Choosing the 'best' small open-source model largely depends on your specific constraints, particularly regarding hardware and the intended use case. I've broken down the current top contenders in the small model (<8B parameters) category, considering factors like per-parameter efficiency and practical deployability.

1. How “small” is defined

The "best small model" is typically evaluated on two primary criteria:

1. ·Per-parameter efficiency: The quality output achieved per million parameters.
2. ·Practical deployability: This encompasses memory footprint, inference latency, and ease of quantization for a given device.

Here’s a breakdown of common parameter count categories and their typical storage:

2. Top-tier Small Open-Source LLMs for Various Use Cases

For most general use cases, especially when balancing quality and resource constraints, here are my top recommendations:

Detailed breakdown:

• ·

  For pure quality as a 'small' (≤2B) model: Phi-2 stands out. Despite its small size, it demonstrates impressive zero-shot reasoning and coding capabilities, often outperforming much larger models like LLaMA-2-7B on benchmarks like MMLU (54.2% vs 53.0%) and HumanEval (45% vs 40%). Its Apache-2.0 license is commercial-friendly, and it runs efficiently on a CPU (around 20 tok/s with 32GB RAM) after 4-bit quantization, taking up about 1 GB on disk.

• ·

  For general-purpose chat quality in the mid-size (4-8B) range: Mistral-7B-Instruct-v0.2 is a top performer. It consistently leads the Open-LLM Leaderboard for the 7B class, thanks to its efficient architecture incorporating grouped-query attention (GQA) and Mixtral-style gating. A 4-bit quantized version is about 3 GB on disk and can be run well on GPUs with ≥8 GB VRAM (e.g., an RTX 3060).

• ·

  For multilingual applications: Qwen-1.5-4B-Chat is an excellent alternative. While its English reasoning scores might be slightly lower than Mistral's, it offers superior performance for non-English corpora and boasts strong multilingual coverage across 20+ languages. It's also very GPU-friendly, running on a single RTX 3060.

• ·

  For extreme resource constraints (e.g., edge devices or <1GB RAM): TinyLlama-1.1-1.1B-Chat is the go-to. It's incredibly lightweight, making it suitable for simple assistants or embeddings even on devices like a Raspberry Pi 4 with swap.

• ·

  For a conversational, safe-tuned 2B model: Gemma-2-2B-Instruct is a great choice. It's built with safety in mind and provides a more conversational tone than Phi-2, although it scores slightly behind Phi-2 on pure reasoning benchmarks like MMLU.

3. Choosing the Right Model for Your Constraints

4. Getting a Model Up & Running – Quick-Start Checklist

Here's a generic pipeline for deployment, suitable for most listed models:

bash

1️⃣ Install core inference engine (choose one)

llama.cpp (CPU/GPU via GGML) for local inference

git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp make -j$(nproc)

or vLLM (GPU-only, faster batching) for server-side inference

pip install vllm

2️⃣ Download the model (example: phi-2) from Hugging Face

Using huggingface-cli (HF token may be required for gated repos)

huggingface-cli download microsoft/phi-2
--local-dir ./phi2
--revision main
--local-dir-use-symlinks=False

3️⃣ Quantize (if you want the 4-bit version) for reduced memory footprint

llama.cpp quantizer (works on GPU/CPU)

./quantize ./phi2/pytorch_model.bin ./phi2/phi2-q4_0.ggml.bin q4_0

4️⃣ Run a simple REPL or API server

llama.cpp:

./main -m ./phi2/phi2-q4_0.ggml.bin -c 2048 -ngl 32 # -ngl = number of layers on GPU (if available)

vLLM:

python -m vllm.entrypoints.openai.api_server --model ./phi2 --dtype auto --quantization gptq

5️⃣ Test the API (if using vLLM in server mode)

curl http://localhost:8000/v1/completions
-H "Content-Type: application/json"
-d '{"model":"phi2","prompt":"Explain quantum tunneling in two sentences.","max_tokens":64}'

Key Tips for Deployment:

• ·Always check the license files within the model repository (e.g., README/LICENSE) as some have specific export or usage restrictions.
• ·For llama.cpp, consider --no-cache for long contexts when RAM is limited and --rope-scaling factor=2 if you need context windows beyond 2k tokens without retraining.
• ·When using vLLM for concurrent users, a batch size of ≥8 can significantly improve GPU utilization.
• ·Small models can still hallucinate or produce unsafe content; integrate a lightweight post-generation filter (e.g., a profanity checker or a small safety model) if exposing to users.

5. Future-proofing: What’s on the horizon?

The landscape of small open-source models is rapidly evolving. Keep an eye out for:

• ·Mistral-8x7B-MoE: Expected to offer quality comparable to 30B models at an inference cost similar to a 7B model (though requiring more 2x 32GB VRAM).
• ·Phi-3-mini (3.8B): Building on the highly efficient Phi-2 architecture with enhanced coding abilities.
• ·Gemma-2-7B-Instruct: A larger version of Gemma-2 focusing on safety-tuned instruction following.

For long-term service deployment, building your infrastructure around platforms like Ollama or vLLM will offer flexibility, as they readily support hot-swapping models and will be well-equipped to ingest future releases.

6. Bottom Line

My recommendation boils down to:

• ·If you need the best quality for a purely 'small' (≤ 2B) model: Go with Phi-2. It provides excellent reasoning and coding capabilities and can run efficiently on a CPU.
• ·If you need the best balance of quality and small footprint (4-8B) for chat: Mistral-7B-Instruct-v0.2 is the leader, offering strong performance on benchmarks and good speed on consumer GPUs.
• ·If multilingual support is paramount: Qwen-1.5-4B-Chat excels in this domain.
• ·If hardware constraints are extremely tight (e.g., <1GB RAM): TinyLlama-1.1-1.1B-Chat is your best bet.

Always ensure the chosen model's license and hardware requirements align with your project's needs.