Instructions to use pipenetwork/DeepSeek-V4-Flash-MLX-REAP50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use pipenetwork/DeepSeek-V4-Flash-MLX-REAP50 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("pipenetwork/DeepSeek-V4-Flash-MLX-REAP50") 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 pipenetwork/DeepSeek-V4-Flash-MLX-REAP50 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "pipenetwork/DeepSeek-V4-Flash-MLX-REAP50" --prompt "Once upon a time"
- Atomic Chat
Refresh model card
Browse files
README.md
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@@ -100,15 +100,15 @@ from deepseek_v4_mlx.load import load
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from deepseek_v4_mlx.generate import greedy_generate, load_tokenizer
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model, args = load("/path/to/this/repo")
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tok = load_tokenizer()
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ids = tok("The capital of France is")["input_ids"]
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print(tok.decode(greedy_generate(model, args, ids, max_new_tokens=32)))
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```
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**The
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## What this port implements
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from deepseek_v4_mlx.generate import greedy_generate, load_tokenizer
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model, args = load("/path/to/this/repo")
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tok = load_tokenizer("/path/to/this/repo") # bundled — no download
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ids = tok("The capital of France is")["input_ids"]
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print(tok.decode(greedy_generate(model, args, ids, max_new_tokens=32)))
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```
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**The tokenizer is bundled in this repo**, so nothing is fetched at runtime. (The base repo
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shipped no tokenizer at first — only `encoding/encoding_dsv4.py`, a prompt-*string* renderer;
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DeepSeek has since published one. DeepSeek-V3's tokenizer, used here in the interim, produces
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**identical ids** for text and identical special-token ids, so builds made either way agree.)
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## What this port implements
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