Instructions to use LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main with Transformers:

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

pipe = pipeline("text-generation", model="LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main")
model = AutoModelForCausalLM.from_pretrained("LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main

SGLang

How to use LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main 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 "LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main" \
    --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": "LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main",
		"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 "LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main" \
        --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": "LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main with Docker Model Runner:
```
docker model run hf.co/LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main
```

2xQwen2.5-Coder-3B-Cyclops-Main

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---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-3B
tags:
- code
- humaneval
- multi-agent
- grpo
- multi-agent
- qwen2.5
library_name: transformers
pipeline_tag: text-generation
---

# 2xQwen2.5-Coder-3B-Cyclops-Main

This model is a fine-tuned version of 2 **Qwen/Qwen2.5-Coder-3B** using Multi-LLM Group Relative Policy Optimization (MLGRPO) on OpenAI HumanEval dataset.

"Cyclops" relies heavily on its `aux()` function for core implementation, while the main function adds edge case handling and refinements — just like a cyclops wielding power through its single eye.

## Model Details

- **Base Model**: Qwen/Qwen2.5-Coder-3B
- **Training Method**: MLGRPO (Multi-LLM Group Relative Policy Optimization)
- **Dataset**: HumanEval
- **Task**: Code generation with auxiliary function collaboration

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main")
model = AutoModelForCausalLM.from_pretrained("LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Main")

# Generate code
inputs = tokenizer(main_prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
main_completion = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
cleaned_main_completion = extract_specific_function(cleanup_code(main_completion), example['entry_point'])
print(cleaned_main_completion)
```

## Training Details

This model was trained as part of a multi-LLM system on the full HumanEval dataset:
- Agent 0 generates auxiliary functions to help solve coding problems
- Agent 1 generates main functions that utilize the auxiliary functions
- Both agents are trained collaboratively using MLGRPO


## Agent Role  
- This is the **Main Function Generator** agent that creates the primary solution functions.
- It will identify edge cases first and then call auxiliary functions generated by [2xQwen2.5-Coder-3B-Cyclops-Aux](https://huggingface.co/LovelyBuggies/2xQwen2.5-Coder-3B-Cyclops-Aux/) to write code more effectively.


## Citation

If you use this model, please cite:
```bibtex
Coming soon.
```