Instructions to use issdandavis/scbe-coding-approval-metrics-qwen-kaggle-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use issdandavis/scbe-coding-approval-metrics-qwen-kaggle-v1 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-0.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "issdandavis/scbe-coding-approval-metrics-qwen-kaggle-v1")

Transformers

How to use issdandavis/scbe-coding-approval-metrics-qwen-kaggle-v1 with Transformers:

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

pipe = pipeline("text-generation", model="issdandavis/scbe-coding-approval-metrics-qwen-kaggle-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("issdandavis/scbe-coding-approval-metrics-qwen-kaggle-v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use issdandavis/scbe-coding-approval-metrics-qwen-kaggle-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/issdandavis/scbe-coding-approval-metrics-qwen-kaggle-v1

SGLang

How to use issdandavis/scbe-coding-approval-metrics-qwen-kaggle-v1 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 "issdandavis/scbe-coding-approval-metrics-qwen-kaggle-v1" \
    --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": "issdandavis/scbe-coding-approval-metrics-qwen-kaggle-v1",
		"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 "issdandavis/scbe-coding-approval-metrics-qwen-kaggle-v1" \
        --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": "issdandavis/scbe-coding-approval-metrics-qwen-kaggle-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use issdandavis/scbe-coding-approval-metrics-qwen-kaggle-v1 with Docker Model Runner:
```
docker model run hf.co/issdandavis/scbe-coding-approval-metrics-qwen-kaggle-v1
```

scbe-coding-approval-metrics-qwen-kaggle-v1

File size: 1,849 Bytes

---

base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
library_name: peft
model_name: scbe-coding-approval-metrics-qwen-kaggle-v1
tags:
- base_model:adapter:Qwen/Qwen2.5-Coder-0.5B-Instruct
- lora
- sft
- transformers
- trl
- superseded
licence: license
pipeline_tag: text-generation
---

<!-- scbe-status -->
> **Status: superseded.** Canonical coding model: [scbe-coding-agent-vtc-qwen15-v1-gguf](https://hf.co/issdandavis/scbe-coding-agent-vtc-qwen15-v1-gguf). Kept as research history.

# Model Card for scbe-coding-approval-metrics-qwen-kaggle-v1

This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python

from transformers import pipeline



question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"

generator = pipeline("text-generation", model="None", device="cuda")

output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]

print(output["generated_text"])

```

## Training procedure

 



This model was trained with SFT.

### Framework versions

- PEFT 0.18.1
- TRL: 1.2.0
- Transformers: 5.0.0
- Pytorch: 2.10.0+cu128
- Datasets: 4.8.3
- Tokenizers: 0.22.2

## Citations



Cite TRL as:
    

```bibtex

@software{vonwerra2020trl,

  title   = {{TRL: Transformers Reinforcement Learning}},

  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},

  license = {Apache-2.0},

  url     = {https://github.com/huggingface/trl},

  year    = {2020}

}

```