Text-to-Speech
Transformers
Safetensors
mistral
text-generation
Merge
mergekit
lazymergekit
google-bert/bert-base-uncased
robowaifudev/megatron-gpt2-345m
text-generation-inference
Instructions to use nagayama0706/sales_talk_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nagayama0706/sales_talk_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="nagayama0706/sales_talk_model")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nagayama0706/sales_talk_model") model = AutoModelForCausalLM.from_pretrained("nagayama0706/sales_talk_model") - Notebooks
- Google Colab
- Kaggle
sales_talk_model
sales_talk_model is a merge of the following models using LazyMergekit:
π§© Configuration
slices:
- sources:
- model: google-bert/bert-base-uncased
layer_range: [0, 32]
- model: robowaifudev/megatron-gpt2-345m
layer_range: [0, 32]
merge_method: slerp
base_model: google-bert/bert-base-uncased
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: 0.5
dtype: bfloat16
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "nagayama0706/sales_talk_model"
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"])
- Downloads last month
- 6