Apodex-1.1-mini vs Qwen3.5-9B

Apodex-1.1-mini

An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.

Qwen3.5-9B

A small dense vision-language cousin with trained tool calling, switchable thinking and a 262,144-token context, for local agent subtasks. Ollama's 4-bit build needs about 8 GB and the BF16 files about 24 GB, in every major runtime. The main risk is neglect, with no file changes since March, no replies from Qwen to reports and no safety results.

Apodex-1.1-mini

1. Serve it with vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3, or the same parsers in SGLang, so tool calls and reasoning come back parsed
2. For llama.cpp, llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek, and the card passes the repository's chat_template.jinja with --chat-template-file
3. Pass tools through the API's tools parameter rather than in the system prompt, as the card asks
4. The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768
5. Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block
6. The template keeps all earlier reasoning in the context unless preserve_thinking is false, which grows long agent sessions
7. Thinking is on by default, send chat_template_kwargs with enable_thinking false for short tasks, a switch the card doesn't mention
8. The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to available_tools[name](**args) before running
9. Validate JSON output yourself, since structured output isn't documented

Qwen3.5-9B

1. Smallest start is ollama run qwen3.5:9b (6.6 GB, q4_k_m)
2. On vLLM run vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, or tool calls come back as text
3. Tool calls use the XML style <tool_call><function=name><parameter=x>, not Hermes JSON, so use the qwen3_coder parser and not hermes
4. Thinking is on by default; pass chat_template_kwargs with enable_thinking false for short tasks, since /nothink isn't supported
5. There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without
6. Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens
7. Validate JSON output yourself or use the runtime's guided decoding