metadata
base_model: mwitiderrick/open_llama_3b_code_instruct_0.1
datasets:
- mwitiderrick/AlpacaCode
inference: true
model_type: llama
prompt_template: |
<s>[INST]
{prompt}
[/INST]
created_by: mwitiderrick
tags:
- transformers
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: mwitiderrick/open_llama_3b_instruct_v_0.2
results:
- task:
type: text-generation
dataset:
name: hellaswag
type: hellaswag
metrics:
- name: hellaswag(0-Shot)
type: hellaswag (0-Shot)
value: 0
- task:
type: text-generation
dataset:
name: winogrande
type: winogrande
metrics:
- name: winogrande(0-Shot)
type: winogrande (0-Shot)
value: 0
- task:
type: text-generation
dataset:
name: arc_challenge
type: arc_challenge
metrics:
- name: arc_challenge(0-Shot)
type: arc_challenge (0-Shot)
value: 0
source:
name: open_llama_3b_instruct_v_0.2 model card
url: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2
OpenLLaMA Code Instruct: An Open Reproduction of LLaMA
This is an OpenLlama model Code Instruct that has been fine-tuned on 1 epoch of the Glaive Assistsnt dataset.
Prompt Template
<s>[INST] {{ user_msg }} [/INST]
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline
tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_glaive_assistant_v0.1")
model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_glaive_assistant_v0.1")
query = "Write a quick sort algorithm in Python"
text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
output = text_gen(f"<s>[INST]{query}[/INST]")
print(output[0]['generated_text'])
"""
"""
Metrics