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---
language:
- en
license: llama2
library_name: transformers
tags:
- CodeMate
- Code
- CodeLLaMa
pipeline_tag: text-generation
model-index:
- name: CodeMate-v0.1
results:
- task:
type: text-generation
dataset:
name: HumanEval
type: openai_humaneval
metrics:
- type: pass@1
value: 74.9%
name: pass@1
verified: false
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 55.55
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=codemateai/CodeMate-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 78.03
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=codemateai/CodeMate-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 55.31
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=codemateai/CodeMate-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 48.64
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=codemateai/CodeMate-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 72.61
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=codemateai/CodeMate-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 40.18
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=codemateai/CodeMate-v0.1
name: Open LLM Leaderboard
---
# **CodeMate-v0.1**
CodeMate-v0.1 is an intelligent programming assistant developed by [CodeMate](https://codemate.ai).
This model aims to assist users in generating high-quality code solutions for programming problems.
Please note that this model is currently in version 0.1.
## Model Details
- **Training Data:** Exclusively fine-tuned on a proprietary dataset of 1.8 billion tokens of high-quality programming problems and solutions.
- The dataset was generated manually and is internal to CodeMate.
- **Training Techniques:** The model was fine-tuned using Flash Attention 2, trained over 15 hours on 40 A100-80GB GPUs.
- A sequence length of 8096 tokens was used during training.
- **Multilingual Support:** CodeMate-v0.1 is proficient in multiple programming languages, including Python, C/C++, TypeScript, Java, and more.
## How to Get Started with the Model
Make sure to install Transformers from the main git branch:
```bash
pip install git+https://github.com/huggingface/transformers.git
```
## How to Prompt the Model
This model accepts prompts in the Alpaca/Vicuna instruction format. For example:
```markdown
### System Prompt
You are an intelligent programming assistant.
### User Message
Implement a linked list in C++
### Assistant
...
```
## Load the Model:
To load the model, utilize the following Python script:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
# Initialize the model
model_path = "codemateai/CodeMate-v0.1"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_path)
# ... generate response ...
```
## Bias, Risks, and Limitations
This model has undergone very limited testing. CodeMate recommends additional safety testing before any real-world deployments.
For more information and updates, visit the [CodeMate website](https://codemate.ai).
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_codemateai__CodeMate-v0.1)
| Metric |Value|
|---------------------------------|----:|
|Avg. |58.39|
|AI2 Reasoning Challenge (25-Shot)|55.55|
|HellaSwag (10-Shot) |78.03|
|MMLU (5-Shot) |55.31|
|TruthfulQA (0-shot) |48.64|
|Winogrande (5-shot) |72.61|
|GSM8k (5-shot) |40.18|
|