Instructions to use BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated with Transformers:

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

pipe = pipeline("text-generation", model="BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated")
model = AutoModelForCausalLM.from_pretrained("BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated", device_map="auto")
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 BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated

SGLang

How to use BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated 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 "BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated" \
    --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": "BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated",
		"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 "BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated" \
        --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": "BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated with Docker Model Runner:
```
docker model run hf.co/BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated

NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated is a merge of the following models using LazyMergekit:

Quantised versions of this model are available in GGUF format from here Or use the following direct links:

open-llm-leaderboard results

Average	IFEval	BBH	MATH Lvl 5	GPQA	MUSR	MMLU-PRO
27.5	79.99	30.76	10.27	4.14	9.47	30.37	🤗 Open LLM Leaderboard

🧩 Configuration

models:
  - model: NousResearch/Meta-Llama-3.1-8B-Instruct
  - model: mlabonne/NeuralDaredevil-8B-abliterated
    parameters:
      density: 0.53
      weight: 0.55
  - model: grimjim/Llama-3.1-SuperNova-Lite-lorabilterated-8B
    parameters:
      density: 0.53
      weight: 0.45
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3.1-8B-Instruct
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-ablorabliterated"
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"])

Downloads last month: 25

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated

grimjim/Llama-3.1-SuperNova-Lite-lorabilterated-8B

mlabonne/NeuralDaredevil-8B-abliterated

Merge model

this model

Merges

1 model

Quantizations

3 models

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

79.990
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

30.760
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

10.270
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

4.140
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

9.470
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

30.370