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README.md
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---
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language:
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- en
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base_model:
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- mistralai/Devstral-Small-2507
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pipeline_tag: text-generation
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tags:
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- mistral
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- neuralmagic
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- redhat
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- llmcompressor
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- quantized
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- FP8
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- compressed-tensors
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license: mit
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license_name: mit
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name: RedHatAI/Devstral-Small-2507
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description: This model was obtained by quantizing weights and activations of Devstral-Small-2507 to FP8 data type.
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readme: https://huggingface.co/RedHatAI/Devstral-Small-2507-FP8-Dynamic/main/README.md
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tasks:
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- text-to-text
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provider: mistralai
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---
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# Devstral-Small-2507-FP8-Dynamic
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## Model Overview
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- **Model Architecture:** MistralForCausalLM
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- **Input:** Text
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- **Output:** Text
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- **Model Optimizations:**
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- **Activation quantization:** FP8
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- **Weight quantization:** FP8
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- **Release Date:** 08/28/2025
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- **Version:** 1.0
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- **Model Developers:** Red Hat (Neural Magic)
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### Model Optimizations
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This model was obtained by quantizing weights and activations of [Devstral-Small-2507](https://huggingface.co/mistralai/Devstral-Small-2507) to FP8 data type.
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This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%).
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Weight quantization also reduces disk size requirements by approximately 50%.
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## Deployment
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
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```bash
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vllm serve RedHatAI/Devstral-Small-2507-FP8-Dynamic --tensor-parallel-size 1 --tokenizer_mode mistral
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```
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## Evaluation
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The model was evaluated on popular coding tasks (HumanEval, HumanEval+, MBPP, MBPP+) via [EvalPlus](https://github.com/evalplus/evalplus) and vllm backend (v0.10.1.1).
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For evaluations, we run greedy sampling and report pass@1
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### Accuracy
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| | Recovery (%) | mistralai/Devstral-Small-2507 | RedHatAI/Devstral-Small-2507-FP8-Dynamic<br>(this model) |
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| --------------------------- | :----------: | :------------------: | :--------------------------------------------------: |
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| HumanEval | 98.50 | 89.0 | 89.6 |
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| HumanEval+ | 99.88 | 81.1 | 82.9 |
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| MBPP | 101.21 | 77.5 | 75.4 |
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| MBPP+ | 101.21 | 66.1 | 64.8 |
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| **Average Score** | **99.68** | **78.43** | **78.18** |
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