---
license: apache-2.0
base_model: Tesslate/OmniCoder-9B
tags:
  - code
  - cobol
  - assembly
  - c
  - legacy
  - low-level
  - mainframe
  - continued-pretraining
language:
  - en
---

# Flare-9B v1.1 — Low-Level & Legacy Code

**A 9B coding model that speaks the languages most models forgot.**

Modern code models are great at Python, JavaScript and the usual suspects — and useless
the moment you hand them a COBOL payroll routine or a hand-written x86 assembly stub.
Flare-9B v1.1 is built for exactly that gap: **COBOL, assembly and C**, the languages that
still run banks, mainframes and firmware, with almost no model support behind them.

It's a continued-pretraining pass over [Tesslate/OmniCoder-9B](https://huggingface.co/Tesslate/OmniCoder-9B)
on a curated corpus of real low-level and legacy code — tuned to add legacy fluency
**without throwing away** the strong general coding ability of the base.

## Why it exists

Ask a top open coding model to write COBOL and you usually get confident nonsense — missing
`DATA DIVISION`, undeclared variables, code that doesn't compile. That's not a knock on those
models; there's simply very little clean COBOL in their training data.

Flare-9B v1.1 was trained on a **cleaned, code-only** legacy corpus (no scraped prose, no
chat transcripts — just real programs), so it produces COBOL that actually compiles and runs.

## Benchmarks

All numbers are **pass@1, executed** — every sample is compiled (gcc / g++ / GnuCOBOL / mono)
and run against test cases. No self-reported or LLM-judged scores.

| Language | Base (OmniCoder-9B) | **Flare-9B v1.1** |
|---|---|---|
| **COBOL** | 0% | **50%** |
| C | 87% | 81% |
| C++ | 87% | 81% |
| C# | 75% | 69% |
| Assembly x86-64 | 4% | 10% |

The headline: **COBOL goes from completely unusable to genuinely useful** — the base model
scores a flat zero, Flare v1.1 solves half of a 40-task executable COBOL suite. General
C-family ability stays strong (~80%+), so you get legacy fluency as an addition, not a
trade-down. Raw hand-written assembly remains hard for any 9B model, but the simple cases
land cleanly.

## Best use

This model is **completion-oriented** — it shines when you give it code context (a function
signature, a program skeleton, a divisions header) rather than long chat instructions.

```python
from transformers import AutoModelForImageTextToText, AutoTokenizer

tok = AutoTokenizer.from_pretrained("DarkKnighToS223/Flare-9B-low-level-programming")
model = AutoModelForImageTextToText.from_pretrained(
    "DarkKnighToS223/Flare-9B-low-level-programming",
    device_map="auto", trust_remote_code=True)

prompt = """*> GnuCOBOL: compute 15 + 27 and DISPLAY the result.
       IDENTIFICATION DIVISION.
       PROGRAM-ID. MAIN.
"""
inputs = tok(prompt, return_tensors="pt").to(model.device)
print(tok.decode(model.generate(**inputs, max_new_tokens=200)[0], skip_special_tokens=True))
```

## Training

| | |
|---|---|
| Base | Tesslate/OmniCoder-9B (Qwen3.5, hybrid linear-attention, 9.6B) |
| Method | QLoRA (4-bit NF4), LoRA rank 64 |
| Data | cleaned code-only corpus: COBOL, x86-64 assembly, C, C++, C# |
| Context | up to long-form programs |
| Framework | Unsloth + PEFT |

## Formats

- **GGUF** quants (Q8_0, Q6_K, Q4_K_M, Q2_K) for llama.cpp — use **Q4_K_M or higher** for
  reliable output; Q2_K on a 9B is best treated as experimental.
- Full-precision weights available on request.

## License

Apache-2.0, inherited from the base model.

---

*Flare-9B v1.1 — because legacy code still needs to ship.*
*im working at way better model than right now...*
*need some time...*