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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 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**
+
+</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 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)
+
+### 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:** 
+
+
+
+#### 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-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)