README.md

metadata

base_model:
  - JoPmt/Trismal-NeurAmoclion-7B-Base-Ties
  - preemware/Prox-MistralHermes-7B
tags:
  - merge
  - mergekit
  - lazymergekit
  - JoPmt/Trismal-NeurAmoclion-7B-Base-Ties
  - preemware/Prox-MistralHermes-7B

Trismal-Xyro-7B-Base-Ties

Trismal-Xyro-7B-Base-Ties is a merge of the following models using LazyMergekit:

• JoPmt/Trismal-NeurAmoclion-7B-Base-Ties
• preemware/Prox-MistralHermes-7B

🧩 Configuration

models:
  - model: JoPmt/Trismal-NeurAmoclion-7B-Base-Ties
    parameters:
      weight: 1
      density: 1
  - model: preemware/Prox-MistralHermes-7B
    parameters:
      weight: 1
      density: 1
merge_method: ties
base_model: JoPmt/Trismal-NeurAmoclion-7B-Base-Ties
parameters:
  weight: 1
  density: 1
  normalize: true
  int8_mask: false
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "JoPmt/Trismal-Xyro-7B-Base-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"])