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

Libraries

How to use MultiverseComputingCAI/LittleLamb with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MultiverseComputingCAI/LittleLamb")
model = AutoModelForCausalLM.from_pretrained("MultiverseComputingCAI/LittleLamb", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MultiverseComputingCAI/LittleLamb with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MultiverseComputingCAI/LittleLamb"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MultiverseComputingCAI/LittleLamb

SGLang

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

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

AntonioTN commited on Aug 7

Commit

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@@ -293,6 +293,15 @@ If you use this model, please cite the base model and this variant:
   url    = {https://huggingface.co/MultiverseComputingCAI/LittleLamb},
   note   = {Model developed based on Qwen/Qwen3-0.6B using CompactifAI technology}
 }
 ```
 **Built by [Multiverse Computing](https://multiversecomputing.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=hf_presence_0626&utm_content=littlelamb_yaml_website)** · [Report an issue](https://huggingface.co/MultiverseComputingCAI/LittleLamb/discussions) · [Discord](https://discord.gg/cGas9uStqp)

   url    = {https://huggingface.co/MultiverseComputingCAI/LittleLamb},
   note   = {Model developed based on Qwen/Qwen3-0.6B using CompactifAI technology}
 }
+@misc{ryskulov2026efficientknowledgedistillationllms,
+      title={Efficient Knowledge Distillation for LLMs: Offline Top-K Logits and a Fused Chunked KL Loss},
+      author={Bakbergen Ryskulov and Iker García-Ferrero and David Montero and David Jansen and Ali Hashemi and Jezabel R. Garcia and Antonio Tiene and Román Orús},
+      year={2026},
+      eprint={2608.03796},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2608.03796},
+}
 ```
 **Built by [Multiverse Computing](https://multiversecomputing.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=hf_presence_0626&utm_content=littlelamb_yaml_website)** · [Report an issue](https://huggingface.co/MultiverseComputingCAI/LittleLamb/discussions) · [Discord](https://discord.gg/cGas9uStqp)