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")

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
```

Vokturz commited on Jan 17, 2024

Commit

dd58664

verified ·

1 Parent(s): be6ab22

Update README.md

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@@ -15,7 +15,7 @@ widget:
 - text: "function quickSort"
   example_title: "function"
 ---
-<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/64760b43ab053b8808c60586/8jawtZsIykGmMLPhJwJi2.png?w=200&h=200&f=face" alt="CodeGPT" width="100" />
 [<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>
@@ -108,18 +108,6 @@ This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](htt
 It achieves the following results on the evaluation set:
 - Loss: 0.7681
-## Model description
-More information needed
-## Intended uses & limitations
-More information needed
-## Training and evaluation data
-More information needed
 ## Training procedure
 ### Training hyperparameters

 - text: "function quickSort"
   example_title: "function"
 ---
+<img src="codegpt-deepseek-typescript.png" alt="CodeGPT" width="800"  />
 [<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>
 It achieves the following results on the evaluation set:
 - Loss: 0.7681
 ## Training procedure
 ### Training hyperparameters