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llama3-8B-slerp-med-262k - GGUF
- Model creator: https://huggingface.co/shanchen/
- Original model: https://huggingface.co/shanchen/llama3-8B-slerp-med-262k/
Original model description:
tags: - merge - mergekit - lazymergekit - gradientai/Llama-3-8B-Instruct-262k - johnsnowlabs/JSL-MedLlama-3-8B-v1.0 base_model: - gradientai/Llama-3-8B-Instruct-262k - johnsnowlabs/JSL-MedLlama-3-8B-v1.0 license: llama3 language: - zh
llama3-8B-slerp-med-262k
llama3-8B-slerp-med-262k is a merge of the following models using LazyMergekit:
🧩 Configuration
slices:
- sources:
- model: gradientai/Llama-3-8B-Instruct-262k
layer_range: [0,32]
- model: johnsnowlabs/JSL-MedLlama-3-8B-v1.0
layer_range: [0,32]
merge_method: slerp
base_model: gradientai/Llama-3-8B-Instruct-262k
parameters:
t:
- filter: self_attn
value: [0.3, 0.5, 0.5, 0.7, 1]
- filter: mlp
value: [1, 0.7, 0.5, 0.5, 0.3]
- value: 0.5
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "shanchen/llama3-8B-slerp-med-262k"
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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