Instructions to use Bin12345/AutoCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bin12345/AutoCoder with Transformers:

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

pipe = pipeline("text-generation", model="Bin12345/AutoCoder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bin12345/AutoCoder")
model = AutoModelForCausalLM.from_pretrained("Bin12345/AutoCoder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Bin12345/AutoCoder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bin12345/AutoCoder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bin12345/AutoCoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Bin12345/AutoCoder

SGLang

How to use Bin12345/AutoCoder 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 "Bin12345/AutoCoder" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bin12345/AutoCoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Bin12345/AutoCoder" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bin12345/AutoCoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Bin12345/AutoCoder with Docker Model Runner:
```
docker model run hf.co/Bin12345/AutoCoder
```

Evolution-Intelligence commited on May 13, 2024

Commit

f55fc86

2 Parent(s): 07948bb 57aadab

Merge branch 'main' of https://huggingface.co/Bin12345/AutoCoder into main

Browse files

Files changed (1) hide show

README.md +0 -3

README.md CHANGED Viewed

@@ -20,7 +20,6 @@ HumanEval = load_dataset("evalplus/humanevalplus")
 Input = "" # input your question here
 messages=[
     { 'role': 'user', 'content': Input}
 ]
@@ -36,6 +35,4 @@ outputs = model.generate(inputs,
                         eos_token_id=tokenizer.eos_token_id)
 answer = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
 ```

 Input = "" # input your question here
 messages=[
     { 'role': 'user', 'content': Input}
 ]
                         eos_token_id=tokenizer.eos_token_id)
 answer = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
 ```