Instructions to use hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179 with Transformers:

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

pipe = pipeline("text-generation", model="hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179")
model = AutoModelForCausalLM.from_pretrained("hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179", 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 hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179

SGLang

How to use hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179 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 "hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179" \
    --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": "hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179",
		"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 "hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179" \
        --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": "hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179 with Docker Model Runner:
```
docker model run hf.co/hasdal/e30265ef-78ca-4cec-b3d3-8f063d27b179
```

e30265ef-78ca-4cec-b3d3-8f063d27b179 / noise_info.json

hasdal

whooop

4922a14 verified about 1 year ago

Raw History Blame Contribute Delete

319 Bytes

	{
	"noise_std": 0.01,
	"noise_type": "gaussian",
	"target": "input_embeddings",
	"timestamp": "2025-08-19T20:10:49.093762",
	"original_model": "unsloth/Qwen2.5-0.5B-Instruct",
	"training_type": "grpo",
	"job_id": "0f567adf-46fb-4da6-8fb5-2c3e48200314",
	"expected_repo": "e30265ef-78ca-4cec-b3d3-8f063d27b179"
	}