llama-3-8b-slow-DUS-layer1-method2
llama-3-8b-slow-DUS-layer1-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
𧩠Configuration
slices:
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [0, 1]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [1, 2]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [2, 3]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [3, 4]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [4, 5]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [5, 6]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [6, 7]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [7, 8]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [8, 9]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [9, 10]
- 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: [13, 14]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [14, 15]
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [30, 31]
merge_method: passthrough
dtype: bfloat16
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ryan0712/llama-3-8b-slow-DUS-layer1-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"])
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