Instructions to use amnottdevv/Hazel-1o-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amnottdevv/Hazel-1o-v1 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "amnottdevv/Hazel-1o-v1")

Transformers

How to use amnottdevv/Hazel-1o-v1 with Transformers:

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

pipe = pipeline("text-generation", model="amnottdevv/Hazel-1o-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("amnottdevv/Hazel-1o-v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amnottdevv/Hazel-1o-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amnottdevv/Hazel-1o-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amnottdevv/Hazel-1o-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amnottdevv/Hazel-1o-v1

SGLang

How to use amnottdevv/Hazel-1o-v1 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 "amnottdevv/Hazel-1o-v1" \
    --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": "amnottdevv/Hazel-1o-v1",
		"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 "amnottdevv/Hazel-1o-v1" \
        --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": "amnottdevv/Hazel-1o-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use amnottdevv/Hazel-1o-v1 with Docker Model Runner:
```
docker model run hf.co/amnottdevv/Hazel-1o-v1
```

Hazel-1o v1

Hazel-1o is an Indonesian chat model fine-tuned from Qwen2.5-1.5B-Instruct using LoRA (Low-Rank Adaptation). It is designed as a friendly AI assistant named Hazel-1o with structured reasoning via a custom <thinking> tag.

Model Details

Model Description

Developed by: amnottdevv
Model type: Causal Language Model (LoRA adapter)
Base model: Qwen/Qwen2.5-1.5B-Instruct
Language(s) (NLP): Bahasa Indonesia (primary), English (secondary)
License: Apache 2.0
Finetuned from: Qwen2.5-1.5B-Instruct

Model Sources

Repository: https://huggingface.co/amnottdevv/Hazel-1o-v1
Base model: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct

Uses

Direct Use

This model is suitable for:

Indonesian persona chatbot — casual conversation as the "Hazel-1o" persona
Structured reasoning — basic math problems using the <thinking> tag
Basic C++ & Python coding — simple code generation with Indonesian explanations
Indonesian FAQ — answering general questions in a friendly tone

Out-of-Scope Use

This model is NOT suitable for:

❌ Production use — outputs require human verification
❌ Illegal use —

Bias, Risks, and Limitations

Known Limitations

Factual hallucination — The model can produce incorrect facts with a convincing format. Always verify factual outputs.
Persona inconsistency — Occasionally generates random names (e.g., Xaeros, Aida) for meta questions. Common issue in 1.5B models.
Context contamination — Topics from previous turns may leak into subsequent answers. Reset history between distinct topics.
Math (4+ digits) — Low accuracy for large-number arithmetic.
Code debugging — Unable to analyze code errors accurately.

Recommendations

Always verify outputs for factual topics
Use temperature 0.4–0.5 for the most stable results
Reset history before switching to unrelated topics
Do not use in production without human supervision

How to Get Started

Installation

pip install transformers peft accelerate torch

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