teknium/Replit-v1-CodeInstruct-3B-fp16

Base Model: replit/replit-code-v1-3b

This model is uploaded in FP16, (half the size as the original fine tuned upload, for easier download)

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