Instructions to use guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5 with Transformers:

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

pipe = pipeline("text-generation", model="guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5")
model = AutoModelForMultimodalLM.from_pretrained("guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5

SGLang

How to use guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5 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 "guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5 with Docker Model Runner:
```
docker model run hf.co/guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5
```

medical-o1-sft-full-1e-5

This model is a fine-tuned version of Qwen/Qwen3-1.7B on the medical_o1_train dataset. It achieves the following results on the evaluation set:

Loss: 1.4197

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: 1e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.05
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.5150	0.3419	50	1.4871
1.4362	0.6838	100	1.4490
1.3921	1.0205	150	1.4327
1.3675	1.3624	200	1.4269
1.3790	1.7043	250	1.4203
1.3200	2.0410	300	1.4197
1.3762	2.3829	350	1.4212
1.2864	2.7248	400	1.4206

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

Downloads last month: 1

Safetensors

Model size

2B params

Tensor type

F32

Model tree for guangyangnlp/Qwen3-1.7B-SFT-medical-1e-5

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