Instructions to use Nin8520/Qwen2.5-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nin8520/Qwen2.5-7B with Transformers:

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

pipe = pipeline("text-generation", model="Nin8520/Qwen2.5-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nin8520/Qwen2.5-7B")
model = AutoModelForCausalLM.from_pretrained("Nin8520/Qwen2.5-7B", 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 Nin8520/Qwen2.5-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nin8520/Qwen2.5-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nin8520/Qwen2.5-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nin8520/Qwen2.5-7B

SGLang

How to use Nin8520/Qwen2.5-7B 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 "Nin8520/Qwen2.5-7B" \
    --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": "Nin8520/Qwen2.5-7B",
		"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 "Nin8520/Qwen2.5-7B" \
        --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": "Nin8520/Qwen2.5-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nin8520/Qwen2.5-7B with Docker Model Runner:
```
docker model run hf.co/Nin8520/Qwen2.5-7B
```

Model Details

Base Model: Qwen2.5-7B-Instruct

Training Details

Training Data

hon9kon9ize/yue-alpaca

Nin8520/words

Training Setting

args = dict(
 stage="sft",                                               
 do_train=True,
 model_name_or_path="Qwen/Qwen2.5-7B-Instruct", 
 dataset="yue_1,yue_2",          
 template="alpaca",                                         
 finetuning_type="lora",                                    
 lora_target="all",                                         
 output_dir="Qwen2.5_lora",                                 
 per_device_train_batch_size=2,                             
 gradient_accumulation_steps=4,                             
 lr_scheduler_type="cosine",                                
 logging_steps=5,                                           
 warmup_ratio=0.1,                                          
 save_steps=1000,                                           
 learning_rate=5e-5,                                        
 num_train_epochs=1.0,                                      
 max_samples=300,                                           
 max_grad_norm=1.0,                                         
 loraplus_lr_ratio=16.0,                                    
 fp16=True,                                                 
 report_to="none",                                          
)

Evaluation

Not done

Downloads last month: 9

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Nin8520/Qwen2.5-7B

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

(3026)

this model

Nin8520
/

Qwen2.5-7B