Instructions to use MultiverseComputingCAI/LittleLamb-ToolCalling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MultiverseComputingCAI/LittleLamb-ToolCalling with Transformers:

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

pipe = pipeline("text-generation", model="MultiverseComputingCAI/LittleLamb-ToolCalling")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MultiverseComputingCAI/LittleLamb-ToolCalling")
model = AutoModelForCausalLM.from_pretrained("MultiverseComputingCAI/LittleLamb-ToolCalling", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MultiverseComputingCAI/LittleLamb-ToolCalling with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/MultiverseComputingCAI/LittleLamb-ToolCalling

SGLang

How to use MultiverseComputingCAI/LittleLamb-ToolCalling 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 "MultiverseComputingCAI/LittleLamb-ToolCalling" \
    --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": "MultiverseComputingCAI/LittleLamb-ToolCalling",
		"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 "MultiverseComputingCAI/LittleLamb-ToolCalling" \
        --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": "MultiverseComputingCAI/LittleLamb-ToolCalling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MultiverseComputingCAI/LittleLamb-ToolCalling with Docker Model Runner:
```
docker model run hf.co/MultiverseComputingCAI/LittleLamb-ToolCalling
```

daniicruzz commited on Jun 2

Commit

c5bbff2

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@@ -46,7 +46,7 @@ license: apache-2.0
 ## Model Overview
-**LittleLamb 0.3B Tool-Calling** is a **tool-calling–optimized variant** of [LittleLamb 0.3B](https://huggingface.co/MultiverseComputingCAI/LittleLamb-ToolCalling) at **290M parameters**, developed based on [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) by **Multiverse Computing**. Built on top of the CompactifAI-compressed LittleLamb base, this variant has been additionally fine-tuned for **function calling, structured outputs, and agentic workflows**. It supports **thinking and non-thinking modes**  while adding native tool-use support in a sub-300M-parameter footprint.
 ---
@@ -335,7 +335,7 @@ Aligned with [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) use cases, wit
 | Based on     | [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)                   |
 | Version      | 2604                                                                        |
 | Release date | 28/04/2026                                                                  |
-| Developed by | Multiverse Computing                                                        |
 | License      | Apache 2.0                                                                  |
 | Contact      | [business@multiversecomputing.com](mailto:business@multiversecomputing.com) |

 ## Model Overview
+**LittleLamb 0.3B Tool-Calling** is a **tool-calling–optimized variant** of [LittleLamb 0.3B](https://huggingface.co/MultiverseComputingCAI/LittleLamb-ToolCalling) at **290M parameters**, developed based on [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) [by **Multiverse Computing**](https://multiversecomputing.com?utm_source=huggingface&utm_medium=referral&utm_campaign=hf_presence_0626&utm_content=littlelamb_tc_overview). Built on top of the CompactifAI-compressed LittleLamb base, this variant has been additionally fine-tuned for **function calling, structured outputs, and agentic workflows**. It supports **thinking and non-thinking modes**  while adding native tool-use support in a sub-300M-parameter footprint.
 ---
 | Based on     | [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)                   |
 | Version      | 2604                                                                        |
 | Release date | 28/04/2026                                                                  |
+| Developed by | [Multiverse Computing](https://multiversecomputing.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=hf_presence_0626&utm_content=littlelamb_tc_modelinfo)                                                        |
 | License      | Apache 2.0                                                                  |
 | Contact      | [business@multiversecomputing.com](mailto:business@multiversecomputing.com) |