Instructions to use divisee/Magic-psy-LoRA-Qwen-Finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use divisee/Magic-psy-LoRA-Qwen-Finetuned with Transformers:

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

pipe = pipeline("text-generation", model="divisee/Magic-psy-LoRA-Qwen-Finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("divisee/Magic-psy-LoRA-Qwen-Finetuned")
model = AutoModelForCausalLM.from_pretrained("divisee/Magic-psy-LoRA-Qwen-Finetuned", 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 divisee/Magic-psy-LoRA-Qwen-Finetuned with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/divisee/Magic-psy-LoRA-Qwen-Finetuned

SGLang

How to use divisee/Magic-psy-LoRA-Qwen-Finetuned 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 "divisee/Magic-psy-LoRA-Qwen-Finetuned" \
    --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": "divisee/Magic-psy-LoRA-Qwen-Finetuned",
		"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 "divisee/Magic-psy-LoRA-Qwen-Finetuned" \
        --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": "divisee/Magic-psy-LoRA-Qwen-Finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use divisee/Magic-psy-LoRA-Qwen-Finetuned with Docker Model Runner:
```
docker model run hf.co/divisee/Magic-psy-LoRA-Qwen-Finetuned
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Инструктивная модель на основе Vikhr-Qwen-2.5-1.5B-Instruct, обученная на русскоязычном сгенерированном датасете. Создана для ответа на запросы в роли волшебного психолога из Хоггвартса.

Пример использования:

def generate_answer(model, tokenizer, prompt):

  inputs = tokenizer.apply_chat_template([{"role": "system", "content": BASE_SYSTEM_PROMPT},{"role": "user", "content": prompt}],
                                       add_generation_prompt=True,
                                       tokenize=True,
                                       return_tensors="pt",
                                       return_dict=True
                                       ).to('cuda')


  gen_kwargs = {"temperature":0.5,
    "top_p":0.8,
    "max_length":512,
    "repetition_penalty":1.05,
     "do_sample": True}


  with torch.no_grad():
      outputs = model.generate(**inputs, **gen_kwargs)
      outputs = outputs[:, inputs['input_ids'].shape[1]:]
      return tokenizer.decode(outputs[0], skip_special_tokens=True)

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Safetensors

Model size

2B params

Tensor type

F32

Model tree for divisee/Magic-psy-LoRA-Qwen-Finetuned

Quantizations

2 models