license: other
language:
- en
library_name: transformers
pipeline_tag: conversational
Model Card for Model ID
Finetuned depacoda-research/llamma-13b-hf on conversations
Model Details
Model Description
The depacoda-research/llamma-13b-hf model was finetuned on conversations and question answering prompts
Developed by: [More Information Needed] Shared by: [More Information Needed] Model type: Causal LM Language(s) (NLP): English, multilingual License: Research Finetuned from model: depacoda-research/llamma-13b-hf
Model Sources [optional]
Repository: [More Information Needed] Paper: [More Information Needed] Demo: [More Information Needed]
Uses
The model can be used for prompt answering
Direct Use
The model can be used for prompt answering
Downstream Use
Generating text and prompt answering
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import LlamaTokenizer, LlamaForCausalLM
from peft import PeftModel
MODEL_NAME = "decapoda-research/llama-13b-hf"
tokenizer = LlamaTokenizer.from_pretrained(MODEL_NAME, add_eos_token=True)
tokenizer.pad_token_id = 0
model = LlamaForCausalLM.from_pretrained(MODEL_NAME, load_in_8bit=True, device_map="auto")
model = PeftModel.from_pretrained(model, "Sandiago21/public-ai-model")
Training Details
Training Data
The decapoda-research/llama-13b-hf was finetuned on conversations and question answering data
Training Procedure
The decapoda-research/llama-13b-hf model was further trained and finetuned on question answering and prompts data
Model Architecture and Objective
The model is based on decapoda-research/llama-13b-hf model and finetuned adapters on top of the main model on conversations and question answering data.