Instructions to use Siddh07ETH/Atlas-Coder-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Siddh07ETH/Atlas-Coder-0.5B with Transformers:

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

pipe = pipeline("text-generation", model="Siddh07ETH/Atlas-Coder-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Siddh07ETH/Atlas-Coder-0.5B")
model = AutoModelForCausalLM.from_pretrained("Siddh07ETH/Atlas-Coder-0.5B", 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 Siddh07ETH/Atlas-Coder-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Siddh07ETH/Atlas-Coder-0.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Siddh07ETH/Atlas-Coder-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Siddh07ETH/Atlas-Coder-0.5B

SGLang

How to use Siddh07ETH/Atlas-Coder-0.5B 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 "Siddh07ETH/Atlas-Coder-0.5B" \
    --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": "Siddh07ETH/Atlas-Coder-0.5B",
		"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 "Siddh07ETH/Atlas-Coder-0.5B" \
        --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": "Siddh07ETH/Atlas-Coder-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Siddh07ETH/Atlas-Coder-0.5B with Docker Model Runner:
```
docker model run hf.co/Siddh07ETH/Atlas-Coder-0.5B
```

Atlas-Coder-0.5B

File size: 739 Bytes

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{%- if messages[0]['role'] == 'system' -%}
 {%- set system_message = messages[0]['content'] -%}
 {%- set loop_messages = messages[1:] -%}
{%- else -%}
 {%- set system_message = 'You are Atlas-Coder, an elite AI coding assistant created by Siddharth N.R. You write clean, efficient, and well-documented Python code. You specialize in code generation, completion, debugging, refactoring, algorithm implementation, and software engineering reasoning.' -%}
 {%- set loop_messages = messages -%}
{%- endif -%}
<|im_start|>system
{{ system_message }}<|im_end|>
{% for message in loop_messages %}
<|im_start|>{{ message['role'] }}
{{ message['content'] }}<|im_end|>
{% endfor %}
{%- if add_generation_prompt -%}<|im_start|>assistant
{%- endif -%}