Instructions to use CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset with Transformers:

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

pipe = pipeline("text-generation", model="CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset")
model = AutoModelForCausalLM.from_pretrained("CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset", device_map="auto")
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 Settings

vLLM

How to use CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset

SGLang

How to use CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset 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 "CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset" \
    --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": "CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset",
		"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 "CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset" \
        --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": "CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset with Docker Model Runner:
```
docker model run hf.co/CompassioninMachineLearning/Qwen-3-8b-final-CPT-10k-urban-density-dataset
```

Qwen3-8b-urban-qwen-20260920-full-CPT-final-step-1500

Standalone merged BF16 model from epoch 3.970178926441352, step 1500.

Dataset: CompassioninMachineLearning/urban_12738_cleaned at ef7c0e742df63ea319e35d02d9f6ba63d6e7c68d.

Training: 10,072 distinct documents plus 2,000 repeat exposures per epoch; 200 disjoint validation documents.

Merged with Unsloth's native save_pretrained_merged(save_method="merged_16bit"). Weights are validated BF16 and packaged losslessly into eight safetensors shards. No adapter is required to load this model.

See run_manifest.json for base revision, document selection hashes, training parameters and export validation. Training does not establish an improvement in compassion; evaluate that separately.