Instructions to use gavinqiangli/codeparrot-ds-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gavinqiangli/codeparrot-ds-v2 with Transformers:

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

pipe = pipeline("text-generation", model="gavinqiangli/codeparrot-ds-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("gavinqiangli/codeparrot-ds-v2")
model = AutoModelForMultimodalLM.from_pretrained("gavinqiangli/codeparrot-ds-v2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gavinqiangli/codeparrot-ds-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gavinqiangli/codeparrot-ds-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gavinqiangli/codeparrot-ds-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gavinqiangli/codeparrot-ds-v2

SGLang

How to use gavinqiangli/codeparrot-ds-v2 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 "gavinqiangli/codeparrot-ds-v2" \
    --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": "gavinqiangli/codeparrot-ds-v2",
		"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 "gavinqiangli/codeparrot-ds-v2" \
        --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": "gavinqiangli/codeparrot-ds-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gavinqiangli/codeparrot-ds-v2 with Docker Model Runner:
```
docker model run hf.co/gavinqiangli/codeparrot-ds-v2
```

codeparrot-ds-v2 / README.md

gavinqiangli

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metadata

license: mit
base_model: gpt2
tags:
  - generated_from_trainer
model-index:
  - name: codeparrot-ds-v2
    results: []

codeparrot-ds-v2

This model is trained from scratch from gpt2 on an python code dataset. It achieves the following results on the evaluation set:

Loss: 1.0617

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: 0.0005
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1000
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
2.5649	0.0766	5000	1.7424
1.6811	0.1533	10000	1.5239
1.5331	0.2299	15000	1.4213
1.4544	0.3065	20000	1.3544
1.3946	0.3832	25000	1.3049
1.3434	0.4598	30000	1.2571
1.2978	0.5365	35000	1.2146
1.2515	0.6131	40000	1.1707
1.2106	0.6897	45000	1.1335
1.1728	0.7664	50000	1.1002
1.1457	0.8430	55000	1.0769
1.1243	0.9196	60000	1.0646
1.1169	0.9963	65000	1.0617

Framework versions

Transformers 4.40.2
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1