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ryan0712_-_llama-3-8b-slow-DUS-random-layer2-method2-4bits
RichardErkhov/ryan0712_-_llama-3-8b-slow-DUS-random-layer2-method2-4bits
ryan0712_-_llama-3-8b-slow-DUS-random-layer2-method2-4bits is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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.safetensors4.1 GB · 100%
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How the weights are stored.
U83.5B · 76%
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
llama-3-8b-slow-DUS-random-layer2-method2 - bnb 4bits
- Model creator: https://huggingface.co/ryan0712/
- Original model: https://huggingface.co/ryan0712/llama-3-8b-slow-DUS-random-layer2-method2/
Original model description:
tags:
- merge
- mergekit
- lazymergekit
- NousResearch/Meta-Llama-3-8B base_model:
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
llama-3-8b-slow-DUS-random-layer2-method2
llama-3-8b-slow-DUS-random-layer2-method2 is a merge of the following models using LazyMergekit:
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
🧩 Configuration
slices:
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [4, 5]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [6, 7]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [8, 9]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [10, 11]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [11, 12]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [12, 13]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [14, 15]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [16, 17]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [17, 18]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [20, 21]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [23, 24]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [25, 26]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [26, 27]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [28, 29]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [29, 30]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [31, 32]
merge_method: passthrough
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ryan0712/llama-3-8b-slow-DUS-random-layer2-method2"
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"])
- Type
- llama
- Created
- Mar 8, 2025
- Updated
- Mar 8, 2025