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

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

Hermes Agent

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

FL-7B-3.1 / README.md

FLs-AI

Update README.md

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	---
	license: cc-by-nc-4.0
	base_model: Qwen/Qwen2.5-Coder-7B
	base_model_relation: finetune
	library_name: transformers
	pipeline_tag: text-generation
	language:
	- en
	tags:
	- qwen2
	- safetensors
	- code
	- cobol
	- mainframe
	- legacy-code
	- code-translation
	- unsloth
	---

	# 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](https://huggingface.co/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

	```python
	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](https://github.com/zorse-project/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](https://github.com/COBOL-Coder/COBOL-Coder) 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](https://huggingface.co/datasets/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

	```bibtex
	@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

	- [Qwen2.5-Coder](https://huggingface.co/Qwen/Qwen2.5-Coder-7B)
	- [Unsloth](https://github.com/unslothai/unsloth)
	- [GnuCOBOL](https://gnucobol.sourceforge.io/)
	- [COBOLEval](https://github.com/zorse-project/COBOLEval)
	- [COBOL-Coder / COBOL-JavaTrans](https://github.com/COBOL-Coder/COBOL-Coder)
	- [MainframeBench](https://huggingface.co/datasets/Fsoft-AIC/MainframeBench)