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JoPmt/Trismal-HyperAmocles-7B-Base-Ties
Trismal-HyperAmocles-7B-Base-Ties is a machine learning model from JoPmt. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Trismal-HyperAmocles-7B-Base-Ties is a merge of the following models using LazyMergekit: JoPmt/Trismal-Amocles-7B-Base-Ties Locutusque/Hyperion-2.0-Mistral-7B
Downloads · 30 days
13
27% of all-time downloads
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.safetensors14.5 GB · 100%
From the Hugging Face model README
Trismal-HyperAmocles-7B-Base-Ties is a merge of the following models using LazyMergekit:
models:
- model: JoPmt/Trismal-Amocles-7B-Base-Ties
parameters:
weight: 1
density: 1
- model: Locutusque/Hyperion-2.0-Mistral-7B
parameters:
weight: 1
density: 1
merge_method: ties
base_model: JoPmt/Trismal-Amocles-7B-Base-Ties
parameters:
weight: 1
density: 1
normalize: true
int8_mask: false
dtype: float16
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "JoPmt/Trismal-HyperAmocles-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"])