Instructions to use encredible/Gaiel-1.5B-Coding-Tuned-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use encredible/Gaiel-1.5B-Coding-Tuned-MLX with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("encredible/Gaiel-1.5B-Coding-Tuned-MLX")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use encredible/Gaiel-1.5B-Coding-Tuned-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "encredible/Gaiel-1.5B-Coding-Tuned-MLX"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "encredible/Gaiel-1.5B-Coding-Tuned-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use encredible/Gaiel-1.5B-Coding-Tuned-MLX with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "encredible/Gaiel-1.5B-Coding-Tuned-MLX"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "encredible/Gaiel-1.5B-Coding-Tuned-MLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "encredible/Gaiel-1.5B-Coding-Tuned-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use encredible/Gaiel-1.5B-Coding-Tuned-MLX with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "encredible/Gaiel-1.5B-Coding-Tuned-MLX"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default encredible/Gaiel-1.5B-Coding-Tuned-MLX

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use encredible/Gaiel-1.5B-Coding-Tuned-MLX with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "encredible/Gaiel-1.5B-Coding-Tuned-MLX"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "encredible/Gaiel-1.5B-Coding-Tuned-MLX" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

⚡ Gaiel-1.5B-Coding-Tuned-MLX

Organization & Developer: JK Universe Base Architecture: Qwen2.5-Coder (Alibaba) (Base Foundation: Qwen/Qwen2.5-Coder-1.5B-Instruct) Parameter Size: 1.5B Primary Specialization: Coding & Algorithm Dedicated

📌 Model Overview

High-efficiency 1.5B programming model fine-tuned on 80k Evol-Instruct-Code samples from Qwen2.5-Coder-1.5B-Instruct base architecture.

🏛️ Base Architecture Heritage

Underlying Base Architecture: Qwen2.5-Coder (Alibaba)
Original Base Model: Qwen/Qwen2.5-Coder-1.5B-Instruct
Fine-Tuning & Model Release: JK Universe

💻 Usage with MLX (Apple Silicon)

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("encredible/Gaiel-1.5B-Coding-Tuned-MLX")

messages = [
    {"role": "system", "content": "당신은 한국어와 전문 지식에 특화된 AI 어시스턴트, Gaiel(가이엘)입니다."},
    {"role": "user", "content": "안녕하세요! 간단한 자기소개와 주요 강점을 알려주세요."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=prompt, max_tokens=1024, verbose=True)

🏢 Organization Information

Developer: JK Universe
Official Hub Benchmark Dataset: encredible/gaiel-mlx-benchmarks

Downloads last month: 387

Safetensors

Model size

0.2B params

Tensor type

F16

U32

MLX

Hardware compatibility

4-bit

Model tree for encredible/Gaiel-1.5B-Coding-Tuned-MLX

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B-Instruct

Quantized

(163)

this model