Instructions to use nthehai01/Qwen2.5-7B-Instruct-Math-Code-breadcrumbs 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-breadcrumbs 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-breadcrumbs", device_map="auto")
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-breadcrumbs")
model = AutoModelForCausalLM.from_pretrained("nthehai01/Qwen2.5-7B-Instruct-Math-Code-breadcrumbs", 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-breadcrumbs 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-breadcrumbs"
# 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-breadcrumbs",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

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

nthehai01

Improve language tag (#1)

470f90e verified about 1 year ago

preview code

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metadata

base_model:
  - Qwen/Qwen2.5-7B
  - Qwen/Qwen2.5-Coder-7B
  - Qwen/Qwen2.5-7B-Instruct
  - Qwen/Qwen2.5-Math-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-breadcrumbs

This is a merge of pre-trained language models created using mergekit.

Performance

Metric	Value
GSM8k (zero-shot)	90.06
HellaSwag (zero-Shot)	82.77
MBPP (zero-shot)	62.21

Merge Details

Merge Method

This model was merged using the Model Breadcrumbs merge method using Qwen/Qwen2.5-7B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: Qwen/Qwen2.5-7B
dtype: bfloat16
merge_method: breadcrumbs
parameters:
  lambda: 0.9075603207928135
  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:
      density: 0.11722197443445775
      gamma: 0.07547691839721048
      weight: 0.17267293536872041
  - layer_range: [0, 28]
    model: Qwen/Qwen2.5-Coder-7B
    parameters:
      density: 0.48352747334554935
      gamma: 0.0753405327865558
      weight: 0.11164770709858211
  - layer_range: [0, 28]
    model: Qwen/Qwen2.5-7B-Instruct
    parameters:
      density: 0.8190520808683315
      gamma: 0.022307694128235696
      weight: 0.7626295102691242