Instructions to use ByteDance-Seed/Seed-Coder-8B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ByteDance-Seed/Seed-Coder-8B-Base with Transformers:

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

pipe = pipeline("text-generation", model="ByteDance-Seed/Seed-Coder-8B-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Base")
model = AutoModelForCausalLM.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ByteDance-Seed/Seed-Coder-8B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ByteDance-Seed/Seed-Coder-8B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteDance-Seed/Seed-Coder-8B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ByteDance-Seed/Seed-Coder-8B-Base

SGLang

How to use ByteDance-Seed/Seed-Coder-8B-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 "ByteDance-Seed/Seed-Coder-8B-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": "ByteDance-Seed/Seed-Coder-8B-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 "ByteDance-Seed/Seed-Coder-8B-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": "ByteDance-Seed/Seed-Coder-8B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ByteDance-Seed/Seed-Coder-8B-Base with Docker Model Runner:
```
docker model run hf.co/ByteDance-Seed/Seed-Coder-8B-Base
```

yuyuzhang commited on Apr 27, 2025

Commit

5f0d886

verified ·

1 Parent(s): 8f0e7db

Update README.md

Browse files

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README.md +3 -3

README.md CHANGED Viewed

@@ -53,7 +53,7 @@ A typical usage flow:
 import transformers
 import torch
-model_id = "ByteDance-Seed/Seed-Coder-8B-Base"
 pipeline = transformers.pipeline(
     "text-generation",
@@ -67,9 +67,9 @@ prefix = "def add_numbers(a, b):\n    "
 suffix = "\n    return result"
 # Combine prefix and suffix following the FIM format
-fim_input = "<|fim-suffix|>" + suffix + "<|fim-prefix|>" + prefix + "<|fim-middle|>"
-output = pipeline(fim_input, max_new_tokens=100)
 print(output[0]["generated_text"])
 ```

 import transformers
 import torch
+model_id = "/mnt/bn/daoguang/ckpts/Bytedance/Doubao-Coder-base/P6Dense"
 pipeline = transformers.pipeline(
     "text-generation",
 suffix = "\n    return result"
 # Combine prefix and suffix following the FIM format
+fim_input = '<[PLHD125_never_used_51bce0c785ca2f68081bfa7d91973934]>' + suffix + '<[PLHD124_never_used_51bce0c785ca2f68081bfa7d91973934]>' + prefix + '<[PLHD126_never_used_51bce0c785ca2f68081bfa7d91973934]>'
+output = pipeline(fim_input, max_new_tokens=512)
 print(output[0]["generated_text"])
 ```