--- license: apache-2.0 base_model: Qwen/Qwen3.6-27B library_name: peft pipeline_tag: text-generation tags: - agent - coding - reasoning - tool-use - function-calling - qwen ---
# BTL-3 ### A 27B open-weight agent model for agentic coding and structural tool use **95.1% HumanEval · 88.5% BFCL v4 AST · 88.1% LiveCodeBench v6 (193-case run)** [Compact edition](https://huggingface.co/badtheorylabs/BTL-3-Compact) · [Runtime source](https://github.com/Badtheorylabs/BTL-3) · [Bad Theory Labs](https://www.badtheorylabs.com/) · [Discord](https://discord.gg/QJBCcB7bF)
## Introducing BTL-3 BTL-3 is a 27B open-weight agent model built for agentic coding, structural tool use, repository work, failure recovery, and long multi-turn execution. The release includes the full BTL-3 model and [BTL-3 Compact](https://huggingface.co/badtheorylabs/BTL-3-Compact), which packages the complete text model into one **8.39 GB** native file. That is smaller than an 8B model stored in FP16 and corresponds to an effective artifact footprint of **under 2.5 bits per parameter**. On a fresh private 100-turn tool-contract gate, BTL-3 Compact retained **83 of the 90 behaviors the full model completed correctly: 92.2% measured conditional tool-behavior retention**. ## Overview BTL-3 is a post-trained Qwen3.6-27B model for coding agents, repository work, structured tool use, and long multi-turn execution. It is tuned to reason, act, inspect tool results, recover from failures, and stop when no action is required. This repository contains the frozen **RL-0013 rank-32 PEFT adapter** and its tokenizer configuration. The base checkpoint is pinned to an exact revision for reproducible loading. ## Highlights - Strong structured tool use across single, multiple, and parallel calls. - **91.2% BFCL irrelevance**, measuring when the model correctly avoids an unnecessary tool call. - Thinking-mode coding with **95.12% HumanEval pass@1**. - Qwen3.6 hybrid-attention architecture with a declared **262,144-token** context window. - Open weights under Apache-2.0, deployable with Transformers or vLLM. - An independent [8.39 GB Compact edition](https://huggingface.co/badtheorylabs/BTL-3-Compact) is available for native local inference. ## Results All values below belong to the frozen BTL-3 RL-0013 release. | Evaluation | Score | Protocol | |---|---:|---| | BFCL v4 AST | **88.5% (1097/1240)** | Complete official full set | | HumanEval | **95.12% (156/164)** | pass@1, thinking mode | | LiveCodeBench v6 | **88.1% (170/193)** | Completed 193-case run, thinking mode | | BigCodeBench-Hard Instruct | **26.35% (39/148)** | Official strict pass@1 | | BigCodeBench functional tests | **59.25% (506/854)** | Supplementary test-level score | ### BFCL v4 category breakdown | Category | Score | |---|---:| | Simple | **93.2%** | | Multiple | **95.5%** | | Parallel | **87.0%** | | Parallel-multiple | **70.0%** | | Irrelevance | **91.2%** | ## Model specification | Item | Specification | |---|---| | Base model | `Qwen/Qwen3.6-27B` | | Base revision | `6a9e13bd6fc8f0983b9b99948120bc37f49c13e9` | | Release checkpoint | BTL-3 RL-0013 | | Adapter | PEFT LoRA, rank 32, alpha 64 | | Adapter size | 933,974,032 bytes | | Architectural context | 262,144 tokens | | Maximum RL sequence length | 65,536 tokens | | Launch benchmark context | 32,768 tokens | | Recommended mode | Thinking enabled for coding and reasoning | | License | Apache-2.0 | ## Quickstart ### Transformers ```python import torch from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer base_id = "Qwen/Qwen3.6-27B" base_revision = "6a9e13bd6fc8f0983b9b99948120bc37f49c13e9" adapter_id = "badtheorylabs/BTL-3" tokenizer = AutoTokenizer.from_pretrained(adapter_id) base = AutoModelForCausalLM.from_pretrained( base_id, revision=base_revision, torch_dtype=torch.bfloat16, device_map="auto", ) model = PeftModel.from_pretrained(base, adapter_id) messages = [ { "role": "user", "content": "Inspect this repository, fix the failing tests, and explain the patch.", } ] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False, ) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) output = model.generate(**inputs, max_new_tokens=4096) completion = output[0, inputs.input_ids.shape[1]:] print(tokenizer.decode(completion, skip_special_tokens=False)) ``` The supported release default is non-thinking mode. Thinking remains available for controlled evaluation, but it is currently discouraged because the RL-0013 reasoning policy can become repetitive or fail to terminate on some prompts. ### vLLM BTL-3 was evaluated with vLLM 0.23.0: ```bash vllm serve Qwen/Qwen3.6-27B \ --revision 6a9e13bd6fc8f0983b9b99948120bc37f49c13e9 \ --served-model-name BTL-3 \ --enable-lora \ --max-lora-rank 32 \ --lora-modules BTL-3=/path/to/BTL-3 \ --lora-target-modules \ q_proj k_proj v_proj o_proj \ in_proj_qkv in_proj_z in_proj_b in_proj_a out_proj \ gate_proj up_proj down_proj \ --reasoning-parser qwen3 \ --language-model-only \ --max-model-len 32768 ``` For structured tools, enable the Qwen XML tool parser supported by your installed vLLM version. ## Intended use - coding, debugging, and test-driven repair; - repository and terminal agents; - structured function calling and multi-tool workflows; - private or self-hosted agent deployments; - long multi-turn tasks that require verification and recovery. ## Artifact integrity | Artifact | SHA-256 | |---|---| | `adapter_model.safetensors` | `37a8f519039707eba5906591cdb14268768db43f80489a9c2f83b3e51e5e89db` | ## Operational guidance Run generated code and tool calls in a sandbox. Require explicit confirmation before destructive, privileged, financial, or otherwise high-impact actions. ## License and citation The adapter is released under Apache-2.0 and requires the separately distributed Qwen3.6-27B base model. ```bibtex @software{btl3_2026, title = {BTL-3: A 27B Agentic Coding and Tool-Use Model}, author = {Bad Theory Labs}, year = {2026}, url = {https://huggingface.co/badtheorylabs/BTL-3} } ``` For questions and release updates, visit [Bad Theory Labs](https://www.badtheorylabs.com/) or join the [community Discord](https://discord.gg/QJBCcB7bF).