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

Libraries

How to use katanemo/Arch-Agent-3B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("katanemo/Arch-Agent-3B")
model = AutoModelForCausalLM.from_pretrained("katanemo/Arch-Agent-3B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use katanemo/Arch-Agent-3B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/katanemo/Arch-Agent-3B

SGLang

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

Docker Model Runner
How to use katanemo/Arch-Agent-3B with Docker Model Runner:
```
docker model run hf.co/katanemo/Arch-Agent-3B
```

nehcgs commited on Jun 23, 2025

Commit

47c1964

verified ·

1 Parent(s): e3b2d3e

Update README.md

Browse files

Files changed (1) hide show

README.md +0 -6

README.md CHANGED Viewed

@@ -50,7 +50,6 @@ import json
 from typing import Any, Dict, List
 from transformers import AutoModelForCausalLM, AutoTokenizer
-# Specify the desired model name here
 model_name = "katanemo/Arch-Agent-3B"
 model = AutoModelForCausalLM.from_pretrained(
@@ -58,8 +57,6 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-# Please use the recommended prompt for each model.
 TASK_PROMPT = (
     "You are a helpful assistant designed to assist with the user query by making one or more function calls if needed."
     "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\n"
@@ -95,7 +92,6 @@ tools = [
     }
 ]
 # Helper function to create the system prompt for our model
 def format_prompt(tools: List[Dict[str, Any]]):
     tool_text = "\n".join(
@@ -103,7 +99,6 @@ def format_prompt(tools: List[Dict[str, Any]]):
     )
     return TASK_PROMPT.format(tool_text=tool_text)
 system_prompt = format_prompt(tools)
 messages = [
@@ -111,7 +106,6 @@ messages = [
     {"role": "user", "content": "What is the weather in Seattle?"},
 ]
-#### 2.2.3 Run inference
 model_inputs = tokenizer.apply_chat_template(
     messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
 ).to(model.device)

 from typing import Any, Dict, List
 from transformers import AutoModelForCausalLM, AutoTokenizer
 model_name = "katanemo/Arch-Agent-3B"
 model = AutoModelForCausalLM.from_pretrained(
 )
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 TASK_PROMPT = (
     "You are a helpful assistant designed to assist with the user query by making one or more function calls if needed."
     "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\n"
     }
 ]
 # Helper function to create the system prompt for our model
 def format_prompt(tools: List[Dict[str, Any]]):
     tool_text = "\n".join(
     )
     return TASK_PROMPT.format(tool_text=tool_text)
 system_prompt = format_prompt(tools)
 messages = [
     {"role": "user", "content": "What is the weather in Seattle?"},
 ]
 model_inputs = tokenizer.apply_chat_template(
     messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
 ).to(model.device)