Instructions to use s5252ccc/question-qwen-7B-intention with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use s5252ccc/question-qwen-7B-intention with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "s5252ccc/question-qwen-7B-intention")

Transformers

How to use s5252ccc/question-qwen-7B-intention with Transformers:

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

pipe = pipeline("text-generation", model="s5252ccc/question-qwen-7B-intention")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("s5252ccc/question-qwen-7B-intention", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use s5252ccc/question-qwen-7B-intention with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "s5252ccc/question-qwen-7B-intention"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "s5252ccc/question-qwen-7B-intention",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/s5252ccc/question-qwen-7B-intention

SGLang

How to use s5252ccc/question-qwen-7B-intention 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 "s5252ccc/question-qwen-7B-intention" \
    --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": "s5252ccc/question-qwen-7B-intention",
		"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 "s5252ccc/question-qwen-7B-intention" \
        --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": "s5252ccc/question-qwen-7B-intention",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use s5252ccc/question-qwen-7B-intention with Docker Model Runner:
```
docker model run hf.co/s5252ccc/question-qwen-7B-intention
```

checkpoints_qwen_7b

This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.9091

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.9630	0.3571	50	0.9958
0.9128	0.7143	100	0.9675
0.9323	1.0714	150	0.9503
0.9678	1.4286	200	0.9376
0.9188	1.7857	250	0.9264
0.9122	2.1429	300	0.9206
0.7815	2.5	350	0.9145
0.8959	2.8571	400	0.9091

Framework versions

PEFT 0.18.1
Transformers 5.1.0
Pytorch 2.10.0+cu128
Datasets 4.5.0
Tokenizers 0.22.1

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Model tree for s5252ccc/question-qwen-7B-intention

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

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