Instructions to use budecosystem/code-millenials-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use budecosystem/code-millenials-13b with Transformers:

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

pipe = pipeline("text-generation", model="budecosystem/code-millenials-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("budecosystem/code-millenials-13b")
model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-13b", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use budecosystem/code-millenials-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "budecosystem/code-millenials-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "budecosystem/code-millenials-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/budecosystem/code-millenials-13b

SGLang

How to use budecosystem/code-millenials-13b 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 "budecosystem/code-millenials-13b" \
    --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": "budecosystem/code-millenials-13b",
		"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 "budecosystem/code-millenials-13b" \
        --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": "budecosystem/code-millenials-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use budecosystem/code-millenials-13b with Docker Model Runner:
```
docker model run hf.co/budecosystem/code-millenials-13b
```

code-millenials-13b / README.md

dittops

Update README.md

fbaabb7 over 2 years ago

preview code

Raw

History Blame Contribute Delete

4.53 kB

	---
	license: llama2
	library_name: transformers
	tags:
	- code
	model-index:
	- name: Code Millenials
	results:
	- task:
	type: text-generation
	dataset:
	type: openai_humaneval
	name: HumanEval
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.7621
	verified: false
	---


	# Bud Code Millenials 13B

	Welcome to our Code Model repository! Our model is specifically fine-tuned for code generation tasks. Bud Millenial Code Gen open-source models are currently the State of the Art (SOTA) for code generation, beating all the existing models of all sizes. We have achieved a HumanEval value of 80.48 @ Pass 1, beating proprietary models like Gemini Ultra, Claude, GPT-3.5 etc. by a large margin, and on par with GPT-4 (HumanEval ~ 82. Ref. WizardCoder). Our proprietary model (Bud Code Jr) beats GPT-4 as well with a HumanEval value of 88.2 & a context size of 168K, we will be releasing an API for Researchers, Enterprises, and potential Partners by January 2024 end. If interested, please reach out to jithinvg@bud.studio
	### News 🔥🔥🔥

	- [2024/01/09] We released Code Millenials 3B , which achieves the 56.09 pass@1 on the [HumanEval Benchmarks](https://github.com/openai/human-eval).
	- [2024/01/09] We released Code Millenials 1B , which achieves the 51.82 pass@1 on the [HumanEval Benchmarks](https://github.com/openai/human-eval).
	- [2024/01/03] We released Code Millenials 34B , which achieves the 80.48 pass@1 on the [HumanEval Benchmarks](https://github.com/openai/human-eval).
	- [2024/01/02] We released Code Millenials 13B , which achieves the 76.21 pass@1 on the [HumanEval Benchmarks](https://github.com/openai/human-eval).


	### HumanEval

	<p align="center" width="100%">
	<a ><img src="https://raw.githubusercontent.com/BudEcosystem/code-millenials/main/assets/result.png" alt="CodeMillenials" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
	</p>

	For the millenial models, the eval script in the github repo is used for the above result.

	Note: The humaneval values of other models are taken from the official repos of [WizardCoder](https://github.com/nlpxucan/WizardLM), [DeepseekCoder](https://github.com/deepseek-ai/deepseek-coder), [Gemini](https://deepmind.google/technologies/gemini/#capabilities) etc.


	### Models

	\| Model \| Checkpoint \| HumanEval (+) \| MBPP (+) \|
	\|---------\|-------------\|---------------\|----------\|
	\|Code Millenials 34B \| <a href="https://huggingface.co/budecosystem/code-millenials-34b" target="_blank">HF Link</a> \| 80.48 (75) \| 74.68 (62.9) \|
	\|Code Millenials 13B \| <a href="https://huggingface.co/budecosystem/code-millenials-13b" target="_blank">HF Link</a> \| 76.21 (69.5) \| 70.17 (57.6) \|
	\|Code Millenials 3B \| <a href="https://huggingface.co/budecosystem/code-millenials-3b" target="_blank">HF Link</a> \| 56.09 (52.43) \| 55.13 (47.11) \|
	\|Code Millenials 1B \| <a href="https://huggingface.co/budecosystem/code-millenials-1b" target="_blank">HF Link</a> \| 51.82 (48.17) \| 53.13 (44.61) \|




	### 🚀 Quick Start

	Inference code using the pre-trained model from the Hugging Face model hub

	```python
	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM

	tokenizer = AutoTokenizer.from_pretrained("budecosystem/code-millenials-13b")
	model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-13b")

	template = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
	### Instruction: {instruction} ### Response:"""

	instruction = <Your code instruction here>

	prompt = template.format(instruction=instruction)

	inputs = tokenizer(prompt, return_tensors="pt")
	sample = model.generate(**inputs, max_length=128)
	print(tokenizer.decode(sample[0]))

	```


	## Training details

	The model is trained of 8 A100 80GB for approximately 15hrs.

	\| Hyperparameters \| Value \|
	\| :----------------------------\| :-----: \|
	\| per_device_train_batch_size \| 2 \|
	\| gradient_accumulation_steps \| 1 \|
	\| epoch \| 3 \|
	\| steps \| 34503 \|
	\| learning_rate \| 2e-5 \|
	\| lr schedular type \| cosine \|
	\| warmup ratio \| 0.1 \|
	\| optimizer \| adamw \|
	\| fp16 \| True \|
	\| GPU \| 8 A100 80GB \|

	### Important Note

	- Bias, Risks, and Limitations: Model may sometimes make errors, produce misleading contents, or struggle to manage tasks that are not related to coding.