--- title: Opera canonical_url: "https://www.modelscope.cn/models/beyoru-ai/Opera" md_url: "https://www.modelscope.cn/models/beyoru-ai/Opera.md" repository: beyoru-ai/Opera last_updated: 2026-07-30 license: apache-2.0 pipeline_tag: text-generation tasks: - text-generation model_type: - qwen3_5 architectures: - Qwen3_5ForConditionalGeneration base_model: - Qwen/Qwen3.6-27B base_model_relation: finetune parameters: 27.4B tensor_type: - BF16 library_name: - transformer - lora - safetensors - pytorch frameworks: - Pytorch language: - en - vi downloads: 25 stars: 0 tags: - function-calling - tool-use - tool-calling - agent - agentic - rl - grpo - lora - intuitor - self-certainty - vllm - qwen3.6 - vietnamese - enterprise-automation --- # Opera > Opera - beyoru-ai 在 ModelScope 开源的模型。Opera — a 27B agent tuned for multi-turn tool calling beyoru-ai/Opera 是 ModelScope 魔搭社区上的 27.4B 参数text-generation模型，采用 apache-2.0 许可，基于 Qwen/Qwen3.6-27B 构建。 - **Repository**: beyoru-ai/Opera - **License**: apache-2.0 - **Tasks**: text-generation - **Parameters**: 27.4B - **Base model**: Qwen/Qwen3.6-27B - **Tags**: function-calling, tool-use, tool-calling, agent, agentic, rl, grpo, lora, intuitor, self-certainty, vllm, qwen3.6, vietnamese, enterprise-automation - **Downloads**: 25 - **Stars**: 0 - **Last updated**: 2026-07-30 Source: https://www.modelscope.cn/models/beyoru-ai/Opera --- # Opera — a 27B agent tuned for multi-turn tool calling **Opera** is an RL fine-tune of **Qwen3.6-27B** for *agentic* tool-calling: multi-turn episodes where the model has to look up a policy, read messy free-text input, compute an exact number, and then call the right tool with the right arguments — not just emit one well-formed function call. It was trained with **GRPO + LoRA** and a composite reward, including [Intuitor self-certainty](https://arxiv.org/abs/2505.19590) as an intrinsic signal alongside rule-based correctness. **Use it if you need:** an open-weight agent backend for business automation (accounting, HR, operations) that holds up over long tool-use episodes, and that works on Vietnamese input out of the box. | | | |---|---| | Base | `Qwen/Qwen3.6-27B` (dense, VL architecture) | | Method | GRPO + LoRA (r32 / α64), merged into full weights | | Precision / size | bf16, 11 shards (~54 GB) | | Context | 16K validated (base supports more) | | Reasoning | thinking **on** — this is how it was trained and evaluated | | Tool-call format | Qwen3.5-style XML (``), **not** JSON — see gotchas | | License | Apache-2.0 | --- ## Quickstart ### Serve with vLLM (recommended) ```bash vllm serve beyoru/Opera --served-model-name opera \ -tp 4 --max-model-len 16384 \ --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3 ``` Do **not** pass `--trust-remote-code`: vLLM registers its own `Qwen3_5MoeConfig`/`qwen3_5` config class only when remote code is off. With the flag on you get `Invalid type of HuggingFace config`. Fits on 1×H200, or 2×A100-80G / 4×A100-40G with `-tp`. ### Call it like any OpenAI-compatible endpoint ```python from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY") tools = [{ "type": "function", "function": { "name": "create_penalty_voucher", "description": "Lập chứng từ lãi phạt chậm thanh toán cho một hóa đơn.", "parameters": { "type": "object", "properties": { "invoice_no": {"type": "string"}, "amount": {"type": "integer", "description": "Số tiền lãi phạt (VND)"}, }, "required": ["invoice_no", "amount"], }, }, }] resp = client.chat.completions.create( model="opera", messages=[{"role": "user", "content": "Hóa đơn HD-1042 của Cty TNHH An Phát: dư nợ 120 triệu, quá hạn 45 ngày. " "Tra chính sách rồi lập chứng từ lãi phạt."