Instructions to use lmstudio-community/GLM-4.7-Flash-MLX-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lmstudio-community/GLM-4.7-Flash-MLX-6bit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="lmstudio-community/GLM-4.7-Flash-MLX-6bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lmstudio-community/GLM-4.7-Flash-MLX-6bit")
model = AutoModelForCausalLM.from_pretrained("lmstudio-community/GLM-4.7-Flash-MLX-6bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use lmstudio-community/GLM-4.7-Flash-MLX-6bit 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("lmstudio-community/GLM-4.7-Flash-MLX-6bit")

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
LM Studio

vLLM

How to use lmstudio-community/GLM-4.7-Flash-MLX-6bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lmstudio-community/GLM-4.7-Flash-MLX-6bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmstudio-community/GLM-4.7-Flash-MLX-6bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lmstudio-community/GLM-4.7-Flash-MLX-6bit

SGLang

How to use lmstudio-community/GLM-4.7-Flash-MLX-6bit with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lmstudio-community/GLM-4.7-Flash-MLX-6bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmstudio-community/GLM-4.7-Flash-MLX-6bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lmstudio-community/GLM-4.7-Flash-MLX-6bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmstudio-community/GLM-4.7-Flash-MLX-6bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Pi new

How to use lmstudio-community/GLM-4.7-Flash-MLX-6bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "lmstudio-community/GLM-4.7-Flash-MLX-6bit"

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": "lmstudio-community/GLM-4.7-Flash-MLX-6bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lmstudio-community/GLM-4.7-Flash-MLX-6bit 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 "lmstudio-community/GLM-4.7-Flash-MLX-6bit"

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 lmstudio-community/GLM-4.7-Flash-MLX-6bit

Run Hermes

hermes

MLX LM

How to use lmstudio-community/GLM-4.7-Flash-MLX-6bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "lmstudio-community/GLM-4.7-Flash-MLX-6bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "lmstudio-community/GLM-4.7-Flash-MLX-6bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "lmstudio-community/GLM-4.7-Flash-MLX-6bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use lmstudio-community/GLM-4.7-Flash-MLX-6bit with Docker Model Runner:
```
docker model run hf.co/lmstudio-community/GLM-4.7-Flash-MLX-6bit
```

📋 Documentation Enhancement Suggestion

by CroviaTrust - opened Feb 14

Discussion

CroviaTrust

Feb 14

📋 Documentation Enhancement Suggestion

This observation was generated by Crovia — the AI transparency observation layer.

Crovia does not accuse or judge. It observes publicly available information and suggests improvements.

📊 Quick Stats

Metric	Value
Source	huggingface
Downloads	898536
Likes	2
Last Updated	2026-02-11

💻 Ready-to-Use Code

from transformers import AutoModel, AutoTokenizer

model_id = "lmstudio-community/GLM-4.7-Flash-MLX-6bit"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id)

# Example usage
inputs = tokenizer("Hello, world!", return_tensors="pt")
outputs = model(**inputs)

📚 Citation

If you use this model, please cite:

@misc {lmstudio_community_GLM_4_7_Flash_MLX_6bit_2026,
  author = {lmstudio-community},
  title = {lmstudio-community/GLM-4.7-Flash-MLX-6bit},
  year = {2026},
  url = {https://huggingface.co/lmstudio-community/GLM-4.7-Flash-MLX-6bit},
  note = {Accessed via CROVIA transparency registry}
}

⚖️ EU AI Act Compliance Checklist

Training data disclosed
License clearly stated
Intended use documented
Model limitations documented
Evaluation metrics provided
Bias/fairness analysis

🔍 Training Data Transparency

Training Data Status: Documentation not found

No training data section was observed in the public model card.

This is an observation, not an accusation. Many valid reasons exist for this status.

If you'd like to improve documentation, consider adding:

Dataset names and versions used
Data collection methodology
Preprocessing steps applied
Known limitations

This may help users understand your model better and prepare for upcoming transparency requirements (e.g., EU AI Act).

Enhancement generated by CROVIA · Package ID: f1706a256a4c
Generated at: 2026-02-11T17:07:28.559776Z

This suggestion was generated by Crovia — the AI transparency observation layer.

Crovia does not accuse or judge. It observes publicly available information and suggests documentation improvements.

If this suggestion is helpful, consider adding the recommended sections to your model card.
If not applicable, feel free to close this discussion.

Learn more: croviatrust.com · What is Crovia?

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment