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README.md

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----
-license: apache-2.0
----
+---
+
+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**: Mean output tokens per second across all concurrent requests over the benchmarking phase.
+- **End-to-End Latency per Query:** Median end-to-end response time for each query from the time the query is sent. 
+- **Output Speed per Query:** Median output tokens per second after the first token is received for each query.
+- **Time to first token (TTFT):** Median
+- **Estimated Peak Memory Usage:** 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:** 
+**Summary of improvements:** Little Lamb 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:** Little Lamb 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)