license: cc-by-sa-4.0
datasets:
- bigcode/the-stack-dedup
- sahil2801/CodeAlpaca-20k
- teknium/GPTeacher-CodeInstruct
model-base:
- replit/replit-code-v1-3b
tags:
- code
- instruct
- self instruct
language:
- code
programming_language:
- Markdown
- Java
- JavaScript
- Python
- TypeScript
- PHP
- SQL
- JSX
- reStructuredText
- Rust
- C
- CSS
- Go
- C++
- HTML
- Vue
- Ruby
- Jupyter Notebook
- R
- Shell
Base Model: replit/replit-code-v1-3b
This model is fine tuned on both Sahil2801's CodeAlpaca & Teknium's GPTeacher Code-Instruct to give Replit's Code model instruct capabilities.
Try this model on it's HuggingFace demo Spaces: https://huggingface.co/spaces/teknium/Replit-v1-CodeInstruct-3B
Dataset links: CodeAlpaca: https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k GPTeacher subset - Code Instruct: https://github.com/teknium1/GPTeacher
This model was trained on 2x a100 80gb for 1 hour on ~25,000 code instruction/response pairs in Alpaca format.
Refer to the base models HuggingFace model card for some basic requirements to run: https://huggingface.co/replit/replit-code-v1-3b
This fine tune can be prompted like any alpaca fine tune:
### Instruction:
<prompt>
### Input:
<additional context>
### Response:
or
### Instruction:
<prompt>
### Response:
This model seems to have issues with device="auto" in the model arguments (and requires the trust_remote_code=True, so you should maybe load it like I am here:
self.tokenizer = AutoTokenizer.from_pretrained("./Replit-CodeInstruct/", trust_remote_code=True)
self.model = AutoModelForCausalLM.from_pretrained(
"./Replit-CodeInstruct",
torch_dtype=torch.bfloat16,
trust_remote_code=True
)
self.model.to('cuda')
This model for me produced coherent outputs with the following sampler settings, but feel free to experiment:
max_new_tokens=128, do_sample=True, use_cache=True, temperature=0.2, top_p=0.9, eos_token_id= self.tokenizer.eos_token_id
In the tokenizer decode arguments, it also needs these settings:
skip_special_tokens=True, clean_up_tokenization_space=False
The following parameters were used with HuggingFace trainer to train the model with:
--model_name_or_path replit/replit-code-v1-3b --data_path /root/stanford_alpaca/train.json --bf16 True --output_dir /root/stanford_alpaca/model_ckpts --num_train_epochs 3 --per_device_train_batch_size 4 --per_device_eval_batch_size 1 --gradient_accumulation_steps 8 --save_strategy steps --save_steps 200 --save_total_limit 3 --learning_rate 1e-5 --weight_decay 0. --warmup_ratio 0.03 --tf32 True --run_name Replit1