---

base_model:

- Qwen/Qwen3-0.6B
- MultiverseComputing/LittleLamb-0.3B
library_name: transformers
license: apache-2.0

---
<div align="center">

# LittleLamb 0.3B

### Powered by CompactifAI

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![HuggingFace](https://img.shields.io/badge/🤗-Model_Hub-yellow.svg)](https://huggingface.co/MultiverseComputingCAI/LittleLamb)
[![Discord](https://img.shields.io/badge/Discord-Community-5865F2?logo=discord&logoColor=white)](https://discord.gg/cGas9uStqp)

**Tiny Model** · **50% Compressed** · **Thinking & Non-Thinking Modes**

</div>

---

## Table of Contents

- [Highlights](#highlights)
- [Model Overview](#model-overview)
- [Key Characteristics](#key-characteristics)
- [Quick Start](#quick-start)
- [What's New in LittleLamb 0.3B](#whats-new-in-littlelamb-03b)
- [Dual-Mode Inference (Thinking / Non-Thinking)](#dual-mode-inference-thinking--non-thinking)
- [Training & Fine-Tuning](#training--fine-tuning)
- [Architecture](#architecture)
- [Evaluation & Benchmarks](#evaluation--benchmarks)
- [Languages](#languages)
- [Intended Use](#intended-use)
- [Safety & Limitations](#safety--limitations)
- [Model Information](#model-information)
- [Citation](#citation)

---

## Model Overview

**LittleLamb 0.3B** is a **general-purpose bilingual model** at **290M parameters**, a similar size class to **270M** models such as **gemma3-270m-it** and **functiongemma-270m-it**—developed based on [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B), by **Multiverse Computing**. The original Qwen3-0.6B is an open-weight, instruction-tuned model with thinking and non-thinking capabilities and multilingual coverage. LittleLamb 0.3B is compressed at a **50% compression rate** using **CompactifAI**, Multiverse Computing's proprietary technology. The model supports **English and Spanish** and retains Qwen3's dual thinking/non-thinking modes.

---

## Key Characteristics


| Characteristic   | Description                                                                                                      |
| ---------------- | ---------------------------------------------------------------------------------------------------------------- |
| Base model       | [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) (0.6B params, 0.44B non-embedding; open-weight, Apache 2.0) |
| **Parameters**   | 290M total parameters after CompactifAI compression (50% compression rate from base 0.6B)                       |
| **Architecture** | Decoder-only Transformer (Qwen3 family)                                                                          |
| **Compression**  | CompactifAI (proprietary)                                                                                        |
| **Languages**    | English and Spanish; inherits broader multilingual tokenizer coverage from Qwen3                                 |
| **Modes**        | Thinking (`enable_thinking=True`) and non-thinking (`enable_thinking=False`) via chat template                   |


