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
```

davila7 commited on Jan 17, 2024

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@@ -110,15 +110,20 @@ special_tokens:
 # 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

 # 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