Instructions to use DreadPoor/YM-12B-Model_Stock with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DreadPoor/YM-12B-Model_Stock with Transformers:

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

pipe = pipeline("text-generation", model="DreadPoor/YM-12B-Model_Stock")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DreadPoor/YM-12B-Model_Stock")
model = AutoModelForCausalLM.from_pretrained("DreadPoor/YM-12B-Model_Stock")
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

vLLM

How to use DreadPoor/YM-12B-Model_Stock with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DreadPoor/YM-12B-Model_Stock"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DreadPoor/YM-12B-Model_Stock",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DreadPoor/YM-12B-Model_Stock

SGLang

How to use DreadPoor/YM-12B-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 "DreadPoor/YM-12B-Model_Stock" \
    --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": "DreadPoor/YM-12B-Model_Stock",
		"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 "DreadPoor/YM-12B-Model_Stock" \
        --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": "DreadPoor/YM-12B-Model_Stock",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DreadPoor/YM-12B-Model_Stock with Docker Model Runner:
```
docker model run hf.co/DreadPoor/YM-12B-Model_Stock
```

YM-12B-Model_Stock / README.md

DreadPoor

Update README.md

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	---
	base_model:
	- LatitudeGames/Wayfarer-12B
	- yamatazen/EtherealAurora-12B-v2
	- TheDrummer/Rocinante-12B-v1.1
	- nbeerbower/Lyra4-Gutenberg-12B
	- MarinaraSpaghetti/NemoMix-Unleashed-12B
	- cognitivecomputations/dolphin-2.9.3-mistral-nemo-12b
	- nothingiisreal/MN-12B-Celeste-V1.9
	- anthracite-org/magnum-v2-12b
	library_name: transformers
	tags:
	- mergekit
	- merge

	---
	# merge

	![image/gif](https://cdn-uploads.huggingface.co/production/uploads/632149f88c0da827c72dccde/onx2Y1k8HPSoVKt26xV28.gif)

	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 [yamatazen/EtherealAurora-12B-v2](https://huggingface.co/yamatazen/EtherealAurora-12B-v2) as a base.

	### Models Merged

	The following models were included in the merge:
	* [LatitudeGames/Wayfarer-12B](https://huggingface.co/LatitudeGames/Wayfarer-12B)
	* [TheDrummer/Rocinante-12B-v1.1](https://huggingface.co/TheDrummer/Rocinante-12B-v1.1)
	* [nbeerbower/Lyra4-Gutenberg-12B](https://huggingface.co/nbeerbower/Lyra4-Gutenberg-12B)
	* [MarinaraSpaghetti/NemoMix-Unleashed-12B](https://huggingface.co/MarinaraSpaghetti/NemoMix-Unleashed-12B)
	* [cognitivecomputations/dolphin-2.9.3-mistral-nemo-12b](https://huggingface.co/cognitivecomputations/dolphin-2.9.3-mistral-nemo-12b)
	* [nothingiisreal/MN-12B-Celeste-V1.9](https://huggingface.co/nothingiisreal/MN-12B-Celeste-V1.9)
	* [anthracite-org/magnum-v2-12b](https://huggingface.co/anthracite-org/magnum-v2-12b)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	models:
	- model: LatitudeGames/Wayfarer-12B
	- model: MarinaraSpaghetti/NemoMix-Unleashed-12B
	- model: nothingiisreal/MN-12B-Celeste-V1.9
	- model: TheDrummer/Rocinante-12B-v1.1
	- model: anthracite-org/magnum-v2-12b
	- model: nbeerbower/Lyra4-Gutenberg-12B
	- model: cognitivecomputations/dolphin-2.9.3-mistral-nemo-12b
	merge_method: model_stock
	base_model: yamatazen/EtherealAurora-12B-v2
	normalize: false
	int8_mask: true
	dtype: bfloat16
	```