CodeGPTPlus/deepseek-coder-1.3b-typescript · Hugging Face

[irthomasthomas]

• CodeGPTPlus/deepseek-coder-1.3b-typescript · Hugging Face

CodeGPTPlus/deepseek-coder-1.3b-typescript

This is a fine-tuned model by the CodeGPT team, specifically crafted for generating expert code in TypeScript. It is fine-tuned from deepseek-ai/deepseek-coder-1.3b-base with a dataset of 0.5B tokens, making it an excellent choice for precise and efficient TypeScript code generation.

The model uses a 16K window size and an additional fill-in-the-middle task for project-level code completion.

How to Use

This model is for completion purposes only. Here are some examples of how to use the model:

Running the model on a GPU

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("CodeGPTPlus/deepseek-coder-1.3b-typescript", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("CodeGPTPlus/deepseek-coder-1.3b-typescript", trust_remote_code=True).cuda()

input_text = """<|fim begin|>function quickSort(arr: number[]): number[] {
  if (arr.length <= 1) {
    return arr;
  }
  const pivot = arr[0];
  const left = [];
  const right = [];
<|fim hole|>
  return [...quickSort(left), pivot, ...quickSort(right)];
}<|fim end|>"""

inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Running with Ollama

• Model: https://ollama.ai/codegpt/deepseek-coder-1.3b-typescript
• Command: ollama run codegpt/deepseek-coder-1.3b-typescript

Running with Ollama and CodeGPT Autocomplete in VSCode

• Documentation: https://docs.codegpt.co/docs/tutorial-features/code_autocompletion
• Select "Ollama - codegpt/deepseek-coder-1.3b-typescript" in the autocomplete model selector.

Fill In the Middle (FIM)

<|fim begin|>function quickSort(arr: number[]): number[] {
  if (arr.length <= 1) {
    return arr;
  }
  const pivot = arr[0];
  const left = [];
  const right = [];
<|fim hole|>
  return [...quickSort(left), pivot, ...quickSort(right)];
}<|fim end|>

Training Procedure

The model was trained using the following hyperparameters:

• learning_rate: 2e-05
• train_batch_size: 20
• eval_batch_size: 20
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 40
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 261
• num_epochs: 1

For more information, visit the model page.

Suggested labels

{ "label-name": "TypeScript-Code-Generation", "description": "Model for generating TypeScript code", "repo": "CodeGPTPlus/deepseek-coder-1.3b-typescript", "confidence": 70.59 }