Instructions to use katanemo/Arch-Function-1.5B.gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use katanemo/Arch-Function-1.5B.gguf with Transformers:

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

pipe = pipeline("text-generation", model="katanemo/Arch-Function-1.5B.gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("katanemo/Arch-Function-1.5B.gguf", device_map="auto")

llama-cpp-python

How to use katanemo/Arch-Function-1.5B.gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="katanemo/Arch-Function-1.5B.gguf",
	filename="Arch-Function-1.5B-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use katanemo/Arch-Function-1.5B.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 katanemo/Arch-Function-1.5B.gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf katanemo/Arch-Function-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 serve -hf katanemo/Arch-Function-1.5B.gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf katanemo/Arch-Function-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 katanemo/Arch-Function-1.5B.gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf katanemo/Arch-Function-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 katanemo/Arch-Function-1.5B.gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf katanemo/Arch-Function-1.5B.gguf:Q4_K_M

Use Docker

docker model run hf.co/katanemo/Arch-Function-1.5B.gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use katanemo/Arch-Function-1.5B.gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "katanemo/Arch-Function-1.5B.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": "katanemo/Arch-Function-1.5B.gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/katanemo/Arch-Function-1.5B.gguf:Q4_K_M

SGLang

How to use katanemo/Arch-Function-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 "katanemo/Arch-Function-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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "katanemo/Arch-Function-1.5B.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 "katanemo/Arch-Function-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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "katanemo/Arch-Function-1.5B.gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use katanemo/Arch-Function-1.5B.gguf with Ollama:
```
ollama run hf.co/katanemo/Arch-Function-1.5B.gguf:Q4_K_M
```

Unsloth Studio

How to use katanemo/Arch-Function-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 katanemo/Arch-Function-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 katanemo/Arch-Function-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 katanemo/Arch-Function-1.5B.gguf to start chatting

How to use katanemo/Arch-Function-1.5B.gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf katanemo/Arch-Function-1.5B.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": "katanemo/Arch-Function-1.5B.gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use katanemo/Arch-Function-1.5B.gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf katanemo/Arch-Function-1.5B.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 katanemo/Arch-Function-1.5B.gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use katanemo/Arch-Function-1.5B.gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf katanemo/Arch-Function-1.5B.gguf:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "katanemo/Arch-Function-1.5B.gguf:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use katanemo/Arch-Function-1.5B.gguf with Docker Model Runner:
```
docker model run hf.co/katanemo/Arch-Function-1.5B.gguf:Q4_K_M
```

Lemonade

How to use katanemo/Arch-Function-1.5B.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull katanemo/Arch-Function-1.5B.gguf:Q4_K_M

