Instructions to use aswain4/custom_coding_LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aswain4/custom_coding_LLM with Transformers:

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

pipe = pipeline("text-generation", model="aswain4/custom_coding_LLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aswain4/custom_coding_LLM")
model = AutoModelForCausalLM.from_pretrained("aswain4/custom_coding_LLM")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aswain4/custom_coding_LLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aswain4/custom_coding_LLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aswain4/custom_coding_LLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aswain4/custom_coding_LLM

SGLang

How to use aswain4/custom_coding_LLM 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 "aswain4/custom_coding_LLM" \
    --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": "aswain4/custom_coding_LLM",
		"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 "aswain4/custom_coding_LLM" \
        --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": "aswain4/custom_coding_LLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aswain4/custom_coding_LLM with Docker Model Runner:
```
docker model run hf.co/aswain4/custom_coding_LLM
```

aswain4 commited on Apr 20, 2025

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@@ -45,7 +45,6 @@ The goal of this model is to improve the quality and efficiency of code generati
 This model is designed to generate quality and efficient code in any programming language, but particularly for Python, given a natural language prompt. It can provide troubleshooting for bugged or broken code that is able to provide feedback on why the initial code was faulty and how the code was improved and fixed.
 ### Out-of-Scope Use
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
@@ -159,18 +158,6 @@ def create_training_arguments(path, learning_rate = 0.00001, epochs=2, eval_step
 [More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
 ## Model Card Contact
 - **Email:** yhp7nq@virginia.edu

 This model is designed to generate quality and efficient code in any programming language, but particularly for Python, given a natural language prompt. It can provide troubleshooting for bugged or broken code that is able to provide feedback on why the initial code was faulty and how the code was improved and fixed.
 ### Out-of-Scope Use
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 [More Information Needed]
 ## Model Card Contact
 - **Email:** yhp7nq@virginia.edu