Instructions to use datatab/Yugo55A-GPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use datatab/Yugo55A-GPT with Transformers:

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

pipe = pipeline("text-generation", model="datatab/Yugo55A-GPT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("datatab/Yugo55A-GPT")
model = AutoModelForCausalLM.from_pretrained("datatab/Yugo55A-GPT", device_map="auto")
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 Settings

vLLM

How to use datatab/Yugo55A-GPT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "datatab/Yugo55A-GPT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "datatab/Yugo55A-GPT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/datatab/Yugo55A-GPT

SGLang

How to use datatab/Yugo55A-GPT 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 "datatab/Yugo55A-GPT" \
    --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": "datatab/Yugo55A-GPT",
		"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 "datatab/Yugo55A-GPT" \
        --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": "datatab/Yugo55A-GPT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use datatab/Yugo55A-GPT with Docker Model Runner:
```
docker model run hf.co/datatab/Yugo55A-GPT
```

Yugo55A-GPT

File size: 1,324 Bytes

77bdbe2

---
base_model:
- mlabonne/AlphaMonarch-7B
- datatab/Yugo55-GPT-v4
- datatab/Yugo55-GPT-DPO-v1-chkp-300
- NousResearch/Nous-Hermes-2-Mistral-7B-DPO
library_name: transformers
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:
* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
* [datatab/Yugo55-GPT-v4](https://huggingface.co/datatab/Yugo55-GPT-v4)
* [datatab/Yugo55-GPT-DPO-v1-chkp-300](https://huggingface.co/datatab/Yugo55-GPT-DPO-v1-chkp-300)
* [NousResearch/Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO)

### Configuration

The following YAML configuration was used to produce this model:

```yaml

models:
  - model: datatab/Yugo55-GPT-v4
    parameters:
      weight: 1.0
  - model: datatab/Yugo55-GPT-DPO-v1-chkp-300
    parameters:
      weight: 1.0
  - model: mlabonne/AlphaMonarch-7B
    parameters:
      weight: 0.5
  - model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
    parameters:
      weight: 0.5
merge_method: linear
dtype: float16

```