Text Generation
MLX
Safetensors
deepseek_v4
jang
jangtq
jangtq2
jangtq-prestack
mxtq
deepseek
deepseek-v4
deepseek-v4-flash
Mixture of Experts
mla
hash-layers
mtp
apple-silicon
osaurus
Instructions to use OsaurusAI/DeepSeek-V4-Flash-JANGTQ2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OsaurusAI/DeepSeek-V4-Flash-JANGTQ2 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("OsaurusAI/DeepSeek-V4-Flash-JANGTQ2") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use OsaurusAI/DeepSeek-V4-Flash-JANGTQ2 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "OsaurusAI/DeepSeek-V4-Flash-JANGTQ2" --prompt "Once upon a time"
File size: 526 Bytes
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{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Hello"
},
{
"role": "assistant",
"reasoning_content": "The user said hello, I should greet back.",
"content": "Hi there! How can I help you?"
},
{
"role": "user",
"content": "What is the capital of France?"
},
{
"role": "assistant",
"reasoning_content": "The user asks about the capital of France. It is Paris.",
"content": "The capital of France is Paris."
}
] |