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

Libraries

How to use deepseek-ai/deepseek-coder-1.3b-base with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-1.3b-base")
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-base")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deepseek-ai/deepseek-coder-1.3b-base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/deepseek-ai/deepseek-coder-1.3b-base

SGLang

How to use deepseek-ai/deepseek-coder-1.3b-base 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 "deepseek-ai/deepseek-coder-1.3b-base" \
    --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": "deepseek-ai/deepseek-coder-1.3b-base",
		"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 "deepseek-ai/deepseek-coder-1.3b-base" \
        --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": "deepseek-ai/deepseek-coder-1.3b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

zqh11 commited on Nov 3, 2023

Commit

606fddd

1 Parent(s): 720b6ea

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -36,7 +36,7 @@ import torch
 tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-1.3b-base", trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-base", trust_remote_code=True).cuda()
 input_text = "#write a quick sort algorithm"
-inputs = tokenizer(input_text, return_tensors="pt").cuda()
 outputs = model.generate(**inputs, max_length=128)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
@@ -59,7 +59,7 @@ input_text = """<｜fim▁begin｜>def quick_sort(arr):
         else:
             right.append(arr[i])
     return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>"""
-inputs = tokenizer(input_text, return_tensors="pt").cuda()
 outputs = model.generate(**inputs, max_length=128)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])
 ```
@@ -144,7 +144,7 @@ from model import IrisClassifier as Classifier
 def main():
     # Model training and evaluation
 """
-inputs = tokenizer(input_text, return_tensors="pt").cuda()
 outputs = model.generate(**inputs, max_new_tokens=140)
 print(tokenizer.decode(outputs[0]))
 ```

 tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-1.3b-base", trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-base", trust_remote_code=True).cuda()
 input_text = "#write a quick sort algorithm"
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
 outputs = model.generate(**inputs, max_length=128)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
         else:
             right.append(arr[i])
     return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>"""
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
 outputs = model.generate(**inputs, max_length=128)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])
 ```
 def main():
     # Model training and evaluation
 """
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
 outputs = model.generate(**inputs, max_new_tokens=140)
 print(tokenizer.decode(outputs[0]))
 ```