Instructions to use maxcurrent/NeuralMonarchCoderPearlBeagle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maxcurrent/NeuralMonarchCoderPearlBeagle with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maxcurrent/NeuralMonarchCoderPearlBeagle")
model = AutoModelForCausalLM.from_pretrained("maxcurrent/NeuralMonarchCoderPearlBeagle")
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 maxcurrent/NeuralMonarchCoderPearlBeagle with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maxcurrent/NeuralMonarchCoderPearlBeagle"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maxcurrent/NeuralMonarchCoderPearlBeagle

SGLang

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

Docker Model Runner
How to use maxcurrent/NeuralMonarchCoderPearlBeagle with Docker Model Runner:
```
docker model run hf.co/maxcurrent/NeuralMonarchCoderPearlBeagle
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

NeuralMonarchCoderPearlBeagle

NeuralMonarchCoderPearlBeagle is a merge of the following models using LazyMergekit:

Goals

This is a TIES merge, formed from MonarchCoder-7b (A merge of Alpha Monarch and TessCoder) and NeuralPearlBeagle(which is a merge of mlabonne's NeuralBeagle14-7b and Pearl-7B-Slerp). It is a somewhat haphazard experiment to see if we can merge more math and coding capabilities into the already outstanding NeuralBeagle14-7b and still maintain the same positive chat abilities.

If you find this or my other merges useful, please consider sending a bit of BTC so I don't have to use Google Colab :D

BTC: bc1q8lc4mzdtdyz7fx44vaw3jn8qg6w4c3ypfxpdrv

ETH/POLYGON: 0x102a6fd187db8441d2cbead33ac70e87f382f114

🧩 Configuration

models:
  - model: abideen/MonarchCoder-7B
    parameters:
      density: 0.6
      weight: 0.5
  - model: eldogbbhed/NeuralPearlBeagle
    parameters:
      density: 0.8
      weight: 0.8
merge_method: ties
base_model: eldogbbhed/NeuralPearlBeagle
parameters:
  normalize: true
  int8_mask: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "eldogbbhed/NeuralMonarchCoderPearlBeagle"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	71.50
AI2 Reasoning Challenge (25-Shot)	68.52
HellaSwag (10-Shot)	87.22
MMLU (5-Shot)	64.53
TruthfulQA (0-shot)	61.19
Winogrande (5-shot)	80.51
GSM8k (5-shot)	67.02

Downloads last month: 27

Safetensors

Model size

7B params

Tensor type

F16

Model tree for maxcurrent/NeuralMonarchCoderPearlBeagle

abideen/MonarchCoder-7B

maxcurrent/NeuralPearlBeagle

Merge model

this model

Merges

1 model

Quantizations

1 model

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

68.520
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

87.220
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.530
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

61.190
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

80.510
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

67.020