Text Generation
MLX
Safetensors
English
glm
glm4
MOE
pruning
compression
reap
cerebras
code
function-calling
agentic
Instructions to use mlx-community/GLM-4.7-REAP-50-mixed-3-4-bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/GLM-4.7-REAP-50-mixed-3-4-bits with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("mlx-community/GLM-4.7-REAP-50-mixed-3-4-bits") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use mlx-community/GLM-4.7-REAP-50-mixed-3-4-bits with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "mlx-community/GLM-4.7-REAP-50-mixed-3-4-bits" --prompt "Once upon a time"
mlx-community/GLM-4.7-REAP-50-mixed-3-4-bits
This model mlx-community/GLM-4.7-REAP-50-mixed-3-4-bits was converted to MLX format from 0xSero/GLM-4.7-REAP-50 using mlx-lm version 0.30.0.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/GLM-4.7-REAP-50-mixed-3-4-bits")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_dict=False,
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
185B params
Tensor type
BF16
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Base model
0xSero/GLM-4.7-185B