Instructions to use dwidlee/systemone-lite-0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dwidlee/systemone-lite-0.5b with Transformers:

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

pipe = pipeline("text-generation", model="dwidlee/systemone-lite-0.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dwidlee/systemone-lite-0.5b")
model = AutoModelForCausalLM.from_pretrained("dwidlee/systemone-lite-0.5b", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dwidlee/systemone-lite-0.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dwidlee/systemone-lite-0.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dwidlee/systemone-lite-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dwidlee/systemone-lite-0.5b

SGLang

How to use dwidlee/systemone-lite-0.5b 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 "dwidlee/systemone-lite-0.5b" \
    --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": "dwidlee/systemone-lite-0.5b",
		"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 "dwidlee/systemone-lite-0.5b" \
        --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": "dwidlee/systemone-lite-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dwidlee/systemone-lite-0.5b with Docker Model Runner:
```
docker model run hf.co/dwidlee/systemone-lite-0.5b
```

systemone-lite-0.5b / train_meta.json

dwidlee

Upload folder using huggingface_hub

06b28ed verified about 2 months ago

Raw History Blame Contribute Delete

362 Bytes

{
  "base_model": "Qwen/Qwen2.5-0.5B-Instruct",
  "data": "data/mixed_train.jsonl",
  "steps": 10800,
  "epochs": 1,
  "tasks": [
    "all"
  ],
  "stratified": true,
  "batch_size": 4,
  "final_avg_loss": 1.1945149920370293,
  "samples_by_gym": {
    "chess": 10800,
    "ticket_dungeon": 10800,
    "resource_allocator": 10800,
    "debate_judge": 10800
  }
}

	{
	"base_model": "Qwen/Qwen2.5-0.5B-Instruct",
	"data": "data/mixed_train.jsonl",
	"steps": 10800,
	"epochs": 1,
	"tasks": [
	"all"
	],
	"stratified": true,
	"batch_size": 4,
	"final_avg_loss": 1.1945149920370293,
	"samples_by_gym": {
	"chess": 10800,
	"ticket_dungeon": 10800,
	"resource_allocator": 10800,
	"debate_judge": 10800
	}
	}