Instructions to use YCF-AI/CyberTiel-Coder-35B-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YCF-AI/CyberTiel-Coder-35B-MLX with MLX:

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

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("YCF-AI/CyberTiel-Coder-35B-MLX")
config = load_config("YCF-AI/CyberTiel-Coder-35B-MLX")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use YCF-AI/CyberTiel-Coder-35B-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 "YCF-AI/CyberTiel-Coder-35B-MLX"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "YCF-AI/CyberTiel-Coder-35B-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use YCF-AI/CyberTiel-Coder-35B-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 "YCF-AI/CyberTiel-Coder-35B-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 YCF-AI/CyberTiel-Coder-35B-MLX

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use YCF-AI/CyberTiel-Coder-35B-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 "YCF-AI/CyberTiel-Coder-35B-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 "YCF-AI/CyberTiel-Coder-35B-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"

CyberTiel-Coder-35B-MLX / omlx /model_settings.entry.json

YCF-AI

FP16 tensor cast (M1), bilingual card, figures, oMLX settings, recipe (BF16 build stays at rev 32e067d)

2aa5ea8 verified 11 days ago

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	{
	"_comment": "oMLX 0.7.0 ~/.omlx/model_settings.json entry. Key = directory name of the model under your model_dirs. 键名=模型目录名。",
	"version": 1,
	"models": {
	"CyberTiel-Coder-35B-MLX": {
	"max_context_window": 131072,
	"max_tokens": 32768,
	"temperature": 0.6,
	"top_p": 0.95,
	"top_k": 20,
	"repetition_penalty": 1.0,
	"force_sampling": false,
	"ttl_seconds": 1800,
	"model_alias": "CyberTiel-35B",
	"enable_thinking": true,
	"thinking_budget_enabled": false,
	"guided_grammar_enabled": false,
	"specprefill_enabled": false,
	"mtp_enabled": true,
	"vlm_mtp_enabled": false,
	"trust_remote_code": false
	}
	}
	}