Instructions to use Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2 with Transformers:

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

pipe = pipeline("text-generation", model="Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2")
model = AutoModelForCausalLM.from_pretrained("Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2

SGLang

How to use Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2 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 "Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2" \
    --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": "Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2",
		"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 "Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2" \
        --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": "Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2 with Docker Model Runner:
```
docker model run hf.co/Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2
```

Configuration Parsing Warning:In config.json: "quantization_config.bits" must be an integer

Exllamav2 quant (exl2 / 3.75 bpw) made with ExLlamaV2 v0.0.21

Other EXL2 quants:

Quant	Model Size	lm_head
2.2	3250 MB	6
2.5	3478 MB	6
3.0	3894 MB	6
3.5	4311 MB	6
3.75	4518 MB	6
4.0	4727 MB	6
4.25	4935 MB	6
5.0	5556 MB	6
6.0	6497 MB	8
6.5	6893 MB	8
8.0	8125 MB	8

Daredevil-8B

tl;dr: It looks like a successful merge

Daredevil-8B is a merge of the following models using LazyMergekit:

🔎 Applications

It is a highly functional censored model. You might want to add <end_of_turn> as an additional stop string.

⚡ Quantization

GGUF: https://huggingface.co/mlabonne/Daredevil-8B-GGUF

🏆 Evaluation

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
mlabonne/Daredevil-8B 📄	55.87	44.13	73.52	59.05	46.77
mlabonne/ChimeraLlama-3-8B 📄	51.58	39.12	71.81	52.4	42.98
meta-llama/Meta-Llama-3-8B-Instruct 📄	51.34	41.22	69.86	51.65	42.64
meta-llama/Meta-Llama-3-8B 📄	45.42	31.1	69.95	43.91	36.7

🌳 Model family tree

🧩 Configuration

models:
  - model: NousResearch/Meta-Llama-3-8B
    # No parameters necessary for base model
  - model: nbeerbower/llama-3-stella-8B
    parameters:
      density: 0.6
      weight: 0.16
  - model: Hastagaras/llama-3-8b-okay
    parameters:
      density: 0.56
      weight: 0.1
  - model: nbeerbower/llama-3-gutenberg-8B
    parameters:
      density: 0.6
      weight: 0.18
  - model: openchat/openchat-3.6-8b-20240522
    parameters:
      density: 0.56
      weight: 0.12
  - model: Kukedlc/NeuralLLaMa-3-8b-DT-v0.1
    parameters:
      density: 0.58
      weight: 0.18
  - model: cstr/llama3-8b-spaetzle-v20
    parameters:
      density: 0.56
      weight: 0.08
  - model: mlabonne/ChimeraLlama-3-8B-v3
    parameters:
      density: 0.56
      weight: 0.08
  - model: flammenai/Mahou-1.1-llama3-8B
    parameters:
      density: 0.55
      weight: 0.05
  - model: KingNish/KingNish-Llama3-8b
    parameters:
      density: 0.55
      weight: 0.05
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Daredevil-8B"
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: 6

Model tree for Zoyd/mlabonne_Daredevil-8B-3_75bpw_exl2

Kukedlc/NeuralLLaMa-3-8b-DT-v0.1

cstr/llama3-8b-spaetzle-v20

flammenai/Mahou-1.1-llama3-8B

mlabonne/ChimeraLlama-3-8B-v3

nbeerbower/llama-3-gutenberg-8B

nbeerbower/llama-3-stella-8B

openchat/openchat-3.6-8b-20240522

Merge model

this model