Instructions to use 0xk1h0/codegen2-1B-ds-zero3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0xk1h0/codegen2-1B-ds-zero3 with Transformers:

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

pipe = pipeline("text-generation", model="0xk1h0/codegen2-1B-ds-zero3", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("0xk1h0/codegen2-1B-ds-zero3", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("0xk1h0/codegen2-1B-ds-zero3", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 0xk1h0/codegen2-1B-ds-zero3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xk1h0/codegen2-1B-ds-zero3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0xk1h0/codegen2-1B-ds-zero3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/0xk1h0/codegen2-1B-ds-zero3

SGLang

How to use 0xk1h0/codegen2-1B-ds-zero3 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 "0xk1h0/codegen2-1B-ds-zero3" \
    --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": "0xk1h0/codegen2-1B-ds-zero3",
		"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 "0xk1h0/codegen2-1B-ds-zero3" \
        --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": "0xk1h0/codegen2-1B-ds-zero3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use 0xk1h0/codegen2-1B-ds-zero3 with Docker Model Runner:
```
docker model run hf.co/0xk1h0/codegen2-1B-ds-zero3
```

codegen2-1B_py150_secu

This model is a fine-tuned version of Salesforce/codegen2-1B on the code_segments_py150k dataset. It achieves the following results on the evaluation set:

Loss: 1.0928
Accuracy: 0.7620

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 16
total_train_batch_size: 32
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1.0

Training results

Framework versions

Transformers 4.32.1
Pytorch 1.13.1+cu117
Datasets 2.14.4
Tokenizers 0.13.3

Downloads last month: 11

Model tree for 0xk1h0/codegen2-1B-ds-zero3

Base model

Salesforce/codegen2-1B_P

Finetuned

(3)

this model

Evaluation results

Accuracy on code_segments_py150k
self-reported

0.762