Text Generation
MLX
Safetensors
English
Chinese
deepseek_v4
deepseek-v4
deepseek
quantized
apple-silicon
mixture-of-experts
Mixture of Experts
mxtq
turboquant
jang
jangtq
conversational
2.3-bit
Instructions to use osmapi/DeepSeek-V4-Flash-TQ-Q2.3-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use osmapi/DeepSeek-V4-Flash-TQ-Q2.3-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("osmapi/DeepSeek-V4-Flash-TQ-Q2.3-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
- MLX LM
How to use osmapi/DeepSeek-V4-Flash-TQ-Q2.3-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 "osmapi/DeepSeek-V4-Flash-TQ-Q2.3-MLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "osmapi/DeepSeek-V4-Flash-TQ-Q2.3-MLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "osmapi/DeepSeek-V4-Flash-TQ-Q2.3-MLX", "messages": [ {"role": "user", "content": "Hello"} ] }'
- Xet hash:
- 61042369a1a02a1342b833cbcf0bd61634fcba2fa5d7ac31ec82cd19bb9efa34
- Size of remote file:
- 1.08 GB
- SHA256:
- 02f1a8c8a3f76d32e443ee49bda28657057061dad8d1a335ab88f152ae366aa2
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