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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

We introduced a new model designed for the Code generation task. Its test accuracy on the HumanEval base dataset surpasses that of GPT-4 Turbo (April 2024). (90.9% vs 90.2%).

Additionally, compared to previous open-source models, AutoCoder offers a new feature: it can automatically install the required packages and attempt to run the code until it deems there are no issues, whenever the user wishes to execute the code.

Its base model is deepseeker-coder.

See details on the AutoCoder GitHub.

Simple test script:

from transformers import AutoTokenizer, AutoModelForCausalLM
from datasets import load_dataset

model_path = ""
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, 
                                             device_map="auto")

HumanEval = load_dataset("evalplus/humanevalplus")

Input = "" # input your question here
 
messages=[
    { 'role': 'user', 'content': Input}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, 
                                        return_tensors="pt").to(model.device)

outputs = model.generate(inputs, 
                        max_new_tokens=1024, 
                        do_sample=False, 
                        temperature=0.0,
                        top_p=1.0, 
                        num_return_sequences=1, 
                        eos_token_id=tokenizer.eos_token_id)

answer = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)

Paper: https://arxiv.org/abs/2405.14906

Downloads last month: 33

Safetensors

Model size

33B params

Tensor type

BF16

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Quantizations

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Paper for Bin12345/AutoCoder

AutoCoder: Enhancing Code Large Language Model with AIEV-Instruct

Paper • 2405.14906 • Published May 23, 2024 • 26