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

Libraries

How to use adowu/autocodit with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("adowu/autocodit")
model = AutoModelForCausalLM.from_pretrained("adowu/autocodit")
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 adowu/autocodit with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/adowu/autocodit

SGLang

How to use adowu/autocodit 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 "adowu/autocodit" \
    --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": "adowu/autocodit",
		"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 "adowu/autocodit" \
        --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": "adowu/autocodit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

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AUTOCODIT

Description

This model represents an innovative fusion of three cutting-edge language models: BigL-7B, Code-Mistral-7B, and mistral-7b-anthropic, leveraging the strengths of each to create a more powerful and versatile tool. The integration process employs the TIES merge method, meticulously combining these models to enhance performance and adaptability across a broad spectrum of natural language processing tasks.

Creation Process

The model was crafted through a strategic merging process, utilizing the TIES merge method. This approach was chosen for its effectiveness in preserving the unique capabilities of each constituent model while ensuring seamless interoperability. The base model for this fusion was HuggingFaceH4/mistral-7b-anthropic, selected for its robust architecture and performance.

The merge parameters were carefully calibrated to achieve the optimal balance between the models, with the following configuration:

BigL-7B was integrated with a density of 0.9 and a weight of 0.8, contributing its extensive language understanding and generation capabilities.
Code-Mistral-7B was incorporated with a density of 0.7 and a weight of 0.7, enhancing the model's proficiency in code-related tasks and technical language comprehension.
mistral-7b-anthropic served as the foundation, with its parameters set to a density of 0.9 and a weight of 0.8, ensuring the model's general language processing abilities remained at the forefront.

Features

Model Type: MistralForCausalLM
Vocabulary Size: 32,000 tokens, encompassing a wide array of linguistic elements for comprehensive language coverage.
Maximum Position Embeddings: 32,768, facilitating the processing of extended passages of text.
Hidden Size: 4,096, enabling the model to capture complex patterns and nuances in the data.
Num Attention Heads: 32, allowing for detailed attention to various aspects of the input.
Num Hidden Layers: 32, providing depth to the model's understanding and generation capabilities.

Applications This model is adept at a wide range of natural language processing tasks, including but not limited to text generation, language translation, code synthesis, and more. Its unique blend of features from BigL-7B, Code-Mistral-7B, and mistral-7b-anthropic makes it particularly effective in scenarios requiring a deep understanding of both human and programming languages.

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for adowu/autocodit

Base model

HuggingFaceH4/mistral-7b-cai

Finetuned

HuggingFaceH4/mistral-7b-anthropic

Finetuned

(1)

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