metadata
tags:
- merge
- mergekit
- lazymergekit
- SciPhi/SciPhi-Mistral-7B-32k
base_model:
- SciPhi/SciPhi-Mistral-7B-32k
SciPhi-Mistral-7B-32k-sliced
SciPhi-Mistral-7B-32k-sliced is a merge of the following models using LazyMergekit:
🧩 Configuration
slices:
- sources:
- model: SciPhi/SciPhi-Mistral-7B-32k
layer_range: [0, 3]
- sources:
- model: SciPhi/SciPhi-Mistral-7B-32k
layer_range: [5, 7]
- sources:
- model: SciPhi/SciPhi-Mistral-7B-32k
layer_range: [10, 10]
- sources:
- model: SciPhi/SciPhi-Mistral-7B-32k
layer_range: [17, 23]
- sources:
- model: SciPhi/SciPhi-Mistral-7B-32k
layer_range: [31, 32]
- sources:
- model: Locutusque/TinyMistral-248M-v2.5
layer_range: [0, 1]
- sources:
- model: Locutusque/TinyMistral-248M-v2.5
layer_range: [11, 12]
- sources:
- model: Locutusque/TinyMistral-248M-v2.5-Instruct
layer_range: [0, 1]
- sources:
- model: Locutusque/TinyMistral-248M-v2.5-Instruct
layer_range: [11, 12]
merge_method: slerp
base_model: Locutusque/TinyMistral-248M-v2.5-Instruct
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.3
dtype: float16
tokenizer_source: base
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jtatman/SciPhi-Mistral-7B-32k-sliced"
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"])