Instructions to use mayacinka/NeuralZephyr-Beagle-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mayacinka/NeuralZephyr-Beagle-7B with Transformers:

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

pipe = pipeline("text-generation", model="mayacinka/NeuralZephyr-Beagle-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mayacinka/NeuralZephyr-Beagle-7B")
model = AutoModelForCausalLM.from_pretrained("mayacinka/NeuralZephyr-Beagle-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mayacinka/NeuralZephyr-Beagle-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mayacinka/NeuralZephyr-Beagle-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mayacinka/NeuralZephyr-Beagle-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mayacinka/NeuralZephyr-Beagle-7B

SGLang

How to use mayacinka/NeuralZephyr-Beagle-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 "mayacinka/NeuralZephyr-Beagle-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mayacinka/NeuralZephyr-Beagle-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "mayacinka/NeuralZephyr-Beagle-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mayacinka/NeuralZephyr-Beagle-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mayacinka/NeuralZephyr-Beagle-7B with Docker Model Runner:
```
docker model run hf.co/mayacinka/NeuralZephyr-Beagle-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

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

Code credit: this excellent medium blog

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using CultriX/NeuralTrix-7B-dpo as a base.

Models Merged

The following models were included in the merge:

mlabonne/NeuralBeagle14-7B
HuggingFaceH4/zephyr-7b-alpha

Benchmarks

Open LLM Leaderboard

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
mayacinka/NeuralZephyr-Beagle-7B	71.57	68.6	86.38	64.67	65.17	81.14	63.46

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: CultriX/NeuralTrix-7B-dpo
  - model: HuggingFaceH4/zephyr-7b-alpha
    parameters:
      density: 0.83
      weight: 0.4
  - model: mlabonne/NeuralBeagle14-7B
    parameters: 
      density: 0.83
      weight: 0.6
merge_method: dare_ties
base_model: CultriX/NeuralTrix-7B-dpo
parameters:
  int8_mask: true
dtype: bfloat16

Inference

# pip install transformers

from transformers import AutoTokenizer
import transformers
import torch

model = "mayacinka/NeuralZephyr-Beagle-7B"
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.	71.57
AI2 Reasoning Challenge (25-Shot)	68.60
HellaSwag (10-Shot)	86.38
MMLU (5-Shot)	64.67
TruthfulQA (0-shot)	65.17
Winogrande (5-shot)	81.14
GSM8k (5-shot)	63.46