--- base_model: - Qwen/Qwen3-0.6B - MultiverseComputing/LittleLamb-0.3B library_name: transformers license: apache-2.0 ---
# LittleLamb 0.3B Tool-Calling ### 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-ToolCalling) [![Discord](https://img.shields.io/badge/Discord-Community-5865F2?logo=discord&logoColor=white)](https://discord.gg/cGas9uStqp) **Tiny Model** · **50% Compressed** · **Native Tool Calling** · **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 Tool-Calling](#whats-new-in-littlelamb-03b-tool-calling) - [Tool Calling](#tool-calling) - [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 Tool-Calling** is a **tool-calling–optimized variant** of [LittleLamb 0.3B](https://huggingface.co/MultiverseComputingCAI/LittleLamb-ToolCalling) at **290M parameters**, developed based on [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) by **Multiverse Computing**. Built on top of the CompactifAI-compressed LittleLamb base, this variant has been additionally fine-tuned for **function calling, structured outputs, and agentic workflows**. It supports **thinking and non-thinking modes** while adding native tool-use support in a sub-300M-parameter footprint. --- ## 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) | | **Tool calling** | Native support for function calling with defined schemas and structured outputs | | **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. Spanish is yet to be tested for tool-calling capabilities. | | **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-ToolCalling" 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 and tool calling (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 Tool-Calling ### Summary - **Tool-calling–optimized** variant of LittleLamb 0.3B, fine-tuned for function calling and structured outputs. - **Ultra-compact** at 290M parameters, suitable for edge and on-device deployment with agentic capabilities. - **Developed based on [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)** with **CompactifAI** compression (~50% parameter reduction vs. base non-embedding count). --- ## Tool Calling LittleLamb 0.3B Tool-Calling supports **native tool use** and is designed for: - **Function calling** with defined schemas - **Structured outputs** - **Agentic operations** (e.g. browser tasks, code execution where supported) The model can detect when to invoke tools, emit structured JSON tool calls, and consume tool outputs to continue generation. Tool-calling behavior follows Qwen3-style schemas. ### Example Tool Call ```json { "name": "get_weather", "arguments": { "city": "Paris", "date": "2026-02-10" } } ``` --- ## Dual-Mode Inference (Thinking / Non-Thinking) LittleLamb 0.3B Tool-Calling 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 modes. See the [Qwen3 technical report](https://arxiv.org/abs/2505.09388) for details. ### CompactifAI Compression & Tool-Calling Fine-Tuning - **Compression:** CompactifAI was applied to produce a smaller, efficient model (~0.3B parameters) while aiming to preserve reasoning capabilities. - **Tool-calling fine-tuning:** This variant includes additional fine-tuning for function calling and structured outputs on top of the compressed LittleLamb base. --- ## 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 Tool-Calling** 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, IFBench - **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 #### BFCL v4, τ²-Bench - **Evaluation framework**: [EvalScope](https://github.com/EvalScope/EvalScope) - **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 - Results of `functiongemma-270m-it` for BFCL v4 were extracted from [Google's model card](https://huggingface.co/google/functiongemma-270m-it) (09/04/2026) ### Quantitative Results Reported numbers use the methodology described above. #### Thinking mode | Benchmark | functiongemma-270m-it | Qwen3-0.6B (think) | LittleLamb-TC 0.3B (think) | | --------------------------- | --------------------- | ------------------ | -------------------------- | | IFBench | 12.00 | 23.88 | 20.00 | | GPQA Diamond | 2.53 | 29.59 | 27.47 | | MMLU-Pro | 0.42 | 38.27 | 28.74 | | τ²-Bench | 5.05 | 19.59 | 18.70 | | BFCL Simple | 61.60 | 72.73 | 72.36 | | BFCL Multiple | 63.50 | 85.00 | 89.50 | | BFCL Parallel | 39.00 | 70.00 | 70.00 | | BFCL Parallel Multiple | 29.50 | 71.50 | 68.00 | | BFCL Live Simple | 36.20 | 63.18 | 64.34 | | BFCL Live Multiple | 25.70 | 56.41 | 60.78 | | BFCL Live Parallel | 22.90 | 50.00 | 62.50 | | BFCL Live Parallel Multiple | 20.80 | 50.00 | 45.83 | | BFCL Relevance | 61.10 | 75.00 | 75.00 | | BFCL Irrelevance | 73.70 | 84.58 | 77.92 | | **BFCL v4** | 27.03 | 54.08 | 51.55 | #### Non-thinking mode | Benchmark | functiongemma-270m-it | Qwen3-0.6B (no think) | LittleLamb-TC 0.3B (no think) | | --------------------------- | --------------------- | --------------------- | ----------------------------- | | IFBench | 12.00 | 23.80 | 21.00 | | GPQA Diamond | 2.53 | 27.77 | 27.37 | | MMLU-Pro | 0.42 | 25.72 | 23.71 | | τ²-Bench | 5.05 | 15.50 | 26.67 | | BFCL Simple | 61.60 | 12.73 | 70.55 | | BFCL Multiple | 63.50 | 20.00 | 80.50 | | BFCL Parallel | 39.00 | 18.00 | 71.50 | | BFCL Parallel Multiple | 29.50 | 30.50 | 70.50 | | BFCL Live Simple | 36.20 | 4.65 | 62.02 | | BFCL Live Multiple | 25.70 | 11.02 | 50.43 | | BFCL Live Parallel | 22.90 | 0.00 | 43.75 | | BFCL Live Parallel Multiple | 20.80 | 12.50 | 29.17 | | BFCL Relevance | 61.10 | 12.50 | 75.00 | | BFCL Irrelevance | 73.70 | 97.50 | 87.50 | | **BFCL v4** | 27.03 | 29.17 | 50.51 | ![Intelligence Thinking](assets/littlelamb-tc-intelligence-family.png) BFCL V4 is the de facto industry standard for evaluating function-calling (tool-use) capability. It tests whether models can correctly generate structured function calls in response to user queries, across simple single-call scenarios, parallel calls, multi-turn conversations, and complex agentic workflows. ### 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):** #### 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-tc-performance-family.png) --- ## Languages - **Primary languages**: English. Spanish is yet to be tested for tool-calling capabilities. --- ## Intended Use ### Recommended Use Cases Aligned with [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) use cases, with the added benefit of tool-calling capabilities in a smaller footprint suitable for edge and on-device deployment: - **Function calling and agentic workflows** in resource-constrained environments - **On-device and edge inference** where memory and compute are constrained - **Structured output generation** (JSON, schemas) - **Reasoning tasks** with configurable thinking/non-thinking modes - **Chatbots and virtual assistants** with tool integration ### 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. - **Tool calling:** While fine-tuned for tool use, accuracy and reliability of tool calls should be validated for production use cases given the model's compact size. ### 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 - Validate tool-call outputs before executing them in production --- ## Model Information | Field | Value | | ------------ | --------------------------------------------------------------------------- | | Model name | LittleLamb Tool-Calling | | 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{littlelambtc, title = {LittleLamb Tool-Calling: Compressed Qwen3-0.6B with Tool-Use via CompactifAI}, author = {Multiverse Computing}, year = {2026}, url = {https://huggingface.co/MultiverseComputingCAI/LittleLamb-ToolCalling}, note = {Model developed based on Qwen/Qwen3-0.6B using CompactifAI technology, fine-tuned for tool calling} } ``` **Built by [Multiverse Computing](https://www.multiversecomputing.com)** · [Report an issue](https://huggingface.co/MultiverseComputingCAI/LittleLamb-ToolCalling/discussions) · [Discord](https://discord.gg/cGas9uStqp)