Function-3B-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/Arch-Function-3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Arch-Function-3B-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Arch-Function-3B-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/Arch-Function-3B-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/Arch-Function-3B-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/Arch-Function-3B-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/Arch-Function-3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Arch-Function-3B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Arch-Function-3B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Arch-Function-3B-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/Arch-Function-3B-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/Arch-Function-3B-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/Arch-Function-3B-GGUF to start chatting

Pi new

How to use QuantFactory/Arch-Function-3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Arch-Function-3B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantFactory/Arch-Function-3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/Arch-Function-3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Arch-Function-3B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantFactory/Arch-Function-3B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/Arch-Function-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Arch-Function-3B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Arch-Function-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Arch-Function-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Arch-Function-3B-GGUF-Q4_K_M

List all available models

lemonade list

munish0838 commited on Jan 5, 2025

Commit

0f66054

verified ·

1 Parent(s): 939ce98

Upload README.md with huggingface_hub

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README.md +92 -82

README.md CHANGED Viewed

@@ -6,7 +6,7 @@ license_name: katanemo-research
 license_link: >-
   https://huggingface.co/katanemolabs/Arch-Function-3B/blob/main/LICENSE
 base_model:
-- Qwen/Qwen2.5-3B-Instruct
 language:
 - en
 pipeline_tag: text-generation
@@ -66,7 +66,7 @@ Katanemo Arch-Function collection is built on top of the [Qwen 2.5](https://hugg
 ## Performance Benchmarks
-We evaluate Katanemo Arch-Function series on the [Berkeley Function-Calling Leaderboard (BFCL)](https://gorilla.cs.berkeley.edu/leaderboard.html#leaderboard). For each model family, we select the one with the highest rank. The results are shwon below:
 <table>
   <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
@@ -87,103 +87,113 @@ We evaluate Katanemo Arch-Function series on the [Berkeley Function-Calling Lead
   </tr>
   <tr style="text-align: center; vertical-align: middle;">
     <td>1</td>
-    <td>GPT-4-turbo-2024-04-09</td>
-    <td>59.49%</td>
-    <td>82.65%</td>
-    <td>83.80%</td>
-    <td>73.39%</td>
-    <td>21.62%</td>
-    <td>70.73%</td>
-    <td>79.79%</td>
-  </tr>
-  <tr style="text-align: center; vertical-align: middle;">
-    <td>3</td>
-    <td>xLAM-8x22b-r</td>
-    <td>59.13%</td>
-    <td>89.75%</td>
-    <td>89.32%</td>
-    <td>72.81%</td>
-    <td>15.62%</td>
-    <td>97.56%</td>
-    <td>75.23%</td>
   </tr>
   <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
     <td> </td>
     <td>Arch-Function-7B</td>
-    <td>57.48%</td>
-    <td>87.50%</td>
-    <td>86.80%</td>
-    <td>72.19%</td>
-    <td>13.75%</td>
-    <td>82.93%</td>
-    <td>79.54%</td>
   </tr>
   <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
     <td> </td>
     <td>Arch-Function-3B</td>
-    <td>56.23%</td>
-    <td>85.10%</td>
-    <td>89.16%</td>
-    <td>70.72%</td>
-    <td>12.28%</td>
     <td>90.24%</td>
-    <td>73.98%</td>
   </tr>
-  <tr style="text-align: center; vertical-align: middle;">
-    <td>7</td>
-    <td>mistral-large-2407</td>
-    <td>55.82%</td>
-    <td>84.12%</td>
-    <td>83.09%</td>
-    <td>67.17%</td>
-    <td>20.50%</td>
-    <td>78.05%</td>
-    <td>48.93%</td>
-  </tr>
-  <tr style="text-align: center; vertical-align: middle;">
-    <td>9</td>
-    <td>Claude-3.5-Sonnet-20240620</td>
-    <td>54.83%</td>
-    <td>70.35%</td>
-    <td>66.34%</td>
-    <td>71.39%</td>
-    <td>23.5%</td>
-    <td>63.41%</td>
-    <td>75.91%</td>
   </tr>
   </tr>
   <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
     <td> </td>
-    <td>Arch-Function-3B</td>
-    <td>53.61%</td>
-    <td>82.60%</td>
-    <td>87.36%</td>
-    <td>68.19%</td>
-    <td>8.62%</td>
     <td>87.80%</td>
-    <td>75.90%</td>
   </tr>
-  <tr style="text-align: center; vertical-align: middle;">
-    <td>11</td>
-    <td>o1-mini-2024-09-12</td>
-    <td>53.43%</td>
-    <td>75.48%</td>
-    <td>76.86%</td>
-    <td>71.17%</td>
-    <td>11.00%</td>
-    <td>46.34%</td>
-    <td>88.07%</td>
   </tr>
-  <tr style="text-align: center; vertical-align: middle;">
-    <td>12</td>
-    <td>Gemini-1.5-Flash-Preview-0514</td>
-    <td>53.01%</td>
-    <td>77.10%</td>
-    <td>71.23%</td>
-    <td>71.17%</td>
-    <td>13.12%</td>
-    <td>60.98%</td>
-    <td>76.15%</td>
   </tr>
 </table>

