--- base_model: - Qwen/Qwen3-0.6B - MultiverseComputing/LittleLamb-0.3B library_name: transformers license: apache-2.0 ---
# 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**
--- ## 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)