}], tools=tools, temperature=0.7, max_tokens=4096, ) print(resp.choices[0].message.tool_calls) ``` ### Transformers ```python from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("beyoru/Opera") model = AutoModelForCausalLM.from_pretrained("beyoru/Opera", dtype="auto", device_map="auto") msgs = [{"role": "user", "content": "Tính lãi phạt cho hóa đơn quá hạn 45 ngày, dư nợ 120 triệu."}] ids = tok.apply_chat_template(msgs, tools=tools, add_generation_prompt=True, return_tensors="pt") out = model.generate(ids.to(model.device), max_new_tokens=1024) print(tok.decode(out[0][ids.shape[-1]:], skip_special_tokens=True)) ``` --- ## Results Held-out validation: the 48 hardest multi-turn tool-calling episodes from a private business-workflow set, scored every 5 steps during training. | step | score | |---|---| | 0 (base) | 0.564 | | 5 | 0.570 | | 10 | 0.567 | | 15 | **0.600** (peak, not checkpointed) | | **20 (this checkpoint)** | 0.574 | Only the final checkpoint was saved, so the released weights are **not** the peak of that curve, and the measured gain of this checkpoint over the base on that set is small (+0.010). Treat this as a targeted nudge on top of an already strong base — the value here is a merged, ready-to-serve agentic checkpoint plus the serving recipe below, not a large capability jump. --- ## Gotchas worth knowing These cost real debugging time. They apply to the whole Qwen3.5/3.6 family, not just Opera. 1. **Tool calls are XML, not JSON.** The model emits `VALUE`. A Hermes/JSON parser silently returns *zero* tool calls — which looks exactly like a broken environment or a model that refuses to act. Use `--tool-call-parser qwen3_coder`, or parse the XML yourself. 2. **No `--trust-remote-code`** when serving with vLLM (see above). 3. **Keep thinking on.** Training and evaluation both ran with reasoning enabled; disabling it costs the most on exact-sequence style metrics. 4. **Vision is inherited, untouched, untested.** The checkpoint keeps the base VL architecture and vision tower. RL training was text-only and the multimodal path was never evaluated here. --- ## Training details | | | |---|---| | Algorithm | GRPO, 20 steps × batch 32 (640 prompts), 4 rollouts/prompt | | Adapter | LoRA r32 / α64, lr 5e-6, merged into base at export | | Data | xLAM + APIGen-MT function-calling traces, pre-filtered to ≤8192 tokens | | Reward | rule-based (function name + argument-subset partial credit) + tool-call structural validity + Intuitor self-certainty, rule and self-certainty z-normalized separately then weighted 1.0 / 1.0, applied at EOS | | Framework | verl | Self-certainty is computed over full-vocabulary logits. At 27B that has to be **chunked** (2048-token chunks, mirroring entropy chunking) or the run hangs during log-prob recomputation with the GPU at 0% and no OOM message. Training data is English function-calling; **Vietnamese ability comes from the base model** — it is not something this RL stage taught. ## Limitations - Short RL run (20 steps). This is a targeted nudge on top of a strong base, not a from-scratch agent. - Validation set is small (n=48) and private, so differences of a few points are within noise and cannot be independently reproduced. No public-leaderboard numbers are claimed. - Released checkpoint is step 20, not the step-15 peak. - Not evaluated on: long-context (>16K), multimodal input, non-agentic chat quality, safety. Assume base-model behaviour on all of those. ## Citation ```bibtex @misc{opera2026, title = {Opera: GRPO + self-certainty RL for agentic tool-calling}, author = {beyoru}, year = {2026}, url = {https://huggingface.co/beyoru/Opera} } ``` Built on [Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B). Self-certainty reward from [Intuitor](https://arxiv.org/abs/2505.19590).