Instructions to use ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123 with Transformers:

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

pipe = pipeline("text-generation", model="ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123")
model = AutoModelForCausalLM.from_pretrained("ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123", 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 ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123

SGLang

How to use ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123 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 "ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123" \
    --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": "ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123",
		"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 "ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123" \
        --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": "ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123",
    max_seq_length=2048,
)

Docker Model Runner
How to use ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123 with Docker Model Runner:
```
docker model run hf.co/ubetu/Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123
```

Qwen2.5-Coder-0.5B-Instruct-code-lora-seed123 / metrics.csv

ubetu

Upload metrics.csv with huggingface_hub

b962371 verified about 1 month ago

Raw

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1.68 kB

	loss,grad_norm,learning_rate,epoch,step,train_runtime,train_samples_per_second,train_steps_per_second,total_flos,train_loss
	0.6322,0.17212332785129547,0.00019,0.050505050505050504,20,,,,,
	0.5376,0.07873357087373734,0.00019874255503213152,0.10101010101010101,40,,,,,
	0.5167,0.08910174667835236,0.0001947376831715892,0.15151515151515152,60,,,,,
	0.5077,0.09089775383472443,0.00018809346758274012,0.20202020202020202,80,,,,,
	0.494,0.10141683369874954,0.0001789950127367606,0.25252525252525254,100,,,,,
	0.4934,0.09715284407138824,0.00016769579696382597,0.30303030303030304,120,,,,,
	0.4909,0.10125218331813812,0.000154510610675594,0.35353535353535354,140,,,,,
	0.4909,0.09717316180467606,0.0001398067864642431,0.40404040404040403,160,,,,,
	0.4842,0.10780832171440125,0.00012399396540305205,0.45454545454545453,180,,,,,
	0.4862,0.10296116024255753,0.00010751268465418785,0.5050505050505051,200,,,,,
	0.4769,0.10042838007211685,9.082210432715197e-05,0.5555555555555556,220,,,,,
	0.4878,0.10099862515926361,7.438721551137367e-05,0.6060606060606061,240,,,,,
	0.4819,0.11334463208913803,5.866588586066481e-05,0.6565656565656566,260,,,,,
	0.4718,0.10493728518486023,4.409610363297211e-05,0.7070707070707071,280,,,,,
	0.4749,0.1072872206568718,3.108377556000037e-05,0.7575757575757576,300,,,,,
	0.4815,0.1041322574019432,1.999141849185938e-05,0.8080808080808081,320,,,,,
	0.4785,0.10225611925125122,1.11280598617714e-05,0.8585858585858586,340,,,,,
	0.4777,0.11613084375858307,4.740628338761255e-06,0.9090909090909091,360,,,,,
	0.4826,0.1027882918715477,1.0070745203721532e-06,0.9595959595959596,380,,,,,
	,,,1.0,396,748.3606,67.577,0.529,9.243733345473024e+16,0.49608901052763965