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powermove72/LLama-6b-amt-v0.5-FTA
LLama-6b-amt-v0.5-FTA is a machine learning model from powermove72. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
LLama-6b-amt-v0.5-FTA is a merge of the following models using LazyMergekit: powermove72/LLama-6B-amt-v0.5-FineTome powermove72/LLama-6B-amt-v0.5-Alpagasus
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.safetensors11.3 GB · 100%
From the Hugging Face model README
LLama-6b-amt-v0.5-FTA is a merge of the following models using LazyMergekit:
merge_method: slerp
dtype: bfloat16
base_model: powermove72/LLama-6B-amt-v0.5-FineTome # Use as base for tokenizer/alignment
models:
- model: powermove72/LLama-6B-amt-v0.5-FineTome
parameters:
weight: 0.5
- model: powermove72/LLama-6B-amt-v0.5-Alpagasus
parameters:
weight: 0.5
parameters:
t:
- filter: self_attn
value: [0, 0.2, 0.4, 0.6, 0.8, 0.6, 0.4, 1]
- filter: mlp
value: [1, 0.8, 0.6, 0.4, 0.2, 0.4, 0.6, 0]
- value: 0.5
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "powermove72/LLama-6b-amt-v0.5-FTA"
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"])