Instructions to use mayacinka/Calme-Rity-stock with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mayacinka/Calme-Rity-stock with Transformers:

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

pipe = pipeline("text-generation", model="mayacinka/Calme-Rity-stock")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mayacinka/Calme-Rity-stock")
model = AutoModelForCausalLM.from_pretrained("mayacinka/Calme-Rity-stock", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mayacinka/Calme-Rity-stock with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mayacinka/Calme-Rity-stock

SGLang

How to use mayacinka/Calme-Rity-stock 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/Calme-Rity-stock" \
    --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/Calme-Rity-stock",
		"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/Calme-Rity-stock" \
        --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/Calme-Rity-stock",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mayacinka/Calme-Rity-stock with Docker Model Runner:
```
docker model run hf.co/mayacinka/Calme-Rity-stock
```

Calme-Rity-stock

File size: 1,119 Bytes

---
base_model:
- chihoonlee10/T3Q-Mistral-Orca-Math-DPO
- MaziyarPanahi/Calme-7B-Instruct-v0.9
- liminerity/M7-7b
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
---
# 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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [MaziyarPanahi/Calme-7B-Instruct-v0.9](https://huggingface.co/MaziyarPanahi/Calme-7B-Instruct-v0.9) as a base.

### Models Merged

The following models were included in the merge:
* [chihoonlee10/T3Q-Mistral-Orca-Math-DPO](https://huggingface.co/chihoonlee10/T3Q-Mistral-Orca-Math-DPO)
* [liminerity/M7-7b](https://huggingface.co/liminerity/M7-7b)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
    - model: MaziyarPanahi/Calme-7B-Instruct-v0.9
    - model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
    - model: liminerity/M7-7b
merge_method: model_stock
base_model: MaziyarPanahi/Calme-7B-Instruct-v0.9
dtype: bfloat16
```