Run and chat with the model

lemonade run user.Arch-Function-1.5B.gguf-Q4_K_M

List all available models

lemonade list

nehcgs commited on Dec 2, 2024

Commit

ce431b2

verified ·

1 Parent(s): 17e1d9d

Upload README.md

Browse files

Files changed (1) hide show

README.md +30 -97

README.md CHANGED Viewed

@@ -2,9 +2,9 @@
 license: other
 license_name: katanemo-research
 license_link: >-
-  https://huggingface.co/katanemolabs/Arch-Function-1.5B/blob/main/LICENSE
 base_model:
-- Qwen/Qwen2.5-1.5B-Instruct
 language:
 - en
 pipeline_tag: text-generation
@@ -54,7 +54,7 @@ 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 (as of Oct 21st, 2024) are shwon below:
 <table>
   <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
@@ -84,27 +84,16 @@ We evaluate Katanemo Arch-Function series on the [Berkeley Function-Calling Lead
     <td>63.41%</td>
     <td>82.93%</td>
   </tr>
-  <tr style="text-align: center; vertical-align: middle;">
-    <td>2</td>
-    <td>Functionary-Medium-v3.1 (FC)</td>
-    <td>62.02%</td>
-    <td>89.52%</td>
-    <td>89.77%</td>
-    <td>73.48%</td>
-    <td>23.50%</td>
-    <td>70.73%</td>
-    <td>73.32%</td>
-  </tr>
-  <tr style="text-align: center; vertical-align: middle;">
-    <td>5</td>
-    <td>ToolACE-8B (FC)</td>
-    <td>60.44%</td>
-    <td>87.06%</td>
-    <td>89.52%</td>
-    <td>74.99%</td>
-    <td>17.38%</td>
-    <td>80.49%</td>
-    <td>85.71%</td>
   </tr>
   <tr style="text-align: center; vertical-align: middle;">
     <td>6</td>
@@ -117,28 +106,6 @@ We evaluate Katanemo Arch-Function series on the [Berkeley Function-Calling Lead
     <td>73.17%</td>
     <td>74.60%</td>
   </tr>
-  <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
-    <td> </td>
-    <td>Arch-Function-7B</td>
-    <td>58.44%</td>
-    <td>85.58%</td>
-    <td>88.14%</td>
-    <td>69.08%</td>
-    <td>20.50%</td>
-    <td>92.68%</td>
-    <td>74.05%</td>
-  </tr>
-  <tr style="text-align: center; vertical-align: middle; ">
-    <td>8</td>
-    <td>xLAM-8x22b-r (FC)</td>
-    <td>57.99%</td>
-    <td>88.15%</td>
-    <td>90.11%</td>
-    <td>71.97%</td>
-    <td>14.50%</td>
-    <td>85.37%</td>
-    <td>67.29%</td>
-  </tr>
   <tr style="text-align: center; vertical-align: middle; ">
     <td>9</td>
     <td>Gemini-1.5-Flash-002 (Prompt)</td>
@@ -150,16 +117,16 @@ We evaluate Katanemo Arch-Function series on the [Berkeley Function-Calling Lead
     <td>85.37%</td>
     <td>78.54%</td>
   </tr>
-  <tr style="text-align: center; vertical-align: middle; ">
-    <td>10</td>
-    <td>Hammer2.0-7b (FC)</td>
     <td>57.69%</td>
-    <td>90.27%</td>
-    <td>89.25%</td>
-    <td>69.79%</td>
-    <td>14.75%</td>
-    <td>95.12%</td>
-    <td>68.46%</td>
   </tr>
   <tr style="text-align: center; vertical-align: middle; ">
     <td>12</td>
@@ -183,50 +150,16 @@ We evaluate Katanemo Arch-Function series on the [Berkeley Function-Calling Lead
     <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-3B</td>
-    <td>56.57%</td>
-    <td>83.62%</td>
-    <td>85.36%</td>
-    <td>66.90%</td>
-    <td>19.50%</td>
-    <td>97.56%</td>
-    <td>70.99%</td>
-  </tr>
-  </tr>
   <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
     <td> </td>
     <td>Arch-Function-1.5B</td>
-    <td>54.52%</td>
-    <td>80.31%</td>
-    <td>82.04%</td>
-    <td>66.19%</td>
-    <td>17.25%</td>
-    <td>97.56%</td>
-    <td>69.95%</td>
-  </tr>
-  <tr style="text-align: center; vertical-align: middle; ">
-    <td>19</td>
-    <td>xLAM-7b-r (FC)</td>
-    <td>54.41%</td>
-    <td>81.40%</td>
-    <td>83.46%</td>
-    <td>67.88%</td>
-    <td>14.50%</td>
-    <td>97.56%</td>
-    <td>64.05%</td>
-  </tr>
-  <tr style="text-align: center; vertical-align: middle; ">
-    <td>20</td>
-    <td>Qwen2.5-7B-Instruct (Prompt)</td>
-    <td>54.27%</td>
-    <td>85.79%</td>
-    <td>88.13%</td>
-    <td>65.97%</td>
-    <td>11.25%</td>
-    <td>92.68%</td>
-    <td>64.95%</td>
   </tr>
   <tr style="text-align: center; vertical-align: middle; ">
     <td>21</td>
@@ -409,4 +342,4 @@ The current temperature in Seattle is 62 degrees in Fahrenheit.
 # License
-Katanemo Arch-Function collection is distributed under the [Katanemo license](https://huggingface.co/katanemolabs/Arch-Function-1.5B/blob/main/LICENSE).

 license: other
 license_name: katanemo-research
 license_link: >-
+  https://huggingface.co/katanemolabs/Arch-Function-1.5B.gguf/blob/main/LICENSE
 base_model:
+- katanemo/Arch-Function-1.5B
 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;">
     <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>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>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>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>
 # License
+Katanemo Arch-Function collection is distributed under the [Katanemo license](https://huggingface.co/katanemolabs/Arch-Function-1.5B.gguf/blob/main/LICENSE).