Instructions to use canbingol/qwen3-4B-Instruct-2507-conversational-tool-call with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use canbingol/qwen3-4B-Instruct-2507-conversational-tool-call with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")
model = PeftModel.from_pretrained(base_model, "canbingol/qwen3-4B-Instruct-2507-conversational-tool-call")

Transformers

How to use canbingol/qwen3-4B-Instruct-2507-conversational-tool-call with Transformers:

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

pipe = pipeline("text-generation", model="canbingol/qwen3-4B-Instruct-2507-conversational-tool-call")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("canbingol/qwen3-4B-Instruct-2507-conversational-tool-call", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use canbingol/qwen3-4B-Instruct-2507-conversational-tool-call with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "canbingol/qwen3-4B-Instruct-2507-conversational-tool-call"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "canbingol/qwen3-4B-Instruct-2507-conversational-tool-call",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/canbingol/qwen3-4B-Instruct-2507-conversational-tool-call

SGLang

How to use canbingol/qwen3-4B-Instruct-2507-conversational-tool-call 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 "canbingol/qwen3-4B-Instruct-2507-conversational-tool-call" \
    --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": "canbingol/qwen3-4B-Instruct-2507-conversational-tool-call",
		"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 "canbingol/qwen3-4B-Instruct-2507-conversational-tool-call" \
        --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": "canbingol/qwen3-4B-Instruct-2507-conversational-tool-call",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use canbingol/qwen3-4B-Instruct-2507-conversational-tool-call with Docker Model Runner:
```
docker model run hf.co/canbingol/qwen3-4B-Instruct-2507-conversational-tool-call
```

qwen3-4B-Instruct-2507-conversational-tool-call

This model is a fine-tuned version of Qwen/Qwen3-4B-Instruct-2507 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1739

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: 0.0002
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 64
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
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.2316	0.2980	20	0.1907
0.1901	0.5959	40	0.1782
0.1755	0.8939	60	0.1740
0.1755	1.0	68	0.1739

Framework versions

PEFT 0.20.0
Transformers 5.15.0
Pytorch 2.11.0+cu128
Datasets 5.0.1
Tokenizers 0.22.2

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