Instructions to use Open4bits/llama-nexora-vector-v0.1-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Open4bits/llama-nexora-vector-v0.1-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("Open4bits/llama-nexora-vector-v0.1-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 Open4bits/llama-nexora-vector-v0.1-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 "Open4bits/llama-nexora-vector-v0.1-mlx-4Bit"

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": "Open4bits/llama-nexora-vector-v0.1-mlx-4Bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Open4bits/llama-nexora-vector-v0.1-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 "Open4bits/llama-nexora-vector-v0.1-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 Open4bits/llama-nexora-vector-v0.1-mlx-4Bit

Run Hermes

hermes

OpenClaw new

How to use Open4bits/llama-nexora-vector-v0.1-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 "Open4bits/llama-nexora-vector-v0.1-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 "Open4bits/llama-nexora-vector-v0.1-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"

MLX LM

How to use Open4bits/llama-nexora-vector-v0.1-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 "Open4bits/llama-nexora-vector-v0.1-mlx-4Bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Open4bits/llama-nexora-vector-v0.1-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": "Open4bits/llama-nexora-vector-v0.1-mlx-4Bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

fmasterpro27 commited on Apr 27

Commit

19a592b

verified ·

1 Parent(s): 5c7842c

Update README.md

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README.md +0 -45

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@@ -41,7 +41,6 @@ tags:
 - [Quantization Details](#quantization-details)
 - [Model Details](#model-details)
 - [Requirements](#requirements)
-- [Installation & Usage](#installation--usage)
 - [Capabilities](#capabilities)
 - [Limitations](#limitations)
 - [Intended Use](#intended-use)
@@ -112,50 +111,6 @@ This model is part of the **Llama-Nexora family** — a dedicated branch of Nexo
 ---
-## Installation & Usage
-### Install MLX
-```bash
-pip install mlx-lm
-```
-### Run Inference
-```python
-from mlx_lm import load, generate
-model, tokenizer = load("Open4bits/llama-nexora-vector-v0.1-mlx-4Bit")
-prompt = "Generate an SVG of a simple red circle."
-response = generate(model, tokenizer, prompt=prompt, verbose=True)
-print(response)
-```
-### Chat Template (Recommended)
-Since this model is based on an Instruct variant, using the chat template is recommended for best results:
-```python
-from mlx_lm import load, generate
-model, tokenizer = load("Open4bits/llama-nexora-vector-v0.1-mlx-4Bit")
-messages = [
-    {"role": "user", "content": "Generate an SVG of a simple blue square with rounded corners."}
-]
-prompt = tokenizer.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)
-response = generate(model, tokenizer, prompt=prompt, max_tokens=1024, verbose=True)
-print(response)
-```
----
 ## Capabilities
 llama-nexora-vector-v0.1-mlx-4Bit is designed to translate textual instructions into structured SVG code. The model is best suited for:

 - [Quantization Details](#quantization-details)
 - [Model Details](#model-details)
 - [Requirements](#requirements)
 - [Capabilities](#capabilities)
 - [Limitations](#limitations)
 - [Intended Use](#intended-use)
 ---
 ## Capabilities
 llama-nexora-vector-v0.1-mlx-4Bit is designed to translate textual instructions into structured SVG code. The model is best suited for: