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115 lines
3.7 KiB
Markdown
115 lines
3.7 KiB
Markdown
<!--Copyright 2025 the HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be rendered properly in your Markdown viewer.
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-->
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*This model was contributed to Hugging Face Transformers on 2025-12-01.*
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# Ministral3
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## Overview
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A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.
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This model is the instruct post-trained version, fine-tuned for instruction tasks, making it ideal for chat and instruction based use cases.
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The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware.
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Key features:
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- Vision: Enables the model to analyze images and provide insights based on visual content, in addition to text.
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- Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic.
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- System Prompt: Maintains strong adherence and support for system prompts.
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- Agentic: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
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- Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
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- Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes.
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- Large Context Window: Supports a 256k context window.
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## Usage examples
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```python
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import torch
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from transformers import Mistral3ForConditionalGeneration, MistralCommonBackend
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model_id = "mistralai/Ministral-3-3B-Instruct-2512"
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tokenizer = MistralCommonBackend.from_pretrained(model_id)
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model = Mistral3ForConditionalGeneration.from_pretrained(
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model_id, device_map="auto"
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)
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image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.",
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},
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{"type": "image_url", "image_url": {"url": image_url}},
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],
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},
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]
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tokenized = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to(model.device)
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tokenized["input_ids"] = tokenized["input_ids"].to(device="cuda")
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tokenized["pixel_values"] = tokenized["pixel_values"].to(dtype=torch.bfloat16, device="cuda")
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image_sizes = [tokenized["pixel_values"].shape[-2:]]
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output = model.generate(
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**tokenized,
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image_sizes=image_sizes,
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max_new_tokens=512,
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)[0]
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decoded_output = tokenizer.decode(output[len(tokenized["input_ids"][0]):])
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print(decoded_output)
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```
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## Ministral3Config
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[[autodoc]] Ministral3Config
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## Ministral3PreTrainedModel
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[[autodoc]] Ministral3PreTrainedModel
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- forward
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## Ministral3Model
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[[autodoc]] Ministral3Model
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- forward
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## Ministral3ForCausalLM
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[[autodoc]] Ministral3ForCausalLM
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## Ministral3ForSequenceClassification
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[[autodoc]] Ministral3ForSequenceClassification
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## Ministral3ForTokenClassification
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[[autodoc]] Ministral3ForTokenClassification
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## Ministral3ForQuestionAnswering
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[[autodoc]] Ministral3ForQuestionAnswering
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