Instructions to use joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3 with Transformers:

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

pipe = pipeline("text-generation", model="joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3")
model = AutoModelForCausalLM.from_pretrained("joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3", 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 joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3

SGLang

How to use joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3 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 "joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3" \
    --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": "joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3",
		"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 "joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3" \
        --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": "joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3 with Docker Model Runner:
```
docker model run hf.co/joshuasundance/mypo-qwen2.5-coder-1.5b-dpo-v3
```

mypo-qwen2.5-coder-1.5b-dpo-v3 / emissions.csv

joshuasundance

Upload CodeCarbon emissions.csv

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	timestamp,project_name,run_id,experiment_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,water_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,cpu_utilization_percent,gpu_utilization_percent,ram_utilization_percent,ram_used_gb,on_cloud,pue,wue
	2026-04-22T22:26:33,codecarbon,ba950a38-b3a3-46b2-9911-7183a9df1633,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,6005.371236527004,0.13411510475644237,2.2332525246849374e-05,21.40210806247722,144.90591554415892,54.0,0.03448764703697292,0.24181496956293191,0.08702064124127941,0.3633232578411839,0.0,United States,USA,virginia,,,Linux-6.12.79-101.147.amzn2023.x86_64-x86_64-with-glibc2.36,3.12.12,3.2.6,48,AMD EPYC 7R32,1,1 x NVIDIA A10G,-77.4903,39.0469,186.68793869018555,machine,3.375802139037433,43.505848930481285,7.6515207219251336,14.27656325936955,N,1.0,0.0