metadata
pipeline_tag: text-generation
inference: true
license: apache-2.0
datasets:
- codeparrot/github-code-clean
- bigcode/starcoderdata
- togethercomputer/RedPajama-Data-V2
- togethercomputer/RedPajama-Data-1T
- allenai/peS2o
- open-web-math/open-web-math
- EleutherAI/proof-pile-2
- nvidia/HelpSteer
- garage-bAInd/Open-Platypus
- mosaicml/dolly_hhrlhf
- mosaicml/instruct-v3
- conceptofmind/FLAN_2022
- KnutJaegersberg/longinstruct
- bigcode/oasst-octopack
- CohereForAI/xP3x
- math-ai/StackMathQA
- math-ai/TemplateGSM
- bugdaryan/sql-create-context-instruction
- glaiveai/glaive-function-calling-v2
- glaiveai/glaive-code-assistant-v3
- cognitivecomputations/dolphin-coder
- glaiveai/glaive-code-assistant
- TokenBender/code_instructions_122k_alpaca_style
- TIGER-Lab/MathInstruct
- meta-math/MetaMathQA
- tiedong/goat
- CohereForAI/xP3x
- bigcode/commitpack
- bigcode/commitpackft
- HuggingFaceTB/cosmopedia
- deepmind/code_contests
- ise-uiuc/Magicoder-Evol-Instruct-110K
- ise-uiuc/Magicoder-OSS-Instruct-75K
- theblackcat102/evol-codealpaca-v1
- ajibawa-2023/Code-290k-ShareGPT
- Locutusque/UltraTextbooks-2.0
- teknium/OpenHermes-2.5
- stingning/ultrachat
metrics:
- code_eval
library_name: transformers
tags:
- code
model-index:
- name: granite-3b-code-base
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value: 34.1
veriefied: false
- task:
type: text-generation
dataset:
type: evalplus/humanevalplus
name: HumanEval+
metrics:
- name: pass@1
type: pass@1
value: 29.9
veriefied: false
- task:
type: text-generation
dataset:
type: mbpp
name: MBPP
metrics:
- name: pass@1
type: pass@1
value: 36
veriefied: false
- task:
type: text-generation
dataset:
type: evalplus/mbppplus
name: MBPP+
metrics:
- name: pass@1
type: pass@1
value: 45.1
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(Python)
metrics:
- name: pass@1
type: pass@1
value: 36
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(JavaScript)
metrics:
- name: pass@1
type: pass@1
value: 37.2
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(Java)
metrics:
- name: pass@1
type: pass@1
value: 40.9
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(Go)
metrics:
- name: pass@1
type: pass@1
value: 26.2
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(C++)
metrics:
- name: pass@1
type: pass@1
value: 35.4
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(Rust)
metrics:
- name: pass@1
type: pass@1
value: 22
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(Python)
metrics:
- name: pass@1
type: pass@1
value: 25
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(JavaScript)
metrics:
- name: pass@1
type: pass@1
value: 18.9
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(Java)
metrics:
- name: pass@1
type: pass@1
value: 29.9
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(Go)
metrics:
- name: pass@1
type: pass@1
value: 17.1
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(C++)
metrics:
- name: pass@1
type: pass@1
value: 26.8
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain(Rust)
metrics:
- name: pass@1
type: pass@1
value: 14
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(Python)
metrics:
- name: pass@1
type: pass@1
value: 18.3
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(JavaScript)
metrics:
- name: pass@1
type: pass@1
value: 23.2
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(Java)
metrics:
- name: pass@1
type: pass@1
value: 29.9
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(Go)
metrics:
- name: pass@1
type: pass@1
value: 24.4
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(C++)
metrics:
- name: pass@1
type: pass@1
value: 16.5
veriefied: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFix(Rust)
metrics:
- name: pass@1
type: pass@1
value: 3.7
veriefied: false
Granite 3B Code Base
Model Summary
Granite 3B Code Base is a decoder-only code model designed for code generative tasks (e.g., code generation, code explanation, code fixing). It was trained from scratch on 4 trillion tokens sourced from 116 programming languages, ensuring a comprehensive understanding of programming languages and syntax.
- Developers: IBM Research
- GitHub Repository: ibm-granite/granite-code-models
- Paper: Granite Code Models: A Family of Open Foundation Models for Code Intelligence
- Release Date: May 6th, 2024
- License: Apache 2.0 license.
Usage
Intended use
Prominent enterprise use cases of LLMs in software engineering productivity include code generation, code explanation, code fixing, generating unit tests, generating documentation, addressing technical debt issues, vulnerability detection, code translation, and more. All Granite Code Base models, including the 3B parameters model, are able to handle these tasks as they were trained on a large amount of code data from 116 programming languages.
Generation
This is a simple example of how to use Granite Code Base 3B model.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # or "cpu"
model_path = "ibm-granite/granite-3b-code-base"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()
# change input text as desired
input_text = "def generate():"
# tokenize the text
input_tokens = tokenizer(input_text, return_tensors="pt")
# transfer tokenized inputs to the device
for i in input_tokens:
input_tokens[i] = input_tokens[i].to(device)
# generate output tokens
output = model.generate(**input_tokens)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# loop over the batch to print, in this example the batch size is 1
for i in output:
print(output)
Training Data
- Data Collection and Filtering: Pretraining code data is sourced from a combination of publicly available datasets (e.g., GitHub Code Clean, Starcoder data), and additional public code repositories and issues from GitHub. We filter raw data to retain a list of 116 programming languages. After language filtering, we also filter out low-quality code.
- Exact and Fuzzy Deduplication: We adopt an aggressive deduplication strategy that includes both exact and fuzzy deduplication to remove documents having (near) identical code content.
- HAP, PII, Malware Filtering: We apply a HAP content filter that reduces models' likelihood of generating hateful, abusive, or profane language. We also make sure to redact Personally Identifiable Information (PII) by replacing PII content (e.g., names, email addresses, keys, passwords) with corresponding tokens (e.g., ⟨NAME⟩, ⟨EMAIL⟩, ⟨KEY⟩, ⟨PASSWORD⟩). Moreover, we scan all datasets using ClamAV to identify and remove instances of malware in the source code.
- Natural Language Datasets: In addition to collecting code data for model training, we curate several publicly available high-quality natural language datasets to improve models' proficiency in language understanding and mathematical reasoning. Unlike the code data, we do not deduplicate these datasets.
Infrastructure
We train the Granite Code models using two of IBM's super computing clusters, namely Vela and Blue Vela, both outfitted with NVIDIA A100 and H100 GPUs respectively. These clusters provide a scalable and efficient infrastructure for training our models over thousands of GPUs.
Limitations
Large Language Models are often prone to generating incorrect information, typically referred to as hallucinations. Granite 3B Code Base model is not the exception in this regard. Even though this model is suited for code-related tasks as it is trained on source code from 116 programming languages, the generated code is not guaranteed to work as intended. It can be inefficient and can also contain bugs or exploits. Moreover, Granite Code Base models are NOT instruction-following models. Thus, commands like "Write a function that computes the square root" may not work well. The model is best treated as a code completion or code infilling model.
Citation
@misc{granite-models,
author = {author 1, author2, ...},
title = {Granite Code Large Language Models: IBM Foundation Models for Code},
journal = {},
volume = {},
year = {2024},
url = {https://arxiv.org/abs/0000.00000},
}