Instructions to use arcee-ai/Arcee-Agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arcee-ai/Arcee-Agent with Transformers:

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

pipe = pipeline("text-generation", model="arcee-ai/Arcee-Agent")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arcee-ai/Arcee-Agent")
model = AutoModelForCausalLM.from_pretrained("arcee-ai/Arcee-Agent")
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

vLLM

How to use arcee-ai/Arcee-Agent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arcee-ai/Arcee-Agent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/Arcee-Agent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/arcee-ai/Arcee-Agent

SGLang

How to use arcee-ai/Arcee-Agent 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 "arcee-ai/Arcee-Agent" \
    --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": "arcee-ai/Arcee-Agent",
		"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 "arcee-ai/Arcee-Agent" \
        --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": "arcee-ai/Arcee-Agent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arcee-ai/Arcee-Agent with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Arcee-Agent
```

Crystalcareai commited on Jul 10, 2024

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@@ -133,4 +133,24 @@ While Arcee Agent excels in its specialized areas, users should be aware of its
 - The model's general knowledge and capabilities outside of function calling and tool use may be more limited compared to larger, general-purpose language models.
 - Performance in tasks unrelated to its core functionalities may not match that of models with more diverse training.
 - As with all language models, outputs should be validated and used responsibly, especially in critical applications.
-- The model's knowledge cutoff date may limit its awareness of recent events or technological advancements.

 - The model's general knowledge and capabilities outside of function calling and tool use may be more limited compared to larger, general-purpose language models.
 - Performance in tasks unrelated to its core functionalities may not match that of models with more diverse training.
 - As with all language models, outputs should be validated and used responsibly, especially in critical applications.
+- The model's knowledge cutoff date may limit its awareness of recent events or technological advancements.
+## Usage
+The model was trained to respect many different formats - but the evals were done with this specific tool template:
+```python
+In this environment, you have access to a set of tools you can use to answer the user's question.
+You may call them like this:
+<function_calls>
+  <invoke>
+    <tool_name>$TOOL_NAME</tool_name>
+    <parameters>
+      <$PARAMETER_NAME>$PARAMETER_VALUE</$PARAMETER_NAME>
+      ...
+    </parameters>
+  </invoke>
+</function_calls>
+Here are the tools available:
+<tools>
+```