metadata
tags:
- merge
- mergekit
- lazymergekit
- FelixChao/WestSeverus-7B-DPO-v2
- jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B
- mlabonne/Daredevil-7B
base_model:
- FelixChao/WestSeverus-7B-DPO-v2
- jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B
- mlabonne/Daredevil-7B
WONMSeverusDevilv2-TIES
WONMSeverusDevilv2-TIES is a merge of the following models using LazyMergekit:
🧩 Configuration
models:
- model: FelixChao/WestSeverus-7B-DPO-v2
parameters:
density: [1, 0.7, 0.1]
weight: [0, 0.3, 0.7, 1]
- model: jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B
parameters:
density: [1, 0.7, 0.3]
weight: [0, 0.25, 0.5, 1]
- model: mlabonne/Daredevil-7B
parameters:
density: 0.33
weight:
- filter: mlp
value: [0.35, 0.65]
- value: 0
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
normalize: true
t:
- filter: lm_head
value: [0.55]
- filter: embed_tokens
value: [0.7]
- filter: self_attn
value: [0.65, 0.35]
- filter: mlp
value: [0.35, 0.65]
- filter: layernorm
value: [0.4, 0.6]
- filter: modelnorm
value: [0.6]
- value: 0.5 # fallback for rest of tensors
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jsfs11/WONMSeverusDevilv2-TIES"
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"])