Text Generation
MLX
mlx-lm
gemma
gemma-4
lora
voice-agent
desktop-automation
computer-use
flowcast
gemmaflow
apple-silicon
Instructions to use nsalerni/gemma-4-e2b-flowcast-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nsalerni/gemma-4-e2b-flowcast-v3 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("nsalerni/gemma-4-e2b-flowcast-v3") 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 nsalerni/gemma-4-e2b-flowcast-v3 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "nsalerni/gemma-4-e2b-flowcast-v3" --prompt "Once upon a time"
- Atomic Chat
docs: refresh benchmark scores (99.4% expanded quality)
Browse files
README.md
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| Core hard eval (117) | 100% | **100%** | — |
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| Core held-out (27) | 100% | **100%** | — |
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| Expanded hard (170) |
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| Expanded held-out (39) | 97.4% | **100%** | +2.6% |
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| Generalization suite | 91.4% | **97.1%** | +5.7% |
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| p50 latency | ~
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## Quick start (MLX, Apple Silicon)
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| Core hard eval (117) | 100% | **100%** | — |
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| Core held-out (27) | 100% | **100%** | — |
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| Expanded hard quality (170) | 98.2% | **99.4%** | +1.2% |
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| Expanded held-out (39) | 97.4% | **100%** | +2.6% |
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| Generalization suite | 91.4% | **97.1%** | +5.7% |
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| Core p50 latency | ~1028ms | **~1002ms** | ~same |
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Hard quality = task accuracy excluding latency SLA. v3 is the only variant that passes both production gates (100% core, ≥99% expanded quality).
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## Quick start (MLX, Apple Silicon)
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