Instructions to use ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5 with Transformers:

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

pipe = pipeline("text-generation", model="ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5")
model = AutoModelForMultimodalLM.from_pretrained("ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5")
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 ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5

SGLang

How to use ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5 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 "ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5" \
    --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": "ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5",
		"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 "ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5" \
        --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": "ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5 with Docker Model Runner:
```
docker model run hf.co/ArtusDev/knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5
```

knifeayumu_Behemoth-v1.2-Magnum-v4-123B_EXL3_1.8bpw_H5

File size: 1,283 Bytes

d1bd4b6
 
e37326e
 
 
d1bd4b6
 
 
 
e37326e
d1bd4b6

---
base_model:
- knifeayumu/Behemoth-v1.2-Magnum-v4-123B
base_model_relation: quantized
quantized_by: ArtusDev
library_name: transformers
tags:
- mergekit
- merge
- exl3
license: other
license_name: mrl
inference: false
license_link: https://mistral.ai/licenses/MRL-0.1.md
---
![Not Horny Enough](Behemoth-v1.2-Magnum-v4-123B.png)

# The Drummer becomes hornier (again)

A worthy successor as the v2 didn't set the expectations. SLERPd with less magnum as some people have reported it's being too horny and maybe less coherent.

This is a merge of pre-trained language models created using [mergekit](https://github.com/arcee-ai/mergekit).

## Merge Details
### Merge Method

This model was merged using the SLERP merge method.

### Models Merged

The following models were included in the merge:
* [TheDrummer/Behemoth-123B-v1.2](https://huggingface.co/TheDrummer/Behemoth-123B-v1.2)
* [anthracite-org/magnum-v4-123b](https://huggingface.co/anthracite-org/magnum-v4-123b)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: TheDrummer/Behemoth-123B-v1.2
  - model: anthracite-org/magnum-v4-123b
merge_method: slerp
base_model: TheDrummer/Behemoth-123B-v1.2
parameters:
  t: [0.1, 0.2, 0.4, 0.2, 0.1]
dtype: bfloat16
```