Instructions to use vamazing/Koa-AI-v1-Code-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vamazing/Koa-AI-v1-Code-3B with Transformers:

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

pipe = pipeline("text-generation", model="vamazing/Koa-AI-v1-Code-3B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("vamazing/Koa-AI-v1-Code-3B")
model = AutoModelForMultimodalLM.from_pretrained("vamazing/Koa-AI-v1-Code-3B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vamazing/Koa-AI-v1-Code-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vamazing/Koa-AI-v1-Code-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vamazing/Koa-AI-v1-Code-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vamazing/Koa-AI-v1-Code-3B

SGLang

How to use vamazing/Koa-AI-v1-Code-3B 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 "vamazing/Koa-AI-v1-Code-3B" \
    --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": "vamazing/Koa-AI-v1-Code-3B",
		"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 "vamazing/Koa-AI-v1-Code-3B" \
        --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": "vamazing/Koa-AI-v1-Code-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use vamazing/Koa-AI-v1-Code-3B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vamazing/Koa-AI-v1-Code-3B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vamazing/Koa-AI-v1-Code-3B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vamazing/Koa-AI-v1-Code-3B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="vamazing/Koa-AI-v1-Code-3B",
    max_seq_length=2048,
)

Docker Model Runner
How to use vamazing/Koa-AI-v1-Code-3B with Docker Model Runner:
```
docker model run hf.co/vamazing/Koa-AI-v1-Code-3B
```

Koa-AI-v1-Code-3B / README.md

vamazing

Update README.md

8693ea8 verified about 17 hours ago

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metadata

license: apache-2.0
base_model: mistralai/Ministral-3b-instruct
tags:
  - ministral
  - code
  - coding
  - qlora
  - unsloth
  - text-generation
pipeline_tag: text-generation
library_name: transformers
language:
  - en
datasets:
  - greghavens/fable-5-coding-and-debugging-traces

Koa-AI v1 Code 3B

Koa-AI-v1-Code-3B is an ultra-lightweight, high-efficiency 3B parameter model fine-tuned for code generation, multi-step agentic planning, and debugging tasks.

Built on top of mistralai/Ministral-3b-instruct using Unsloth and QLoRA, it is optimized to run blazingly fast on consumer hardware, local edge devices, and laptop GPUs without sacrificing code reasoning capabilities.

⚡ Highlights

Base Model: mistralai/Ministral-3b-instruct
Dataset: Fine-tuned on multi-turn debugging and agentic coding trace data (greghavens/fable-5-coding-and-debugging-traces).
Efficiency: Lightweight 3B parameter size allows low-latency, real-time code completion in local IDE extensions (e.g., Continue, VS Code).
Extended Context Support: Native context window up to 128,000 tokens (SFT fine-tuned at a 2,048 sequence length cap).

📊 Training Specifications

Parameter	Value
Architecture	Ministral 3B Instruct
Precision	4-bit NormalFloat (NF4) / BF16 mixed
Fine-Tuning Method	QLoRA 4-bit
Target Modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
LoRA Config	$r = 16$, $\alpha = 32$, Dropout = $0.0$
Learning Rate	`2e-4`
Optimizer	AdamW 8-bit
Frameworks	Unsloth, PyTorch, Hugging Face Transformers

💻 Quickstart & Usage

Running with `transformers` (Python)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "vamazing/Koa-AI-v1-Code-3B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

prompt = "Write a Python function to check if a number is prime and optimize it for speed."
messages = [{"role": "user", "content": prompt}]

formatted_prompt = tokenizer.apply_chat_template(
    messages, 
    tokenize=False, 
    add_generation_prompt=True
)

inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.7)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Koa-AI v1 Code 3B

⚡ Highlights

📊 Training Specifications

💻 Quickstart & Usage

Running with transformers (Python)

Running with `transformers` (Python)