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

Libraries

How to use thlurte/Qwen2.5-CodeMath-1.5B-SLERP with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

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

Use Docker

docker model run hf.co/thlurte/Qwen2.5-CodeMath-1.5B-SLERP

SGLang

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

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

Qwen2.5-CodeMath-1.5B-SLERP

File size: 1,129 Bytes

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---
base_model:
- Qwen/Qwen2.5-Math-1.5B
- Qwen/Qwen2.5-Coder-1.5B
library_name: transformers
tags:
- mergekit
- merge

---
# Qwen2.5-CodeMath-1.5B-SLERP

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.

### Models Merged

The following models were included in the merge:
* [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B)
* [Qwen/Qwen2.5-Coder-1.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
base_model: Qwen/Qwen2.5-Coder-1.5B
dtype: bfloat16
merge_method: slerp
modules:
  default:
    slices:
    - sources:
      - layer_range: [0, 28]
        model: Qwen/Qwen2.5-Coder-1.5B
      - layer_range: [0, 28]
        model: Qwen/Qwen2.5-Math-1.5B
parameters:
  t:
  - filter: self_attn
    value: [0.15, 0.2, 0.25, 0.3, 0.35]
  - filter: mlp
    value: 0.25
  - value: 0.3
tokenizer_source: Qwen/Qwen2.5-Coder-1.5B
```