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

Libraries

How to use infinitylogesh/statscoder with Transformers:

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

pipe = pipeline("text-generation", model="infinitylogesh/statscoder", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("infinitylogesh/statscoder", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("infinitylogesh/statscoder", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use infinitylogesh/statscoder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/infinitylogesh/statscoder

SGLang

How to use infinitylogesh/statscoder 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 "infinitylogesh/statscoder" \
    --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": "infinitylogesh/statscoder",
		"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 "infinitylogesh/statscoder" \
        --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": "infinitylogesh/statscoder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

statscoder

File size: 976 Bytes

---
license: openrail
datasets:
- bigcode/the-stack-dedup
library_name: transformers
tags:
- code_generation
- R programming
- sas
- santacoder
---

# Statscoder

This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on `R` and `SAS` language repositories in [the stack ](https://huggingface.co/datasets/bigcode/the-stack-dedup) dataset.


## Training procedure

The model was finetuned using the code adapted from [loubnabnl/santacoder-finetuning](https://github.com/loubnabnl/santacoder-finetuning). Adapted to handle multiple subsets of datasets and it is [here](https://github.com/infinitylogesh/santacoder-finetuning).

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- optimizer: adafactor
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1600
- seq_length: 1024
- no_fp16