Instructions to use maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b with Transformers:

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

pipe = pipeline("text-generation", model="maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b")
model = AutoModelForCausalLM.from_pretrained("maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b")
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 maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b

SGLang

How to use maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b 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 "maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b" \
    --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": "maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b",
		"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 "maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b" \
        --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": "maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b with Docker Model Runner:
```
docker model run hf.co/maxcurrent/NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b
```

NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b

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---
license: cc-by-nc-4.0
tags:
- merge
- mergekit
- vortexmergekit
- chihoonlee10/T3Q-Mistral-Orca-Math-DPO
- eldogbbhed/NeuralMonarchCoderPearlBeagle
---

# NeuralMonarchCoderPearlBeagle-T3Q-Mistral-Orca-Math-DPO-7b

This is a merge of multiple models brought together using the awesome [VortexMerge kit](https://colab.research.google.com/drive/1YjcvCLuNG1PK7Le6_4xhVU5VpzTwvGhk#scrollTo=UG5H2TK4gVyl).

Let's see what we've got in this merge:
* [chihoonlee10/T3Q-Mistral-Orca-Math-DPO](https://huggingface.co/chihoonlee10/T3Q-Mistral-Orca-Math-DPO) 🚀
* [eldogbbhed/NeuralMonarchCoderPearlBeagle](https://huggingface.co/eldogbbhed/NeuralMonarchCoderPearlBeagle) 🚀

## 🧩 Configuration

```yaml
models:
  - model: mlabonne/NeuralBeagle14-7B
    # no parameters necessary for base model
  - model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
    parameters:
      density: 0.5
      weight: 0.5
  - model: eldogbbhed/NeuralMonarchCoderPearlBeagle
    parameters:
      density: 0.5
      weight: 0.3
merge_method: ties
base_model: mlabonne/NeuralBeagle14-7B
parameters:
  normalize: true
  int8_mask: true
dtype: float16