Instructions to use saishf/Neural-SOVLish-Devil-8B-L3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saishf/Neural-SOVLish-Devil-8B-L3 with Transformers:

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

pipe = pipeline("text-generation", model="saishf/Neural-SOVLish-Devil-8B-L3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("saishf/Neural-SOVLish-Devil-8B-L3")
model = AutoModelForCausalLM.from_pretrained("saishf/Neural-SOVLish-Devil-8B-L3")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use saishf/Neural-SOVLish-Devil-8B-L3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saishf/Neural-SOVLish-Devil-8B-L3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saishf/Neural-SOVLish-Devil-8B-L3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/saishf/Neural-SOVLish-Devil-8B-L3

SGLang

How to use saishf/Neural-SOVLish-Devil-8B-L3 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 "saishf/Neural-SOVLish-Devil-8B-L3" \
    --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": "saishf/Neural-SOVLish-Devil-8B-L3",
		"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 "saishf/Neural-SOVLish-Devil-8B-L3" \
        --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": "saishf/Neural-SOVLish-Devil-8B-L3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use saishf/Neural-SOVLish-Devil-8B-L3 with Docker Model Runner:
```
docker model run hf.co/saishf/Neural-SOVLish-Devil-8B-L3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

This is another "SOVL" style merge, this time using mlabonne/NeuralDaredevil-8B-abliterated.

Daredevil is the first abliterated model series i've tried that feels as smart as base llama-3-instruct while also being willing to give instructions to do all kinda of illegal things

Neural daredevil is trained further on the original abliterated model, which should result in a better experience in most scenarios. (A bandaid for the damage abliteration causes)

This model should do well in rp, I'm yet to test it (waiting for gguf files @_@)

Merge Method

This model was merged using the Model Stock merge method using mlabonne/NeuralDaredevil-8B-abliterated as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: mlabonne/NeuralDaredevil-8B-abliterated+ResplendentAI/Aura_Llama3
  - model: mlabonne/NeuralDaredevil-8B-abliterated+ResplendentAI/Smarts_Llama3
  - model: mlabonne/NeuralDaredevil-8B-abliterated+ResplendentAI/Luna_Llama3
  - model: mlabonne/NeuralDaredevil-8B-abliterated+ResplendentAI/BlueMoon_Llama3
  - model: mlabonne/NeuralDaredevil-8B-abliterated+ResplendentAI/RP_Format_QuoteAsterisk_Llama3
merge_method: model_stock
base_model: mlabonne/NeuralDaredevil-8B-abliterated
dtype: bfloat16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	72.22
AI2 Reasoning Challenge (25-Shot)	69.11
HellaSwag (10-Shot)	84.77
MMLU (5-Shot)	69.02
TruthfulQA (0-shot)	59.05
Winogrande (5-shot)	78.30
GSM8k (5-shot)	73.09