Instructions to use automerger/Inex12Experiment26-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use automerger/Inex12Experiment26-7B with Transformers:

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

pipe = pipeline("text-generation", model="automerger/Inex12Experiment26-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("automerger/Inex12Experiment26-7B")
model = AutoModelForCausalLM.from_pretrained("automerger/Inex12Experiment26-7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use automerger/Inex12Experiment26-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "automerger/Inex12Experiment26-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "automerger/Inex12Experiment26-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/automerger/Inex12Experiment26-7B

SGLang

How to use automerger/Inex12Experiment26-7B 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 "automerger/Inex12Experiment26-7B" \
    --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": "automerger/Inex12Experiment26-7B",
		"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 "automerger/Inex12Experiment26-7B" \
        --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": "automerger/Inex12Experiment26-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use automerger/Inex12Experiment26-7B with Docker Model Runner:
```
docker model run hf.co/automerger/Inex12Experiment26-7B
```

Inex12Experiment26-7B / README.md

automerger

Upload folder using huggingface_hub

5f6e07c verified over 2 years ago

preview code

Raw

History Blame Contribute Delete

1.35 kB

	---
	license: apache-2.0
	tags:
	- merge
	- mergekit
	- lazymergekit
	- automerger
	base_model:
	- yam-peleg/Experiment26-7B
	---

	# Inex12Experiment26-7B

	Inex12Experiment26-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
	* [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B)

	## 🧩 Configuration

	```yaml
	models:
	- model: MSL7/INEX12-7b
	# No parameters necessary for base model
	- model: yam-peleg/Experiment26-7B
	parameters:
	density: 0.53
	weight: 0.6
	merge_method: dare_ties
	base_model: MSL7/INEX12-7b
	parameters:
	int8_mask: true
	dtype: bfloat16
	random_seed: 0
	```

	## 💻 Usage

	```python
	!pip install -qU transformers accelerate

	from transformers import AutoTokenizer
	import transformers
	import torch

	model = "automerger/Inex12Experiment26-7B"
	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"])
	```