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

Libraries

How to use pooraddyy/Koda-0.5B with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pooraddyy/Koda-0.5B")
model = AutoModelForCausalLM.from_pretrained("pooraddyy/Koda-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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pooraddyy/Koda-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pooraddyy/Koda-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": "pooraddyy/Koda-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pooraddyy/Koda-0.5B

SGLang

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

Docker Model Runner
How to use pooraddyy/Koda-0.5B with Docker Model Runner:
```
docker model run hf.co/pooraddyy/Koda-0.5B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Koda-0.5B

A fine-tuned version of Qwen2.5-Coder-0.5B-Instruct for Python code generation from natural-language instructions. This is the full merged model — LoRA weights merged into the base, so it loads directly with transformers, no PEFT needed.

Model details

Base model: Qwen/Qwen2.5-Coder-0.5B-Instruct by the Qwen team (Alibaba Cloud) — https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct

Training details

Setting	Value
Method	LoRA via PEFT (merged into base after training)
Rank / alpha / dropout	16 / 32 / 0.05
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Trainable params	~8.8M (1.75% of 0.5B)
Dataset	`iamtarun/python_code_instructions_18k_alpaca` (subset)
Train / eval examples	1,400 / 100
Epochs	2
Learning rate	2e-4, cosine schedule, 20 warmup steps
Batch size	1 × 8 grad-accum steps (effective 8)
Precision / device	bfloat16, CPU-only
Training time	~~2.26 hours (~~48 tokens/sec)

Results

Final train loss: 0.78
Final eval loss: 0.82 (stable across epochs — no overfitting observed)

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "pooraddyy/Koda-0.5B"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16)
model.eval()

prompt = (
    "<|im_start|>user\n"
    "Write a python function to sort a list of dictionaries by age.\n"
    "<|im_end|>\n<|im_start|>assistant\n"
)
inputs = tok(prompt, return_tensors="pt")
with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=120, do_sample=False,
                         pad_token_id=tok.eos_token_id)
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Expected output:

def sort_by_age(list_of_dicts):
    # Sort the list of dictionaries by age
    list_of_dicts.sort(key=lambda x: x['age'])
    return list_of_dicts

Intended use

Generating short Python functions/snippets from English instructions.
Learning / experimentation with small-model fine-tuning.

Limitations

0.5B parameters is a small model — keep expectations modest. It can produce incorrect or insecure code.
Trained only on short Python instruction examples; performance on other languages, long programs, or complex reasoning is limited.
Always review generated code before running it.

License

Apache 2.0. This model is derived from Qwen2.5-Coder-0.5B-Instruct — all credit for the base model belongs to the Qwen team (Alibaba Cloud), and the base model's original license and attribution are retained.

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Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for pooraddyy/Koda-0.5B

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-Coder-0.5B

Finetuned

Qwen/Qwen2.5-Coder-0.5B-Instruct

Finetuned

(110)

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Quantizations

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