297 lines
13 KiB
Markdown
297 lines
13 KiB
Markdown
---
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base_model:
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- Qwen/Qwen3-0.6B
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- MultiverseComputing/LittleLamb-0.3B
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library_name: transformers
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license: apache-2.0
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---
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<div align="center">
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# LittleLamb 0.3B
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### Powered by CompactifAI
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[](https://opensource.org/licenses/Apache-2.0)
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[](https://huggingface.co/MultiverseComputingCAI/LittleLamb)
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[](https://discord.gg/cGas9uStqp)
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**Tiny Model** · **50% Compressed** · **Thinking & Non-Thinking Modes**
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</div>
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---
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## Table of Contents
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- [Highlights](#highlights)
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- [Model Overview](#model-overview)
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- [Key Characteristics](#key-characteristics)
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- [Quick Start](#quick-start)
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- [What's New in LittleLamb 0.3B](#whats-new-in-littlelamb-03b)
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- [Dual-Mode Inference (Thinking / Non-Thinking)](#dual-mode-inference-thinking--non-thinking)
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- [Training & Fine-Tuning](#training--fine-tuning)
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- [Architecture](#architecture)
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- [Evaluation & Benchmarks](#evaluation--benchmarks)
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- [Languages](#languages)
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- [Intended Use](#intended-use)
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- [Safety & Limitations](#safety--limitations)
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- [Model Information](#model-information)
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- [Citation](#citation)
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---
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## Model Overview
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**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.
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---
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## Key Characteristics
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| Characteristic | Description |
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| ---------------- | ---------------------------------------------------------------------------------------------------------------- |
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| Base model | [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) (0.6B params, 0.44B non-embedding; open-weight, Apache 2.0) |
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| **Parameters** | 290M total parameters after CompactifAI compression (50% compression rate from base 0.6B) |
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| **Architecture** | Decoder-only Transformer (Qwen3 family) |
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| **Compression** | CompactifAI (proprietary) |
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| **Languages** | English and Spanish; inherits broader multilingual tokenizer coverage from Qwen3 |
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| **Modes** | Thinking (`enable_thinking=True`) and non-thinking (`enable_thinking=False`) via chat template |
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---
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## Quick Start
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This model can be loaded with the **Transformers** library. Requires `transformers>=4.51.0` for Qwen3 architecture support.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "MultiverseComputingCAI/LittleLamb"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype="auto",
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device_map="auto",
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)
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messages = [{"role": "user", "content": "Hello!"}]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=True,
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)
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inputs = tokenizer([text], return_tensors="pt").to(model.device)
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output_ids = model.generate(**inputs, max_new_tokens=256)[0]
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response = tokenizer.decode(
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output_ids[len(inputs.input_ids[0]) :], skip_special_tokens=True
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)
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print(response)
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```
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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.
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---
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## What's New in LittleLamb 0.3B
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### Summary
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- **Ultra-compact general-purpose model** at 290M parameters, suitable for edge and on-device deployment.
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- **Developed based on [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)** with **CompactifAI** compression (~50% parameter reduction vs. base non-embedding count).
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- **Bilingual focus:** English and Spanish for supported use cases.
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---
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## Dual-Mode Inference (Thinking / Non-Thinking)
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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).
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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.
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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.
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---
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## Training & Fine-Tuning
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### Base Model: Qwen3-0.6B
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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.
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---
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## Architecture
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### Model Specifications
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| Field | Value |
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| ---------------- | ----------------------------------------------------------------------- |
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| Base model | [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) (0.6B params) |
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| Total parameters |290M dense |
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---
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## Evaluation & Benchmarks
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### Evaluation Methodology
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Benchmark scores were obtained with the following setups. Methodology varies by benchmark family.
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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).
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#### MMLU-Pro, GPQA Diamond, HLE (Humanity's Last Exam)
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- **Evaluation framework**: [Nemo-skills](https://github.com/NVIDIA/NeMo-Skills)
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- **Inference library**: vLLM 0.18.0
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- **Thinking mode** (`enable_thinking=True`, per Qwen3-0.6B instruct): temperature = 0.6, top_p = 0.95, top_k = 20, min_p = 0
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- **Non-thinking mode** (`enable_thinking=False`, per Qwen3-0.6B instruct): temperature = 0.7, top_p = 0.8, top_k = 20, min_p = 0
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### Quantitative Results (Reported & Planned)
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Reported numbers use the methodology described above.
