badtheorylabs

BTL-3

README

License: apache-2.0

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 is available for native local inference.

Results

All values below belong to the frozen BTL-3 RL-0013 release.

Table with columns: Evaluation, Score, Protocol
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

Table with columns: Category, Score
Category	Score
Simple	93.2%
Multiple	95.5%
Parallel	87.0%
Parallel-multiple	70.0%
Irrelevance	91.2%

Model specification

Table with columns: Item, 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

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=True,
)
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))

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

Table with columns: Artifact, SHA-256
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 or join the community Discord.

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Model Details

Model Provider

badtheorylabs

Model Tree

Base

Qwen/Qwen3.6-27B

Adapter

this model

Input Modalities

Text

Image

Video

Output Modalities