FL-9B-0.2

FL-9B-0.2 is a fine-tune of Qwen3.5-9B focused on legacy mainframe code, primarily COBOL, with additional coverage of JCL, DB2 and embedded SQL. It is meant for teams working with COBOL codebases who want a local model that writes, completes and translates mainframe code. The base model keeps its hybrid layout (Gated DeltaNet linear-attention layers interleaved with full-attention layers) and native 262k context. Fine-tuning was done with QLoRA on a single GPU, so the release is a merged set of weights ready for vLLM, SGLang, Transformers, and llama.cpp.

Training

• Base: Qwen/Qwen3.5-9B
• Method: supervised fine-tuning, QLoRA (rank 256), merged back to bf16
• Hardware: one RTX 5090 (32 GB)

Benchmarks

Numbers below are for FL-9B-0.2. FL-9B-0.1 is the previous release.

Benchmark	Metric	FL-9B-0.1	FL-9B-0.2
COBOLEval	pass@1	37.0%	41.8%
MainframeBench	MCQ accuracy	71.3%	77.4%
MainframeBench	QA token F1	12.8%	18.5%
MainframeBench	Summarization token F1	27.6%	35.5%
CobolCodeBench	INSTRUCT compile rate	47.8%	67.4%
CobolCodeBench	COMPLETE compile rate	32.6%	41.3%
COBOL to Java	pass@1	80.4%	54.5%

Comparison with other models on COBOLEval

The scores for the other models come from the COBOL-Coder paper and the Skylar model card, so harness details differ and the numbers are indicative rather than a controlled head-to-head.

Model	Params	Compile / CSR	pass@1
GPT-4o	-	41.8%	16.4%
Skylar-980M-Cobol	980M	80.1%	7.5%
COBOL-Coder-7B	7B	73.8%	44.7%
COBOL-Coder-14B	14B	74.0%	49.3%
FL-9B-0.2	9B	90.6%	41.8%
FL-9B-0.2 has the highest compile rate in this group and a pass@1 above GPT-4o and Skylar, close
to COBOL-Coder-7B. On COBOL to Java translation it trails the COBOL-Coder models (pass@1 54.5%
against roughly 82 to 84%), which is the main area for the next data pass.

Usage

The model thinks by default, the same as base Qwen3.5. For code generation and the benchmark numbers above, thinking is turned off and generation is greedy.

vLLM

vllm serve FLs-AI/FL-9B-0.2 --port 8000 --max-model-len 8192 --reasoning-parser qwen3

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "FLs-AI/FL-9B-0.2"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")
messages = [{"role": "user", "content": "Write a COBOL program that reads a file and prints each record."}]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
out = model.generate(**tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=1024)
print(tok.decode(out[0], skip_special_tokens=True))

Recommended settings for COBOL

• Turn thinking off: enable_thinking=False
• Greedy decoding: temperature=0
• Leave repetition penalty at 1.0. COBOL is a repetitive language by nature, and a penalty above 1.0 breaks otherwise valid, repetitive source.

Notes on GGUF

GGUF builds are published separately. Column layout matters when compiling generated COBOL: code belongs in area B (column 8 onward), and a first line flush to column 1 will fail under cobc -fformat=variable. The benchmark harness normalizes columns before compiling.

License

Apache 2.0, following the base model.