Instructions to use nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic with Transformers:

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

pipe = pipeline("text-generation", model="nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic")
model = AutoModelForCausalLM.from_pretrained("nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic", 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 nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic

SGLang

How to use nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic 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 "nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic" \
    --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": "nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic",
		"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 "nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic" \
        --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": "nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic with Docker Model Runner:
```
docker model run hf.co/nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic
```

Qwen2.5-7B-Instruct-Math-Code-task-arithmetic / README.md

nthehai01

Improve language tag (#1)

aa92ff9 verified over 1 year ago

preview code

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	---
	base_model:
	- Qwen/Qwen2.5-Coder-7B
	- Qwen/Qwen2.5-Math-7B
	- Qwen/Qwen2.5-7B-Instruct
	- Qwen/Qwen2.5-7B
	library_name: transformers
	tags:
	- mergekit
	- merge
	language:
	- zho
	- eng
	- fra
	- spa
	- por
	- deu
	- ita
	- rus
	- jpn
	- kor
	- vie
	- tha
	- ara
	---
	# nthehai01/Qwen2.5-7B-Instruct-Math-Code-task-arithmetic

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

	## Performance
	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|GSM8k (zero-shot) \|86.20\|
	\|HellaSwag (zero-Shot) \|49.91\|
	\|MBPP (zero-shot) \|55.20\|

	## Merge Details
	### Merge Method

	This model was merged using the [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) as a base.

	### Models Merged

	The following models were included in the merge:
	* [Qwen/Qwen2.5-Coder-7B](https://huggingface.co/Qwen/Qwen2.5-Coder-7B)
	* [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B)
	* [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	base_model: Qwen/Qwen2.5-7B
	dtype: bfloat16
	merge_method: task_arithmetic
	parameters:
	lambda: 0.5676097213578511
	normalize: 1.0
	slices:
	- sources:
	- layer_range: [0, 28]
	model: Qwen/Qwen2.5-7B
	- layer_range: [0, 28]
	model: Qwen/Qwen2.5-Math-7B
	parameters:
	weight: 0.5215841338521604
	- layer_range: [0, 28]
	model: Qwen/Qwen2.5-Coder-7B
	parameters:
	weight: 0.13680114132969845
	- layer_range: [0, 28]
	model: Qwen/Qwen2.5-7B-Instruct
	parameters:
	weight: 0.8507353075455186
	```