Instructions to use Fadilahnuryasin/Assistant-Coding-Python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fadilahnuryasin/Assistant-Coding-Python with Transformers:

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

pipe = pipeline("text-generation", model="Fadilahnuryasin/Assistant-Coding-Python")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Fadilahnuryasin/Assistant-Coding-Python")
model = AutoModelForCausalLM.from_pretrained("Fadilahnuryasin/Assistant-Coding-Python", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Fadilahnuryasin/Assistant-Coding-Python with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fadilahnuryasin/Assistant-Coding-Python"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fadilahnuryasin/Assistant-Coding-Python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Fadilahnuryasin/Assistant-Coding-Python

SGLang

How to use Fadilahnuryasin/Assistant-Coding-Python 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 "Fadilahnuryasin/Assistant-Coding-Python" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fadilahnuryasin/Assistant-Coding-Python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Fadilahnuryasin/Assistant-Coding-Python" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fadilahnuryasin/Assistant-Coding-Python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Fadilahnuryasin/Assistant-Coding-Python with Docker Model Runner:
```
docker model run hf.co/Fadilahnuryasin/Assistant-Coding-Python
```

Assistant-Coding-Python / README.md

Fadilahnuryasin

Update README.md

ed05617 verified 7 days ago

preview code

Raw History Blame Contribute Delete

1.98 kB

	---
	library_name: transformers
	tags:
	- trl
	- sft
	- python
	- code
	- conversational
	license: mit
	---

	# Model Card for Assistant-Coding-Python

	## Model Details

	### Model Description

	Assistant-Coding-Python is a fine-tuned causal language model based on SmolLM2-360M. It is specifically trained to assist and answer various Python programming questions, ranging from implementing basic mathematical functions to data structure manipulation.

	- Developed by: Fadilahnuryasin
	- Model type: Causal Language Model (Fine-tuned with Supervised Fine-Tuning)
	- Language(s) (NLP): English, Python
	- License: MIT
	- Finetuned from model: HuggingFaceTB/SmolLM2-360M

	## Uses

	### Direct Use
	This model is designed to act as a coding assistant, helping users write Python code, solve basic logic problems, and understand Python syntax.

	### Out-of-Scope Use
	This model is not designed for deployment in critical systems requiring high safety verification or complex, enterprise-scale code generation without human supervision.

	## Bias, Risks, and Limitations
	As a small-scale model (360M parameters), it may occasionally produce inaccurate outputs on complex mathematical logic or incorrectly predict final execution results. Users are advised to review and test all generated code before use.

	## How to Get Started with the Model

	Use the code below to get started with the model using the Transformers library:

	```python
	from transformers import pipeline

	pipe = pipeline("text-generation", model="Fadilahnuryasin/Assistant-Coding-Python")
	messages = [
	{"role": "system", "content": "You are an expert and helpful Python programming assistant."},
	{"role": "user", "content": "Create a Python function to calculate the area of a rectangle."}
	]
	prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	outputs = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)
	print(outputs[0]["generated_text"])