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 / README.md

amnottdevv

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---
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: peft
pipeline_tag: text-generation
language:
- id
- en
tags:
- base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
- lora
- peft
- sft
- transformers
- trl
- unsloth
- indonesian
- chatbot
- reasoning
license: apache-2.0
---
# 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](https://huggingface.co/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

1. **Factual hallucination** — The model can produce incorrect facts with a convincing format. Always verify factual outputs.

2. **Persona inconsistency** — Occasionally generates random names (e.g., Xaeros, Aida) for meta questions. Common issue in 1.5B models.

3. **Context contamination** — Topics from previous turns may leak into subsequent answers. Reset history between distinct topics.

4. **Math (4+ digits)** — Low accuracy for large-number arithmetic.

5. **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

```bash
pip install transformers peft accelerate torch

	---
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	library_name: peft
	pipeline_tag: text-generation
	language:
	- id
	- en
	tags:
	- base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
	- lora
	- peft
	- sft
	- transformers
	- trl
	- unsloth
	- indonesian
	- chatbot
	- reasoning
	license: apache-2.0
	---

	# 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](https://huggingface.co/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

	1. Factual hallucination — The model can produce incorrect facts with a convincing format. Always verify factual outputs.

	2. Persona inconsistency — Occasionally generates random names (e.g., Xaeros, Aida) for meta questions. Common issue in 1.5B models.

	3. Context contamination — Topics from previous turns may leak into subsequent answers. Reset history between distinct topics.

	4. Math (4+ digits) — Low accuracy for large-number arithmetic.

	5. 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

	```bash
	pip install transformers peft accelerate torch