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 Settings

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 21, 2025

Commit

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@@ -181,13 +181,6 @@ print(is_palindrome(12321))
 # In the given example, the number 12321 is a palindrome, so the function returns True.
 ```
-The function `is_palindrome` takes a number as input and converts it into a string using the `str()` function. It then checks if the string is equal to its reversed version (`str_num[::-1]`). If they are equal, it means the number is a palindrome and the function returns `True`. Otherwise, it returns `False`.
-In the example code, we test the function with the number `12321`. The function call `is_palindrome(num)` returns `True` because `12321` is a palindrome. Finally, the result is printed to the console.
-I hope this helps! Let me know if you have any further questions.
-```
 ## Training Details
 ### Training Data

 # In the given example, the number 12321 is a palindrome, so the function returns True.
 ```
 ## Training Details
 ### Training Data