Create README.md

README.md

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+---
+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