Exllamav2 quant (exl2 / 4.0 bpw) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
Quant | Model Size | lm_head |
---|---|---|
Daredevil-8B
tl;dr: It looks like a successful merge
Daredevil-8B is a merge of the following models using LazyMergekit:
- nbeerbower/llama-3-stella-8B
- Hastagaras/llama-3-8b-okay
- nbeerbower/llama-3-gutenberg-8B
- openchat/openchat-3.6-8b-20240522
- Kukedlc/NeuralLLaMa-3-8b-DT-v0.1
- cstr/llama3-8b-spaetzle-v20
- mlabonne/ChimeraLlama-3-8B-v3
- flammenai/Mahou-1.1-llama3-8B
- KingNish/KingNish-Llama3-8b
π Applications
It is a highly functional censored model. You might want to add <end_of_turn>
as an additional stop string.
β‘ Quantization
π Evaluation
Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
---|---|---|---|---|---|
mlabonne/Daredevil-8B π | 55.87 | 44.13 | 73.52 | 59.05 | 46.77 |
mlabonne/ChimeraLlama-3-8B π | 51.58 | 39.12 | 71.81 | 52.4 | 42.98 |
meta-llama/Meta-Llama-3-8B-Instruct π | 51.34 | 41.22 | 69.86 | 51.65 | 42.64 |
meta-llama/Meta-Llama-3-8B π | 45.42 | 31.1 | 69.95 | 43.91 | 36.7 |
π³ Model family tree
𧩠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
π» 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
- 12
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.