Instructions to use ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit 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("ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit")

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 ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit"

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": "ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit 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 "ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit"

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 ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit"

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 "ahmedandaloes/CyberStrike-OffSec-35B-MLX-4bit" \
  --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"

ahmedandaloes commited on Jul 7

Commit

5ec62f4

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1 Parent(s): b4f6a04

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@@ -63,8 +63,17 @@ may cause a small deviation at lower bit-widths).
 ## Usage
 > **Note:** this model uses the transformers-5 `TokenizersBackend` tokenizer.
-> `mlx-lm` 0.31.3 crashes at import under transformers 5 due to an unrelated
-> `AutoTokenizer.register(...)` call. Patch it at runtime **before** importing
 > `mlx_lm` (harmless — registers an mlx-lm helper this model does not use):
 ```python

 ## Usage
 > **Note:** this model uses the transformers-5 `TokenizersBackend` tokenizer.
+> `mlx-lm` **0.31.3 (current pypi release)** crashes at import under transformers 5
+> due to an unrelated `AutoTokenizer.register(...)` call.
+>
+> **Cleanest fix — install mlx-lm from git** (already patched on `main`, see
+> [ml-explore/mlx-lm#1458](https://github.com/ml-explore/mlx-lm/issues/1458)):
+> ```bash
+> pip install "git+https://github.com/ml-explore/mlx-lm" "transformers>=5"
+> ```
+> Then the snippet below works **without** the runtime patch.
+>
+> **Or, staying on the 0.31.3 release**, patch it at runtime **before** importing
 > `mlx_lm` (harmless — registers an mlx-lm helper this model does not use):
 ```python