Instructions to use TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TokenAI-zer/Xing4.0-29B-A4B-4bit-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("TokenAI-zer/Xing4.0-29B-A4B-4bit-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 TokenAI-zer/Xing4.0-29B-A4B-4bit-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 "TokenAI-zer/Xing4.0-29B-A4B-4bit-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": "TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use TokenAI-zer/Xing4.0-29B-A4B-4bit-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 "TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use TokenAI-zer/Xing4.0-29B-A4B-4bit-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 "TokenAI-zer/Xing4.0-29B-A4B-4bit-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 TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TokenAI-zer/Xing4.0-29B-A4B-4bit-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 "TokenAI-zer/Xing4.0-29B-A4B-4bit-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 "TokenAI-zer/Xing4.0-29B-A4B-4bit-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"

TokenAI-zer commited on 9 days ago

Commit

7d9990c

verified ·

1 Parent(s): 7925263

Update references after account rename

Browse files

Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -44,7 +44,7 @@ at the model file.
 ## Pick a variant
-| | [4bit](https://huggingface.co/suzu89/Xing4.0-29B-A4B-4bit-MLX) | [6bit](https://huggingface.co/suzu89/Xing4.0-29B-A4B-6bit-MLX) | [8bit](https://huggingface.co/suzu89/Xing4.0-29B-A4B-8bit-MLX) |
 |---|---|---|---|
 | Weights on disk | 15.51 GiB (16.65 GB), 4 shards | 22.37 GiB (24.02 GB), 5 shards | 29.23 GiB (31.38 GB), 6 shards |
 | Effective precision | 4.514 bits per weight | 6.512 bits per weight | 8.509 bits per weight |
@@ -100,7 +100,7 @@ from mlx_lm import load, generate
 # the custom tokenizer is loaded from the repo, so both flags are needed
 model, tokenizer = load(
-    "suzu89/Xing4.0-29B-A4B-4bit-MLX",
     tokenizer_config={"trust_remote_code": True},
     trust_remote_code=True,
 )
@@ -194,9 +194,9 @@ Distributed under the **Apache License 2.0**, inherited from
 ```bibtex
 @misc{xing4-29b-a4b-mlx-4bit,
   title        = {Xing4.0-29B-A4B-4bit-MLX: MLX 4-bit quantization of Xing4.0-29B-A4B},
-  author       = {suzu89},
   year         = {2026},
-  howpublished = {\url{https://huggingface.co/suzu89/Xing4.0-29B-A4B-4bit-MLX}},
   note         = {Unofficial quantization of XingChen-AGI/Xing4.0-29B-A4B}
 }

 ## Pick a variant
+| | [4bit](https://huggingface.co/TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX) | [6bit](https://huggingface.co/TokenAI-zer/Xing4.0-29B-A4B-6bit-MLX) | [8bit](https://huggingface.co/TokenAI-zer/Xing4.0-29B-A4B-8bit-MLX) |
 |---|---|---|---|
 | Weights on disk | 15.51 GiB (16.65 GB), 4 shards | 22.37 GiB (24.02 GB), 5 shards | 29.23 GiB (31.38 GB), 6 shards |
 | Effective precision | 4.514 bits per weight | 6.512 bits per weight | 8.509 bits per weight |
 # the custom tokenizer is loaded from the repo, so both flags are needed
 model, tokenizer = load(
+    "TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX",
     tokenizer_config={"trust_remote_code": True},
     trust_remote_code=True,
 )
 ```bibtex
 @misc{xing4-29b-a4b-mlx-4bit,
   title        = {Xing4.0-29B-A4B-4bit-MLX: MLX 4-bit quantization of Xing4.0-29B-A4B},
+  author       = {TokenAI-zer},
   year         = {2026},
+  howpublished = {\url{https://huggingface.co/TokenAI-zer/Xing4.0-29B-A4B-4bit-MLX}},
   note         = {Unofficial quantization of XingChen-AGI/Xing4.0-29B-A4B}
 }