Instructions to use Salesforce/xLAM-2-32b-fc-r with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Salesforce/xLAM-2-32b-fc-r with Transformers:

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

pipe = pipeline("text-generation", model="Salesforce/xLAM-2-32b-fc-r")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/xLAM-2-32b-fc-r")
model = AutoModelForMultimodalLM.from_pretrained("Salesforce/xLAM-2-32b-fc-r")
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 Salesforce/xLAM-2-32b-fc-r with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Salesforce/xLAM-2-32b-fc-r"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Salesforce/xLAM-2-32b-fc-r",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Salesforce/xLAM-2-32b-fc-r

SGLang

How to use Salesforce/xLAM-2-32b-fc-r 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 "Salesforce/xLAM-2-32b-fc-r" \
    --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": "Salesforce/xLAM-2-32b-fc-r",
		"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 "Salesforce/xLAM-2-32b-fc-r" \
        --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": "Salesforce/xLAM-2-32b-fc-r",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Salesforce/xLAM-2-32b-fc-r with Docker Model Runner:
```
docker model run hf.co/Salesforce/xLAM-2-32b-fc-r
```

Add link to paper and license metadata

by nielsr HF Staff - opened Apr 9, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-3

Files changed (1) hide show

README.md +4 -3

README.md CHANGED Viewed

@@ -1,9 +1,10 @@
 ---
-license: cc-by-nc-4.0
 datasets:
 - Salesforce/xlam-function-calling-60k
 language:
 - en
 pipeline_tag: text-generation
 tags:
 - function-calling
@@ -13,9 +14,9 @@ tags:
 - qwen
 - pytorch
 - LLaMA-factory
-library_name: transformers
 ---
 <p align="center">
 <img width="500px" alt="xLAM" src="https://huggingface.co/datasets/jianguozhang/logos/resolve/main/xlam-no-background.png">
 </p>
@@ -216,4 +217,4 @@ Additionally, please check our other related works regarding xLAM and consider c
   year={2024}
 }
 ```

 ---
 datasets:
 - Salesforce/xlam-function-calling-60k
 language:
 - en
+library_name: transformers
+license: cc-by-nc-4.0
 pipeline_tag: text-generation
 tags:
 - function-calling
 - qwen
 - pytorch
 - LLaMA-factory
 ---
+```markdown
 <p align="center">
 <img width="500px" alt="xLAM" src="https://huggingface.co/datasets/jianguozhang/logos/resolve/main/xlam-no-background.png">
 </p>
   year={2024}
 }
 ```
+```