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")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

dittops commited on Jan 2, 2024

Commit

5bf1e5d

1 Parent(s): 0014911

Update README.md

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@@ -7,8 +7,34 @@ license: llama2
 Welcome to our Code Model repository! Our model is specifically fine-tuned for code generation tasks, aiming to revolutionize how systems understand and translate natural language instructions into code queries. Built on CodeLLaMa 13B, our model has been meticulously fine-tuned with a curated code generation instructions, ensuring quality and precision.
-## Generate responses
 Inference code  using the pre-trained model from the Hugging Face model hub
@@ -49,3 +75,7 @@ The model is trained of 8 A100 80GB for approximately 15hrs.
 | optimizer                    | adamw  |
 | fp16                         | True   |
 | GPU                          | 8 A100 80GB |

 Welcome to our Code Model repository! Our model is specifically fine-tuned for code generation tasks, aiming to revolutionize how systems understand and translate natural language instructions into code queries. Built on CodeLLaMa 13B, our model has been meticulously fine-tuned with a curated code generation instructions, ensuring quality and precision.
+### News 🔥🔥🔥
+- [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://github.com/BudEcosystem/code-millenials/blob/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 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 |
+|---------|-------------|-----------|
+|Code Millenials 34B | <a href="https://huggingface.co/budecosystem/code-millenials-34b" target="_blank">HF Link</a> | 80.48 |
+|Code Millenials 13B | <a href="https://huggingface.co/budecosystem/code-millenials-13b" target="_blank">HF Link</a> | 76.21 |
+### 🚀 Quick Start
 Inference code  using the pre-trained model from the Hugging Face model hub
 | 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.