Instructions to use CodeGPTPlus/deepseek-coder-1.3b-typescript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeGPTPlus/deepseek-coder-1.3b-typescript with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CodeGPTPlus/deepseek-coder-1.3b-typescript")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CodeGPTPlus/deepseek-coder-1.3b-typescript")
model = AutoModelForCausalLM.from_pretrained("CodeGPTPlus/deepseek-coder-1.3b-typescript", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CodeGPTPlus/deepseek-coder-1.3b-typescript with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeGPTPlus/deepseek-coder-1.3b-typescript"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeGPTPlus/deepseek-coder-1.3b-typescript",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CodeGPTPlus/deepseek-coder-1.3b-typescript

SGLang

How to use CodeGPTPlus/deepseek-coder-1.3b-typescript with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "CodeGPTPlus/deepseek-coder-1.3b-typescript" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeGPTPlus/deepseek-coder-1.3b-typescript",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "CodeGPTPlus/deepseek-coder-1.3b-typescript" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeGPTPlus/deepseek-coder-1.3b-typescript",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CodeGPTPlus/deepseek-coder-1.3b-typescript with Docker Model Runner:
```
docker model run hf.co/CodeGPTPlus/deepseek-coder-1.3b-typescript
```

deepseek-coder-1.3b-typescript / README.md

davila7

update README links

a2defce verified over 2 years ago

4.6 kB

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 {
       constructor(public name:
    example_title: class
  - text: function quickSort
    example_title: function

CodeGPT: DeepSeek Coder - Typescript

[CodeGPT.co] | [🦙 Ollama] | [Discord] | [VSCode Extension]

See axolotl config

axolotl version: 0.3.0

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｜>"

deepseek-coder-1.3b-typescript

This model is a fine-tuned version of deepseek-ai/deepseek-coder-1.3b-base on the the-stack dataset, using 0.5B of tokens of typescript only. It achieves the following results on the evaluation set:

Loss: 0.7681

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