Instructions to use varox34/7B-Model_Stock with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use varox34/7B-Model_Stock with Transformers:

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

pipe = pipeline("text-generation", model="varox34/7B-Model_Stock")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("varox34/7B-Model_Stock")
model = AutoModelForCausalLM.from_pretrained("varox34/7B-Model_Stock", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use varox34/7B-Model_Stock with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "varox34/7B-Model_Stock"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "varox34/7B-Model_Stock",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/varox34/7B-Model_Stock

SGLang

How to use varox34/7B-Model_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 "varox34/7B-Model_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": "varox34/7B-Model_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 "varox34/7B-Model_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": "varox34/7B-Model_Stock",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use varox34/7B-Model_Stock with Docker Model Runner:
```
docker model run hf.co/varox34/7B-Model_Stock
```

7B-Model_Stock

File size: 1,201 Bytes

---
base_model:
- Undi95/Toppy-M-7B
- SanjiWatsuki/Kunoichi-DPO-v2-7B
- Epiculous/Fett-uccine-7B
- NeverSleep/Noromaid-7B-0.4-DPO
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 [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) as a base.

### Models Merged

The following models were included in the merge:
* [Undi95/Toppy-M-7B](https://huggingface.co/Undi95/Toppy-M-7B)
* [Epiculous/Fett-uccine-7B](https://huggingface.co/Epiculous/Fett-uccine-7B)
* [NeverSleep/Noromaid-7B-0.4-DPO](https://huggingface.co/NeverSleep/Noromaid-7B-0.4-DPO)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: NeverSleep/Noromaid-7B-0.4-DPO
  - model: SanjiWatsuki/Kunoichi-DPO-v2-7B
  - model: Undi95/Toppy-M-7B
  - model: Epiculous/Fett-uccine-7B
merge_method: model_stock
base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
dtype: bfloat16
```