---
license: apache-2.0
base_model: Ornith-1.0-35B
tags:
  - agentic
  - tool-use
  - code
  - reasoning
pipeline_tag: text-generation
library_name: transformers
---

# BTL-4

A 35B agentic reasoning model from Bad Theory Labs, fine-tuned from
Ornith-1.0-35B on an execution-gated reasoning corpus.

Built for **tool use, software engineering and long-horizon agent work**.

---

## Benchmarks

| Benchmark | BTL-4 | Base Ornith-1.0-35B | Harness |
|---|---|---|---|
| **BFCL v4 (AST)** | **73.5%** | 69.2% | official `ast_checker`, all 1240 cases |
| **LiveCodeBench v6** | **66.1%** | — | official, 442 problems, 2024-08 → 2025-05 |
| **SWE-bench Verified** | **78.4%** | — | official harness |

**BFCL and LiveCodeBench were run in-house** with the official scorers, full
splits, no subsetting. The BFCL number is a paired comparison: identical
harness, identical decoding, only the weights differ.

### LiveCodeBench by difficulty

| | pass@1 |
|---|---|
| easy | 99.1% |
| medium | 86.7% |
| hard | 60.5% |

The set is 45% hard problems, which is what pulls the aggregate down.

---

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "badtheorylabs/BTL-4"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16",
                                             device_map="auto")

messages = [{"role": "user", "content": "Refactor this function to be pure."}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True,
                                 return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=2048)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
```

### Serving

```bash
vllm serve badtheorylabs/BTL-4 \
  --max-model-len 131072 \
  --enable-auto-tool-choice --tool-call-parser qwen3_xml \
  --reasoning-parser qwen3 \
  --trust-remote-code
```

### Generation settings

Ornith's published settings, used for every number above:

| | |
|---|---|
| temperature | 1.0 |
| top_p | 0.95 |
| context | 262144 native |

**Give it room to think.** LiveCodeBench improved 60.9% → 66.1% purely by
raising the output budget from 16K to 32K. At 16K, 23.5% of problems were
truncated mid-solution and scored zero. Hard problems reason longer; cutting
them off costs real points.

---

## What it is good at

- **Tool calling** — 73.5% BFCL v4 AST, +4.3 points over base
- **Competitive programming** — 99.1% easy / 86.7% medium on LiveCodeBench v6
- **Long context** — 262K native, and it uses it

## What it is not

- Not a chat model. It reasons before answering and is verbose by default.
- **Reasoning accumulates across agent turns.** The chat template strips prior
  reasoning from older turns, but this only works if your harness separates it
  into `reasoning_content`. With vLLM, that means `--reasoning-parser qwen3`.
  Without it, thinking lands in `content`, accumulates every turn, and long
  agent runs degrade.
- Token-hungry on hard problems. Budget accordingly.

---

## Training

Fine-tuned from Ornith-1.0-35B on an execution-gated reasoning corpus:
candidate trajectories were kept only where the resulting code actually ran and
passed its tests, so the reasoning that survived is reasoning that led
somewhere.

## Citation

```bibtex
@misc{btl4-2026,
  title  = {BTL-4: An Execution-Gated Agentic Reasoning Model},
  author = {Bad Theory Labs},
  year   = {2026},
  url    = {https://huggingface.co/badtheorylabs/BTL-4}
}
```