---

## Quick Start

This model can be loaded with the **Transformers** library. Requires `transformers>=4.51.0` for Qwen3 architecture support.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "MultiverseComputingCAI/LittleLamb"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [{"role": "user", "content": "Hello!"}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
output_ids = model.generate(**inputs, max_new_tokens=256)[0]
response = tokenizer.decode(
    output_ids[len(inputs.input_ids[0]) :], skip_special_tokens=True
)
print(response)
```

For OpenAI-compatible serving, use a stack that supports Qwen3 reasoning (e.g. recent **vLLM** or **SGLang** with Qwen3 parsers); see the [Qwen3-0.6B model card](https://huggingface.co/Qwen/Qwen3-0.6B) for deployment examples.

---

## What's New in LittleLamb 0.3B

### Summary

- **Ultra-compact general-purpose model** at 290M parameters, suitable for edge and on-device deployment.
- **Developed based on [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)** with **CompactifAI** compression (~50% parameter reduction vs. base non-embedding count).
- **Bilingual focus:** English and Spanish for supported use cases.

---

## Dual-Mode Inference (Thinking / Non-Thinking)

LittleLamb 0.3B inherits Qwen3's dual-mode capability, supporting seamless switching between **thinking mode** (for complex reasoning) and **non-thinking mode** (for efficient general-purpose dialogue).

The model generates internal reasoning in Qwen3’s thinking format (see the Qwen3 chat template) before producing the final response. Use this for tasks requiring multi-step reasoning, math, or code generation.

Set `enable_thinking=False` for lower-latency dialogue without explicit chain-of-thought in the template. Follow the **sampling parameters** recommended in the [Qwen3-0.6B model card](https://huggingface.co/Qwen/Qwen3-0.6B) for each mode.

---

## Training & Fine-Tuning

### Base Model: Qwen3-0.6B

The base model [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) is a causal language model from the Qwen3 family, supporting thinking/non-thinking. See the [Qwen3 technical report](https://arxiv.org/abs/2505.09388) for details.

---

## Architecture

### Model Specifications


| Field            | Value                                                                   |
| ---------------- | ----------------------------------------------------------------------- |
| Base model       | [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) (0.6B params) |
| Total parameters |290M dense                                                             |


---

## Evaluation & Benchmarks

### Evaluation Methodology

Benchmark scores were obtained with the following setups. Methodology varies by benchmark family.

For **LittleLamb 0.3B** and **Qwen3-0.6B (base)**, benchmark runs are reported under both **thinking** and **non-thinking** chat modes using the sampling settings recommended in the [Qwen3-0.6B model card](https://huggingface.co/Qwen/Qwen3-0.6B).

#### MMLU-Pro, GPQA Diamond, HLE (Humanity's Last Exam)

- **Evaluation framework**: [Nemo-skills](https://github.com/NVIDIA/NeMo-Skills)
- **Inference library**: vLLM 0.18.0
- **Thinking mode** (`enable_thinking=True`, per Qwen3-0.6B instruct): temperature = 0.6, top_p = 0.95, top_k = 20, min_p = 0
- **Non-thinking mode** (`enable_thinking=False`, per Qwen3-0.6B instruct): temperature = 0.7, top_p = 0.8, top_k = 20, min_p = 0

### Quantitative Results (Reported & Planned)

Reported numbers use the methodology described above.

#### Thinking mode


| Benchmark    | gemma3-270m-it | Qwen3-0.6B (think) | LittleLamb-0.3B (think) |
| ------------ | -------------- | ------------------ | ----------------------- |
| HLE          | 4.00           | 5.65               | 6.12                    |
| GPQA Diamond | 21.21          | 29.59              | 28.18                   |
| MMLU-Pro     | 6.23           | 38.27              | 31.21                   |


#### Non-thinking mode


| Benchmark    | gemma3-270m-it | Qwen3-0.6B (no think) | LittleLamb-0.3B (no think) |
| ------------ | -------------- | --------------------- | -------------------------- |
| HLE          | 4.00           | 4.54                  | 5.37                       |
| GPQA Diamond | 21.21          | 27.77                 | 24.04                      |
| MMLU-Pro     | 6.23           | 25.72                 | 25.11                      |


![Intelligence Thinking](assets/littlelamb-intelligence-thinking-family.png)
![Intelligence No-Thinking](assets/littlelamb-intelligence-nothinking-family.png)

### Quantitative Results (Inference Performance)

#### Metrics reported
- **System Output Throughput (higher is better)**: Mean output tokens per second across all concurrent requests over the benchmarking phase.
- **End-to-End Latency per Query (lower is better):** Median end-to-end response time for each query from the time the query is sent. 
- **Output Speed per Query (higher is better):** Median output tokens per second after the first token is received for each query.
- **Time to first token (TTFT) (lower is better):** Median
- **Estimated Peak Memory Usage (lower is better):** KV cache utilization is monitored during the phase and we estimate memory usage as follows: $model\_ weights_{gb} + kv\_ cache_{usage\_pct} × (nvml\_used_{gb} − model\_ weights_{gb})$
- **Model weights (lower is better):**

**Summary of improvements:** LittleLamb shows a slight improvement in performance with respect to the original Qwen Model. This is expected as for such small models, VRAM usage is dominated by KV cache and not model weights.

#### Performance evaluation conditions

Our performance evaluation follows the spirit of [Artificial Analysis](https://artificialanalysis.ai/methodology/system-load-test).

- **Inference library**: vLLM 0.18.0
- **Monitoring libraries**: GuideLLM 0.6.0, nvidia-ml-py 13.590.48
- **Hardware**: 1× NVIDIA L4 GPU
- **Conditions**: concurrency=16
- **Phase duration**: Each phase lasts 3 minutes (excluding ramp-up and cool-down periods).
- **Workload shape**: 1,000 input tokens and 1,000 output tokens per query.
- **Streaming**: Benchmarking is conducted with streaming enabled.


**Summary of improvements:** LittleLamb shows a slight improvement in performance with respect to the original Qwen Model. This is expected as for such small models, VRAM usage is dominated by KV cache and not model weights.

![Performance](assets/littlelamb-performance-family.png)

---

## Languages

- **Primary languages**: English and Spanish (supported for product use cases).

---

## Intended Use

### Recommended Use Cases

Aligned with [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) use cases, with the benefit of a smaller footprint suitable for edge and on-device deployment:

- **On-device and edge inference** where memory and compute are constrained
- **Reasoning tasks** with configurable thinking/non-thinking modes
- **Bilingual applications** (English and Spanish)
- **Chatbots and virtual assistants** in resource-constrained environments
- **General knowledge, math, and science** question answering

### Out-of-Scope Uses

- Harmful, illegal, or deceptive content generation
- Impersonation of real individuals without consent
- High-risk decision-making without human oversight
- Surveillance or tracking of individuals
- Any use that violates applicable laws or regulations

---

## Safety & Limitations

### Known Limitations

- **Model scale:** At ~0.3B parameters, this is an ultra-compact model. Several frontier-scale benchmarks (GDPval-AA, Terminal-Bench Hard, AA-LCR, CritPt) produce no discriminative signal at this model size, as the base Qwen3-0.6B itself scores near zero on them.
- **Thinking mode:** Performance differs substantially between thinking and non-thinking modes across benchmarks. Users should evaluate both modes for their specific use case.

### Recommendations

- Use human oversight for critical applications
- Perform task-specific evaluation prior to deployment
- Test both thinking and non-thinking modes for your use case

---

## Model Information


| Field        | Value                                                                       |
| ------------ | --------------------------------------------------------------------------- |
| Model name   | LittleLamb                                                                  |
| 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) |


---

## Citation

If you use this model, please cite the base model and this variant:

```bibtex
@misc{qwen3technicalreport,
  title         = {Qwen3 Technical Report},
  author        = {Qwen Team},
  year          = {2025},
  eprint        = {2505.09388},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2505.09388}
}
@misc{littlelamb,
  title  = {LittleLamb: Compressed Qwen3-0.6B via CompactifAI},
  author = {Multiverse Computing},
  year   = {2026},
  url    = {https://huggingface.co/MultiverseComputingCAI/LittleLamb},
  note   = {Model developed based on Qwen/Qwen3-0.6B using CompactifAI technology}
}
```

**Built by [Multiverse Computing](https://www.multiversecomputing.com)** · [Report an issue](https://huggingface.co/MultiverseComputingCAI/LittleLamb/discussions) · [Discord](https://discord.gg/cGas9uStqp)