Instructions to use Kamka-IT/shadow-clown-BioMistral-7B-SLERP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kamka-IT/shadow-clown-BioMistral-7B-SLERP with Transformers:

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

pipe = pipeline("text-generation", model="Kamka-IT/shadow-clown-BioMistral-7B-SLERP")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kamka-IT/shadow-clown-BioMistral-7B-SLERP")
model = AutoModelForCausalLM.from_pretrained("Kamka-IT/shadow-clown-BioMistral-7B-SLERP")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kamka-IT/shadow-clown-BioMistral-7B-SLERP with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Kamka-IT/shadow-clown-BioMistral-7B-SLERP

SGLang

How to use Kamka-IT/shadow-clown-BioMistral-7B-SLERP 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 "Kamka-IT/shadow-clown-BioMistral-7B-SLERP" \
    --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": "Kamka-IT/shadow-clown-BioMistral-7B-SLERP",
		"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 "Kamka-IT/shadow-clown-BioMistral-7B-SLERP" \
        --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": "Kamka-IT/shadow-clown-BioMistral-7B-SLERP",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Kamka-IT/shadow-clown-BioMistral-7B-SLERP with Docker Model Runner:
```
docker model run hf.co/Kamka-IT/shadow-clown-BioMistral-7B-SLERP
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

shadow-clown-BioMistral-7B-SLERP

shadow-clown-BioMistral-7B-SLERP is a merge of the following models using mergekit:

🧩 Configuration

```yaml slices:

sources:
- model: CorticalStack/shadow-clown-7B-dare layer_range: [0, 32]
- model: BioMistral/BioMistral-7B-DARE layer_range: [0, 32]

merge_method: slerp base_model: CorticalStack/shadow-clown-7B-dare parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: float16 ```

Downloads last month: 30

Safetensors

Model size

7B params

Tensor type

F16

Model tree for Kamka-IT/shadow-clown-BioMistral-7B-SLERP

Quantizations

1 model