Instructions to use alexchapin/portkit-coder-8b-grpo7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alexchapin/portkit-coder-8b-grpo7 with Transformers:

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

pipe = pipeline("text-generation", model="alexchapin/portkit-coder-8b-grpo7")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alexchapin/portkit-coder-8b-grpo7")
model = AutoModelForCausalLM.from_pretrained("alexchapin/portkit-coder-8b-grpo7")
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 alexchapin/portkit-coder-8b-grpo7 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alexchapin/portkit-coder-8b-grpo7"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alexchapin/portkit-coder-8b-grpo7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alexchapin/portkit-coder-8b-grpo7

SGLang

How to use alexchapin/portkit-coder-8b-grpo7 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 "alexchapin/portkit-coder-8b-grpo7" \
    --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": "alexchapin/portkit-coder-8b-grpo7",
		"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 "alexchapin/portkit-coder-8b-grpo7" \
        --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": "alexchapin/portkit-coder-8b-grpo7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alexchapin/portkit-coder-8b-grpo7 with Docker Model Runner:
```
docker model run hf.co/alexchapin/portkit-coder-8b-grpo7
```

alexchapin/portkit-coder-8b-grpo7

Self-reflection RL fine-tuned model for Minecraft Java (Forge) to Bedrock Add-on conversion.

Model Details

Base model: Qwen/Qwen3-8B
Training method: GRPO with self-reflection rewards (inspired by ReflexiCoder)
Checkpoint: GRPO7 final (100 steps, group_size=12)
Learning rate: 1e-6

Reward Components

Component	Weight	Description
manifest_completeness	0.20	format_version, header, uuid, version validation
structure_building	0.20	JSON structure, module types
api_correctness	0.20	@minecraft/server API usage
js_syntax	0.20	JavaScript syntax validity
self_reflection	0.20	Correction pattern detection

Training Details

Steps: 100
Group size: 12
Reward function: Self-reflection rewards inspired by ReflexiCoder
Key insight: Lower LR (1e-6) for stability; slightly better JS API correctness (72.7% vs 72.5%)

Usage

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("alexchapin/portkit-coder-8b-grpo7")

Framework Versions

tinker-cookbook: 0.4.1
transformers: 5.5.3
torch: 2.11.0+rocm7.2

Downloads last month: 27

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for alexchapin/portkit-coder-8b-grpo7

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Finetuned

(1609)

this model