Granite 4.2 3B vs Ling-3.0-tiny

Granite 4.2 3B

A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.

Ling-3.0-tiny

A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.

Granite 4.2 3B

1. Start it with ollama run granite4.2:3b; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072
2. With vLLM 0.32.0 or later, serve ibm-granite/granite-4.2-3b with --reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice; that release has the parser built in, so the card's --reasoning-parser-plugin ./granite_thinking_parser.py isn't needed
3. With SGLang 0.5.18 or later, --reasoning-parser auto --tool-call-parser auto resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content
4. Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires
5. Pass enable_thinking false for direct answers or low_effort true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server
6. Earlier turns' thinking is stripped by default (truncate_history_thinking), so pass it false to keep reasoning in multi-turn history
7. For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2
8. Verify the files with python -m model_signing verify sigstore and IBM's identity, per IBM's signature guide

Ling-3.0-tiny

1. Start it with llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768, which needs llama.cpp b11772 or a recent release with bailingmoe3
2. With vLLM 0.32.0, vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3, which runs the repository's config code
3. With SGLang 0.5.22, its cookbook uses --reasoning-parser deepseek-r1 --tool-call-parser glm45 for this model, and drop the card's NEXTN flags since tiny has no MTP layer
4. Thinking is on by default, pass chat_template_kwargs with enable_thinking false for short tasks
5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20
6. The template keeps earlier reasoning in the prompt, which grows long agent sessions
7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches