Instructions to use jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus with Transformers:

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

pipe = pipeline("text-generation", model="jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus")
model = AutoModelForCausalLM.from_pretrained("jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus

SGLang

How to use jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus 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 "jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus" \
    --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": "jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus",
		"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 "jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus" \
        --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": "jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus with Docker Model Runner:
```
docker model run hf.co/jvh/Mistral-NeuralBeagle14-OpenOrca-Turdus
```

Mistral-NeuralBeagle14-OpenOrca-Turdus / README.md

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	---
	base_model:
	- udkai/Turdus
	- Open-Orca/Mistral-7B-OpenOrca
	- mlabonne/NeuralBeagle14-7B
	tags:
	- mergekit
	- merge

	---
	# merge

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.

	### Models Merged

	The following models were included in the merge:
	* [udkai/Turdus](https://huggingface.co/udkai/Turdus)
	* [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)
	* [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml

	models:
	- model: mlabonne/NeuralBeagle14-7B
	parameters:
	weight: 0.55
	- model: udkai/Turdus
	parameters:
	weight: 0.15
	- model: Open-Orca/Mistral-7B-OpenOrca
	parameters:
	weight: 0.3
	merge_method: linear
	dtype: float16

	# slices:
	# - sources:
	# - model: Open-Orca/Mistral-7B-OpenOrca
	# layer_range: [0, 32]
	# - model: mlabonne/NeuralBeagle14-7B
	# layer_range: [0, 32]
	# merge_method: slerp
	# base_model: Open-Orca/Mistral-7B-OpenOrca
	# parameters:
	# t:
	# - filter: self_attn
	# value: [0, 0.5, 0.3, 0.7, 1]
	# - filter: mlp
	# value: [1, 0.5, 0.7, 0.3, 0]
	# - value: 0.5
	# dtype: bfloat16

	```