 license_link: >-
   https://huggingface.co/katanemolabs/Arch-Function-3B/blob/main/LICENSE
 base_model:
+- Qwen/Qwen2.5-Coder-3B-Instruct
 language:
 - en
 pipeline_tag: text-generation
 ## Performance Benchmarks
+We evaluate Katanemo Arch-Function series on the [Berkeley Function-Calling Leaderboard (BFCL)](https://gorilla.cs.berkeley.edu/leaderboard.html#leaderboard). We compare with commonly-used models and the results (as of Oct 21st, 2024) are shwon below. For each model family, we select the one with the highest rank.
 <table>
   <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
   </tr>
   <tr style="text-align: center; vertical-align: middle;">
     <td>1</td>
+    <td>GPT-4o-2024-08-06 (FC)</td>
+    <td>62.19%</td>
+    <td>85.90%</td>
+    <td>85.64%</td>
+    <td>75.43%</td>
+    <td>25.00%</td>
+    <td>63.41%</td>
+    <td>82.93%</td>
   </tr>
   <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
     <td> </td>
     <td>Arch-Function-7B</td>
+    <td>59.62%</td>
+    <td>86.83%</td>
+    <td>88.07%</td>
+    <td>71.57%</td>
+    <td>21.00%</td>
+    <td>95.12%</td>
+    <td>73.63%</td>
+  </tr>
+  <tr style="text-align: center; vertical-align: middle;">
+    <td>6</td>
+    <td>o1-preview-2024-09-12 (Prompt)</td>
+    <td>59.27%</td>
+    <td>86.42%</td>
+    <td>88.88%</td>
+    <td>73.08%</td>
+    <td>17.62%</td>
+    <td>73.17%</td>
+    <td>74.60%</td>
+  </tr>
+  <tr style="text-align: center; vertical-align: middle; ">
+    <td>9</td>
+    <td>Gemini-1.5-Flash-002 (Prompt)</td>
+    <td>57.92%</td>
+    <td>86.58%</td>
+    <td>89.48%</td>
+    <td>76.28%</td>
+    <td>9.88%</td>
+    <td>85.37%</td>
+    <td>78.54%</td>
   </tr>
   <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
     <td> </td>
     <td>Arch-Function-3B</td>
+    <td>57.69%</td>
+    <td>85.19%</td>
+    <td>86.18%</td>
+    <td>71.21%</td>
+    <td>17.50%</td>
     <td>90.24%</td>
+    <td>72.88%</td>
   </tr>
+  <tr style="text-align: center; vertical-align: middle; ">
+    <td>12</td>
+    <td>Claude-3.5-Sonnet-20240620 (FC)</td>
+    <td>57.42%</td>
+    <td>70.04%</td>
+    <td>66.27%</td>
+    <td>74.68%</td>
+    <td>28.38%</td>
+    <td>68.29%</td>
+    <td>74.58%</td>
   </tr>
+  <tr style="text-align: center; vertical-align: middle; ">
+    <td>13</td>
+    <td>mistral-large-2407 (FC)</td>
+    <td>56.80%</td>
+    <td>86.62%</td>
+    <td>84.57%</td>
+    <td>68.37%</td>
+    <td>20.62%</td>
+    <td>75.61%</td>
+    <td>49.44%</td>
   </tr>
   <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
     <td> </td>
+    <td>Arch-Function-1.5B</td>
+    <td>56.20%</td>
+    <td>84.40%</td>
+    <td>83.96%</td>
+    <td>69.36%</td>
+    <td>15.88%</td>
     <td>87.80%</td>
+    <td>74.39%</td>
   </tr>
+  <tr style="text-align: center; vertical-align: middle; ">
+    <td>21</td>
+    <td>Llama-3.1-70B-Instruct (Prompt)</td>
+    <td>53.67%</td>
+    <td>88.90%</td>
+    <td>89.34%</td>
+    <td>61.13%</td>
+    <td>12.38%</td>
+    <td>92.68%</td>
+    <td>58.38%</td>
   </tr>
+  <tr style="text-align: center; vertical-align: middle; ">
+    <td>22</td>
+    <td>Gemma-2-27b-it (Prompt)</td>
+    <td>53.66%</td>
+    <td>88.52%</td>
+    <td>87.89%</td>
+    <td>69.48%</td>
+    <td>4.12%</td>
+    <td>87.8%</td>
+    <td>68.76%</td>
   </tr>
 </table>