MinLlama-3-8B-instruct-pass
MinLlama-3-8B-instruct-pass is a merge of the following models using LazyMergekit:
- NousResearch/Meta-Llama-3-8B-Instruct
- NousResearch/Meta-Llama-3-8B-Instruct
- NousResearch/Meta-Llama-3-8B-Instruct
- NousResearch/Meta-Llama-3-8B-Instruct
- NousResearch/Meta-Llama-3-8B-Instruct
- NousResearch/Meta-Llama-3-8B-Instruct
🧩 Configuration
slices:
- sources:
- model: NousResearch/Meta-Llama-3-8B-Instruct
layer_range: [0, 8]
- sources:
- model: NousResearch/Meta-Llama-3-8B-Instruct
layer_range: [10, 12]
- sources:
- model: NousResearch/Meta-Llama-3-8B-Instruct
layer_range: [14, 18]
- sources:
- model: NousResearch/Meta-Llama-3-8B-Instruct
layer_range: [20, 22]
- sources:
- model: NousResearch/Meta-Llama-3-8B-Instruct
layer_range: [24, 26]
- sources:
- model: NousResearch/Meta-Llama-3-8B-Instruct
layer_range: [28, 32]
merge_method: passthrough
base_model: NousResearch/Meta-Llama-3-8B-Instruct
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "JoPmt/MinLlama-3-8B-instruct-pass"
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"])