---
license: other
base_model: deepseek-ai/deepseek-coder-1.3b-base
tags:
- axolotl
- generated_from_trainer
model-index:
- name: deepseek-coder-1.3b-typescript
  results: []
datasets:
- bigcode/the-stack-dedup
widget:
- text: "class Person {\n constructor(public name:"
  example_title: "class"
- text: "function quickSort"
  example_title: "function"
---

<p align="center">
<img width="1000px" alt="CodeGPT: DeepSeek Coder - Typescript" src="codegpt-deepseek-typescript.png?raw=true">
</p>
<p align="center"><a href="https://codegpt.co/">[CodeGPT.co]</a>  |  <a href="https://ollama.ai/codegpt/deepseek-coder-1.3b-typescript">[🦙 Ollama]</a>  |  <a href="https://discord.gg/fKyyJX5pne">[Discord]</a>  |  <a href="https://marketplace.visualstudio.com/items?itemName=DanielSanMedium.dscodegpt">[VSCode Extension]</a> </p>
<hr>

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.3.0`
```yaml
base_model: deepseek-ai/deepseek-coder-1.3b-base
model_type: AutoModelForCausalLM
trust_remote_code: true
load_in_8bit: false
load_in_4bit: false
strict: false


datasets:
  - path: CodeGPTPlus/typescript-0-500000-seq1024
    type: completion
    field: text


val_set_size: 0.001
output_dir:  ./fft-out

sequence_len: 1024

adapter:
lora_model_dir:
lora_r: 
lora_alpha: 
lora_dropout: 
lora_target_linear: 
lora_fan_in_fan_out:
lora_modules_to_save:

wandb_project: deepseek_1.3_fft
wandb_entity:
wandb_watch:
wandb_name: aws_a10g
wandb_log_model: end


gradient_accumulation_steps: 2
micro_batch_size: 20
num_epochs: 1
optimizer: adamw_bnb_8bit
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 0.000001
max_grad_norm: 1.0
weight_decay: 0.1
lr_scheduler: cosine
learning_rate: 0.00002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

hub_model_id: CodeGPTPlus/deepseek_coder_1.3b_typescript
hub_strategy: every_save
warmup_ratio: 0.01
evals_per_epoch: 20
saves_per_epoch: 3
debug:
deepspeed:

fsdp:
fsdp_config:
special_tokens:
  bos_token: "<｜begin▁of▁sentence｜>"
  eos_token: "<｜end▁of▁sentence｜>"
  pad_token: "<｜end▁of▁sentence｜>"
```

</details><br>

# deepseek-coder-1.3b-typescript

CodeGPTPlus/deepseek-coder-1.3b-typescript, emerges as a fine-tuned iteration of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base), meticulously crafted by the CodeGPT team to excel in generating expert code in TypeScript. With specific fine-tuning for TypeScript and a dataset of 0.5B tokens, this model excels in producing precise and efficient solutions in this programming language.

The 16K window size and an additional fill-in-the-middle task are employed to deliver project-level code completion.

This new model stands as the ideal choice for those seeking a specialized code generator for TypeScript, backed by the expertise of the CodeGPT team. 

It achieves the following results on the evaluation set:
- Loss: 0.7681

**Model Developers** CodeGPT Team

**Variations**  1.3B

**Input** Models input text only.

**Output** Models generate text only.

## How to Use
This model is for completion purposes only. Here give some examples of how to use the model.

#### Running the model on a GPU
```python
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

```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. 

Then, write any code or comment in the vscode text editor, and the model will provide you with code suggestions through the CodeGPT code autocomplete.

<img width="1000px" alt="CodeGPT: DeepSeek Coder - Typescript" src="ollama_autocomplete_codegpt.gif">

### Fill In the Middle (FIM)
```python
<｜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

### Training hyperparameters

The following hyperparameters were used during training:
- 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

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0745        | 0.0   | 1     | 0.8681          |
| 1.2267        | 0.05  | 1308  | 0.8130          |
| 1.1594        | 0.1   | 2616  | 0.8018          |
| 0.7674        | 0.15  | 3924  | 0.7942          |
| 0.6443        | 0.2   | 5232  | 0.7889          |
| 0.9155        | 0.25  | 6540  | 0.7847          |
| 0.7501        | 0.3   | 7848  | 0.7819          |
| 0.8835        | 0.35  | 9156  | 0.7792          |
| 0.7261        | 0.4   | 10464 | 0.7769          |
| 0.9746        | 0.45  | 11772 | 0.7748          |
| 0.6884        | 0.5   | 13080 | 0.7734          |
| 0.6104        | 0.55  | 14388 | 0.7722          |
| 0.8876        | 0.6   | 15696 | 0.7710          |
| 0.9567        | 0.65  | 17004 | 0.7703          |
| 0.6915        | 0.7   | 18312 | 0.7696          |
| 0.8874        | 0.75  | 19620 | 0.7691          |
| 0.6124        | 0.8   | 20928 | 0.7686          |
| 0.8147        | 0.85  | 22236 | 0.7684          |
| 0.8021        | 0.9   | 23544 | 0.7683          |
| 0.8665        | 0.95  | 24852 | 0.7681          |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0