Instructions to use V7W3D/qwen-code-doc-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use V7W3D/qwen-code-doc-ft with Transformers:

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

pipe = pipeline("text-generation", model="V7W3D/qwen-code-doc-ft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("V7W3D/qwen-code-doc-ft")
model = AutoModelForCausalLM.from_pretrained("V7W3D/qwen-code-doc-ft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use V7W3D/qwen-code-doc-ft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "V7W3D/qwen-code-doc-ft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "V7W3D/qwen-code-doc-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/V7W3D/qwen-code-doc-ft

SGLang

How to use V7W3D/qwen-code-doc-ft 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 "V7W3D/qwen-code-doc-ft" \
    --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": "V7W3D/qwen-code-doc-ft",
		"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 "V7W3D/qwen-code-doc-ft" \
        --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": "V7W3D/qwen-code-doc-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use V7W3D/qwen-code-doc-ft with Docker Model Runner:
```
docker model run hf.co/V7W3D/qwen-code-doc-ft
```

Qwen2.5-Coder-3B-Instruct – Fine-tuned for COBOL Code Documentation

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-3B-Instruct, optimized for generating natural language documentation from COBOL source code. The fine-tuning was done using freeze fine-tuning on the last transformer layer only, preserving the rest of the model's pretrained weights.

🔧 Model Description

Architecture: Qwen2.5-Coder-3B (decoder-only transformer)
Base Model: Qwen/Qwen2.5-Coder-3B-Instruct
Fine-tuning Method: Freeze fine-tuning (only last transformer block's parameters were updated)
Training Objective: Instruction-following text generation for COBOL code documentation

🧠 Use Cases

This model is specialized in generating descriptive documentation for legacy COBOL code, especially useful for:

Legacy system maintenance
Automated codebase documentation
Migration planning
COBOL code understanding and onboarding

✍️ Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_name = "V7W3D/qwen-code-doc-ft"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

doc_gen = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "### Document this COBOL code:\n\n       IDENTIFICATION DIVISION.\n       PROGRAM-ID. HELLO-WORLD.\n       PROCEDURE DIVISION.\n           DISPLAY 'HELLO, WORLD!'\n           STOP RUN.\n\n### Documentation:"
response = doc_gen(prompt, max_new_tokens=200, do_sample=False)

print(response[0]["generated_text"])

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Safetensors

Model size

3B params

Tensor type

F32

Model tree for V7W3D/qwen-code-doc-ft

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-Coder-3B

Finetuned

Qwen/Qwen2.5-Coder-3B-Instruct

Finetuned

(111)

this model