Instructions to use iromu/Qwen2.5-1.5B-tools with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iromu/Qwen2.5-1.5B-tools with Transformers:

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

pipe = pipeline("text-generation", model="iromu/Qwen2.5-1.5B-tools")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iromu/Qwen2.5-1.5B-tools")
model = AutoModelForCausalLM.from_pretrained("iromu/Qwen2.5-1.5B-tools", 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 iromu/Qwen2.5-1.5B-tools with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iromu/Qwen2.5-1.5B-tools"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iromu/Qwen2.5-1.5B-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/iromu/Qwen2.5-1.5B-tools

SGLang

How to use iromu/Qwen2.5-1.5B-tools 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 "iromu/Qwen2.5-1.5B-tools" \
    --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": "iromu/Qwen2.5-1.5B-tools",
		"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 "iromu/Qwen2.5-1.5B-tools" \
        --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": "iromu/Qwen2.5-1.5B-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use iromu/Qwen2.5-1.5B-tools with Docker Model Runner:
```
docker model run hf.co/iromu/Qwen2.5-1.5B-tools
```

Qwen2.5-1.5B-tools

Merged BF16 weights of the LoRA tool-calling fine-tune of unsloth/Qwen2.5-1.5B-Instruct. Built by scripts/build_tools_gguf.sh Qwen2.5-1.5B-tools (merge_and_unload of the latest checkpoint in checkpoints/Qwen2.5-1.5B-tools/).

Training

Recipe: TrainFinetuneRecipeForNextTokenPrediction (NeMo AutoModel 0.5.0)
Config: configs/qwen25_1.5b_tools.yaml
LoRA: dim 32, alpha 32, dropout 0.05, target_modules *.proj
Data: sft_tools split of r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation
Learning rate: 1e-5 (cosine decay), 501 steps, global batch 64, seq length 4096

Sibling artifacts

GGUF quantizations: models/Qwen2.5-1.5B-tools-GGUF/
NVFP4 checkpoint: models/Qwen2.5-1.5B-tools-NVFP4/

Validation matrix

Tool-calling validation on the sft_tools validation split (greedy decoding, 384 max new tokens). Throughput is single-stream greedy decode, not serving throughput.

Pretrained base (unsloth/Qwen2.5-1.5B-Instruct): 8.8% exact-args match (24/274). Fine-tuned (BF16): 0.7% exact-args match (2/274) (-8pp vs base).

GGUF-BF16: 2/274 (0.7%) exact, 88.6 tok/s — 100% of BF16.
GGUF-Q4_K_M: 3/274 (1.1%) exact, 142.9 tok/s — 150% of BF16.
GGUF-Q5_K_M: 6/274 (2.2%) exact, 131.4 tok/s — 300% of BF16.
GGUF-Q8_0: 2/274 (0.7%) exact, 115.3 tok/s — 100% of BF16.

Model	Quant	n	Tool call emitted	Names match	Exact args match	Δ exact vs BASE	tok/s
Qwen2.5-1.5B-tools	BASE (unsloth/Qwen2.5-1.5B-Instruct)	274	272/274 (99.3%)	59/274 (21.5%)	24/274 (8.8%)	—	40.6
Qwen2.5-1.5B-tools	BF16	274	258/274 (94.2%)	216/274 (78.8%)	2/274 (0.7%)	-8pp	38.0
Qwen2.5-1.5B-tools	GGUF-BF16	274	272/274 (99.3%)	167/274 (60.9%)	2/274 (0.7%)	-8pp	88.6
Qwen2.5-1.5B-tools	GGUF-Q4_K_M	274	248/274 (90.5%)	49/274 (17.9%)	3/274 (1.1%)	-7.7pp	142.9
Qwen2.5-1.5B-tools	GGUF-Q5_K_M	274	260/274 (94.9%)	79/274 (28.8%)	6/274 (2.2%)	-6.6pp	131.4
Qwen2.5-1.5B-tools	GGUF-Q8_0	274	271/274 (98.9%)	187/274 (68.2%)	2/274 (0.7%)	-8pp	115.3

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for iromu/Qwen2.5-1.5B-tools

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Finetuned

unsloth/Qwen2.5-1.5B-Instruct

Adapter

(500)

this model

iromu
/

Qwen2.5-1.5B-tools

Qwen2.5-1.5B-tools

Training

Sibling artifacts

Validation matrix

Model tree for iromu/Qwen2.5-1.5B-tools

Dataset used to train iromu/Qwen2.5-1.5B-tools