Instructions to use marcuscedricridia/Cheng-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use marcuscedricridia/Cheng-1 with Transformers:

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

pipe = pipeline("text-generation", model="marcuscedricridia/Cheng-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("marcuscedricridia/Cheng-1")
model = AutoModelForCausalLM.from_pretrained("marcuscedricridia/Cheng-1")
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

vLLM

How to use marcuscedricridia/Cheng-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "marcuscedricridia/Cheng-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "marcuscedricridia/Cheng-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/marcuscedricridia/Cheng-1

SGLang

How to use marcuscedricridia/Cheng-1 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 "marcuscedricridia/Cheng-1" \
    --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": "marcuscedricridia/Cheng-1",
		"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 "marcuscedricridia/Cheng-1" \
        --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": "marcuscedricridia/Cheng-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use marcuscedricridia/Cheng-1 with Docker Model Runner:
```
docker model run hf.co/marcuscedricridia/Cheng-1
```

Cheng-1: Multi-Specialty Merged Language Model

Model Overview

Cheng-1 is a high-performance language model created through strategic merging of top-tier, pre-existing fine-tuned models. It excels in coding, math, translation, and roleplay without requiring additional fine-tuning. The final model was built using the model_stock method with a restore model to maintain strong instruction-following and mathematical abilities.

Development Process

1. Foundation Model - "Yell-Qwen2.5-7B-1M"

Base Merge: Combined Qwen2.5-7B-Instruct-1M with Qwen2.5-7B using SCE merging.
Purpose: Established a strong general-purpose foundation for later merges.

Merge Code:

merge_method: sce  
models:
  - model: Qwen/Qwen2.5-7B-Instruct-1M
  - model: Qwen/Qwen2.5-7B  
base_model: Qwen/Qwen2.5-7B-Instruct-1M  
parameters:  
  select_topk: 1  
dtype: bfloat16  
tokenizer_source: base  
normalize: true  
int8_mask: true  
name: Yell-Qwen2.5-7B-1M

2. Domain-Specific Merges

Coding: Merged AceCoder-Qwen2.5-7B-Ins-Rule with Yell-Qwen2.5-7B-1M.
Translation: Merged DRT-7B with Yell-Qwen2.5-7B-1M.
Math: Merged AceMath-7B-Instruct with Yell-Qwen2.5-7B-1M.
Method: All three were merged using della merging, producing three intermediate models.

Merge Code:

merge_method: della  
base_model: marcuscedricridia/Yell-Qwen2.5-7B-1M 
models:  
  - model: TIGER-Lab/AceCoder-Qwen2.5-7B-Ins-Rule 
    parameters:  
      density: 1  
      weight: 1  
      lambda: 0.9  
  - model: Krystalan/DRT-7B
    parameters:  
      density: 1  
      weight: 1  
      lambda: 0.9  
  - model: nvidia/AceMath-7B-Instruct
    parameters:  
      density: 1  
      weight: 1  
      lambda: 0.9  
parameters:  
  density: 1  
  weight: 1  
  lambda: 0.9  
  normalize: true  
  int8_mask: true  
dtype: bfloat16  
tokenizer_source: base  
name: Cheng-1

3. Final Model Stock Merge

Models Combined:
- mergekit-della-wpunuct
- mergekit-della-phphmhr
- mergekit-della-qejrhsk
- Hush-Qwen2.5-7B-RP-v1.2-1M (Roleplay model)
Base Model: YOYO-AI/Qwen2.5-7B-it-restore
Final Method: Used model_stock merging to integrate all models into Cheng-1.

Merge Code:

merge_method: model_stock  
base_model: YOYO-AI/Qwen2.5-7B-it-restore  
models:  
  - model: marcuscedricridia/mergekit-della-wpunuct
  - model: marcuscedricridia/mergekit-della-phphmhr
  - model: marcuscedricridia/mergekit-della-qejrhsk
  - model: marcuscedricridia/Hush-Qwen2.5-7B-RP-v1.2-1M  
dtype: bfloat16  
tokenizer_source: base  
int8_mask: true  
normalize: true  
name: Cheng-1

Benchmarks

Model: marcuscedricridia/Cheng-1
Precision: torch.bfloat16
Revision: cd8c9dd37c67c2e1b7c683fdd5e72b7f08c074b9

Average: 36.06
IFEval: 77.89
BBH: 36.54
MATH: 48.94
GPQA: 6.15
MUSR: 9.62
MMLU-PRO: 37.21

Conclusion

Cheng-1 is a versatile model optimized for multiple domains. By merging top-performing models in coding, math, translation, and roleplay, it achieves balanced and strong benchmark results without direct fine-tuning.

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Safetensors

Model size

8B params

Tensor type

BF16