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#### Thinking mode
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| Benchmark | gemma3-270m-it | Qwen3-0.6B (think) | LittleLamb-0.3B (think) |
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| ------------ | -------------- | ------------------ | ----------------------- |
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| HLE | 4.00 | 5.65 | 6.12 |
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| GPQA Diamond | 21.21 | 29.59 | 28.18 |
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| MMLU-Pro | 6.23 | 38.27 | 31.21 |
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#### Non-thinking mode
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| Benchmark | gemma3-270m-it | Qwen3-0.6B (no think) | LittleLamb-0.3B (no think) |
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| ------------ | -------------- | --------------------- | -------------------------- |
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| HLE | 4.00 | 4.54 | 5.37 |
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| GPQA Diamond | 21.21 | 27.77 | 24.04 |
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| MMLU-Pro | 6.23 | 25.72 | 25.11 |
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### Quantitative Results (Inference Performance)
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#### Metrics reported
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- **System Output Throughput (higher is better)**: Mean output tokens per second across all concurrent requests over the benchmarking phase.
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- **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.
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- **Output Speed per Query (higher is better):** Median output tokens per second after the first token is received for each query.
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- **Time to first token (TTFT) (lower is better):** Median
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- **Estimated Peak Memory Usage (lower is better):** KV cache utilization is monitored during the phase and we estimate memory usage as follows: $model\_ weights_{gb} + kv\_ cache_{usage\_pct} × (nvml\_used_{gb} − model\_ weights_{gb})$
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- **Model weights (lower is better):**
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**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.
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#### Performance evaluation conditions
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Our performance evaluation follows the spirit of [Artificial Analysis](https://artificialanalysis.ai/methodology/system-load-test).
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- **Inference library**: vLLM 0.18.0
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- **Monitoring libraries**: GuideLLM 0.6.0, nvidia-ml-py 13.590.48
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- **Hardware**: 1× NVIDIA L4 GPU
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- **Conditions**: concurrency=16
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- **Phase duration**: Each phase lasts 3 minutes (excluding ramp-up and cool-down periods).
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- **Workload shape**: 1,000 input tokens and 1,000 output tokens per query.
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- **Streaming**: Benchmarking is conducted with streaming enabled.
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**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.
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---
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## Languages
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- **Primary languages**: English and Spanish (supported for product use cases).
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---
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## Intended Use
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### Recommended Use Cases
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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:
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- **On-device and edge inference** where memory and compute are constrained
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- **Reasoning tasks** with configurable thinking/non-thinking modes
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- **Bilingual applications** (English and Spanish)
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- **Chatbots and virtual assistants** in resource-constrained environments
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- **General knowledge, math, and science** question answering
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### Out-of-Scope Uses
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- Harmful, illegal, or deceptive content generation
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- Impersonation of real individuals without consent
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- High-risk decision-making without human oversight
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- Surveillance or tracking of individuals
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- Any use that violates applicable laws or regulations
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---
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## Safety & Limitations
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### Known Limitations
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- **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.
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- **Thinking mode:** Performance differs substantially between thinking and non-thinking modes across benchmarks. Users should evaluate both modes for their specific use case.
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### Recommendations
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- Use human oversight for critical applications
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- Perform task-specific evaluation prior to deployment
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- Test both thinking and non-thinking modes for your use case
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---
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## Model Information
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| Field | Value |
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| ------------ | --------------------------------------------------------------------------- |
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| Model name | LittleLamb |
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| Based on | [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) |
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| Version | 2604 |
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| Release date | 28/04/2026 |
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| Developed by | Multiverse Computing |
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| License | Apache 2.0 |
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| Contact | [business@multiversecomputing.com](mailto:business@multiversecomputing.com) |
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---
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## Citation
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If you use this model, please cite the base model and this variant:
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```bibtex
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@misc{qwen3technicalreport,
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title = {Qwen3 Technical Report},
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author = {Qwen Team},
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year = {2025},
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eprint = {2505.09388},
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archivePrefix = {arXiv},
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primaryClass = {cs.CL},
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url = {https://arxiv.org/abs/2505.09388}
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}
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@misc{littlelamb,
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title = {LittleLamb: Compressed Qwen3-0.6B via CompactifAI},
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author = {Multiverse Computing},
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year = {2026},
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url = {https://huggingface.co/MultiverseComputingCAI/LittleLamb},
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note = {Model developed based on Qwen/Qwen3-0.6B using CompactifAI technology}
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}
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```
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**Built by [Multiverse Computing](https://www.multiversecomputing.com)** · [Report an issue](https://huggingface.co/MultiverseComputingCAI/LittleLamb/discussions) · [Discord](https://discord.gg/cGas9uStqp) |