Instructions to use FLs-AI/FL-7B-3.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FLs-AI/FL-7B-3.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="FLs-AI/FL-7B-3.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FLs-AI/FL-7B-3.1")
model = AutoModelForCausalLM.from_pretrained("FLs-AI/FL-7B-3.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use FLs-AI/FL-7B-3.1 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FLs-AI/FL-7B-3.1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf FLs-AI/FL-7B-3.1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FLs-AI/FL-7B-3.1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf FLs-AI/FL-7B-3.1:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf FLs-AI/FL-7B-3.1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf FLs-AI/FL-7B-3.1:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf FLs-AI/FL-7B-3.1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FLs-AI/FL-7B-3.1:Q4_K_M

Use Docker

docker model run hf.co/FLs-AI/FL-7B-3.1:Q4_K_M

LM Studio
Jan

vLLM

How to use FLs-AI/FL-7B-3.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FLs-AI/FL-7B-3.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FLs-AI/FL-7B-3.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FLs-AI/FL-7B-3.1:Q4_K_M

SGLang

How to use FLs-AI/FL-7B-3.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "FLs-AI/FL-7B-3.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FLs-AI/FL-7B-3.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "FLs-AI/FL-7B-3.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FLs-AI/FL-7B-3.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use FLs-AI/FL-7B-3.1 with Ollama:
```
ollama run hf.co/FLs-AI/FL-7B-3.1:Q4_K_M
```

Unsloth Studio

How to use FLs-AI/FL-7B-3.1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FLs-AI/FL-7B-3.1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FLs-AI/FL-7B-3.1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FLs-AI/FL-7B-3.1 to start chatting

How to use FLs-AI/FL-7B-3.1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FLs-AI/FL-7B-3.1:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "FLs-AI/FL-7B-3.1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FLs-AI/FL-7B-3.1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FLs-AI/FL-7B-3.1:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default FLs-AI/FL-7B-3.1:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use FLs-AI/FL-7B-3.1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FLs-AI/FL-7B-3.1:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "FLs-AI/FL-7B-3.1:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use FLs-AI/FL-7B-3.1 with Docker Model Runner:
```
docker model run hf.co/FLs-AI/FL-7B-3.1:Q4_K_M
```

Lemonade

How to use FLs-AI/FL-7B-3.1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FLs-AI/FL-7B-3.1:Q4_K_M

Run and chat with the model

lemonade run user.FL-7B-3.1-Q4_K_M

List all available models

lemonade list

FL-7B-3.1

A Qwen2.5-Coder-7B model adapted for COBOL, mainframe knowledge, and legacy-code modernization.

FL-7B-3.1 starts from Qwen/Qwen2.5-Coder-7B and was trained in two stages: continued pretraining (CPT) on COBOL source material, followed by assistant-only supervised fine-tuning (SFT) on COBOL and mainframe-oriented instructions.

The model is intended for:

generating and completing GnuCOBOL programs;
translating COBOL into Java;
answering mainframe and legacy-system questions;
explaining and summarizing COBOL source code.

Highlights

Benchmark	Result
COBOLEval pass@1	17.81% (26/146)
COBOLEval test compilation rate	57.73% (474/821)
COBOLEval test pass rate	30.82% (253/821)
COBOL-to-Java CSR	61.54% (88/143)
COBOL-to-Java pass@1	48.25% (69/143)
MainframeBench MCQ accuracy	80.84% (1,561/1,931)

Usage with Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "FLs-AI/FL-7B-3.1"

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    fix_mistral_regex=True,
)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": (
            "Write a complete GnuCOBOL 3.2 program that reads signed integers "
            "until EOF and prints their sum. Return only COBOL source code."
        ),
    }
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=1536,
    do_sample=False,
    repetition_penalty=1.05,
)

generated = outputs[0, inputs["input_ids"].shape[1]:]
print(tokenizer.decode(generated, skip_special_tokens=True))

Recommended generation settings

Use case	Temperature	Max new tokens	Repetition penalty
COBOL generation	`0`	`1536`	`1.05`
COBOL to Java	`0`	`4096`	`1.0`
Mainframe MCQ	`0`	`16`	`1.0`
Mainframe QA / summarization	`0`	`512`	`1.0`

For the final COBOLEval run, repetition_penalty=1.05 produced the strongest measured result. COBOL relies heavily on repeated identifiers and fixed structural phrases, so large repetition penalties can damage syntax and correctness.

Evaluation

All results below were measured on the merged BF16 checkpoint with greedy decoding.

COBOLEval

COBOLEval evaluates generated programs by compiling and executing them with GnuCOBOL. The evaluation used 146 problems and 821 test cases, GnuCOBOL 3.2.0, max_new_tokens=1536, and one sample per task.

Model / setting	pass@1	Test compilation rate	Tests passed
Qwen2.5-Coder-7B base	0.00%	3.65%	4/821
FL-7B-3.1, repetition penalty 1.00	17.12%	41.29%	175/821
FL-7B-3.1, repetition penalty 1.05	17.81%	57.73%	253/821

The harness was pinned to commit 0bb96c3114bb2bb28e221e9d6000614781f8609d.

COBOL to Java

The COBOL-JavaTrans C2J evaluation compiles and executes generated Java translations.

Metric	Result
Tasks	143
Compilation success rate (CSR)	61.54% (88/143)
pass@1	48.25% (69/143)

The evaluator was pinned to commit 2b14b7bf7e55556205654c6f7657fa60e36251fa.

MainframeBench

Fsoft-AIC/MainframeBench contains multiple-choice questions, open-ended QA, and COBOL code summarization.

Multiple choice

Tasks	Correct	Accuracy	Invalid predictions
1,931	1,561	80.84%	1

Open-ended tasks

Suite	Tasks	Token F1	ROUGE-L F1	BLEU-4
Question answering	2,598	28.46%	24.31%	3.76
COBOL summarization	2,523	41.76%	36.94%	14.04

Normalized exact match was 0% for both open-ended suites. This strict lexical metric requires the generated response to match the single reference wording after normalization; it is not an accuracy or semantic-correctness score. Token F1, ROUGE-L, and BLEU-4 measure lexical overlap and should not be interpreted as execution-based correctness or human preference.

The dataset was pinned to revision 70d30c76eb29e45dd8965304b41c56bc1f527972.

Limitations

The model can enter repetition loops or produce excessively long code on difficult tasks.
A compiling program is not necessarily functionally correct or safe.
Evaluation used GnuCOBOL 3.2.0.
General-purpose coding performance inherited from Qwen2.5-Coder was not re-evaluated and may have regressed during domain adaptation.
MainframeBench QA and summarization results are lexical-overlap scores, not semantic accuracy.

Do not deploy generated code to production systems without compilation, tests, static analysis, and review by an experienced mainframe engineer.

License

This fine-tune is released under CC BY-NC 4.0. Attribution is required and commercial use of the fine-tuned weights is not permitted under this license. The Qwen2.5-Coder-7B base model is licensed separately under Apache 2.0.

Citation

@misc{fl7b31,
  title  = {FL-7B-3.1: COBOL and Mainframe Code Model},
  author = {FLs-AI},
  year   = {2026},
  url    = {https://huggingface.co/FLs-AI/FL-7B-3.1}
}

Acknowledgements

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8B params

Architecture

qwen2

Hardware compatibility

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