Meta-Llama-3.1-70B-Instruct
unsloth基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
입력 텍스트→ 출력 텍스트
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗DISCOVER & COMPARE
AI 모델 탐색 1038
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
입력 텍스트→ 출력 텍스트
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗Meta-Llama-3.1-8B
unslothWe have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. - This conversational notebook is useful for ShareGPT ChatML / Vicuna templates. - This text completion notebook is for raw text. This DPO notebook replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗Meta-Llama-3.1-8B-bnb-4bit
unslothWe have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗Meta-Llama-3.1-8B-Instruct
unslothWe have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. - This conversational notebook is useful for ShareGPT ChatML / Vicuna templates. - This text completion notebook is for raw text. This DPO notebook replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
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입력 텍스트→ 출력 텍스트
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. - This conversational notebook is useful for ShareGPT ChatML / Vicuna templates. - This text completion notebook is for raw text. This DPO notebook replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗Unsloth's Dynamic 4-bit Quants is selectively quantized, greatly improving accuracy over standard 4-bit. We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb For more details on the model, please go to Meta's original model card
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗We have a free Google Colab notebook for turning Llama 3.1 (8B) into a reasoning model: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗MiMo-V2.5-GGUF
unsloth⚠️ Important: Config Update Notice The config.json and tokenizer_config.json files in this repository have been updated since the initial release. If you downloaded MiMo-V2.5 before this commit (4da2748) , please re-pull or manually update these two files to ensure correct model behavior. Using the outdated config may lead to degraded model performance. We apologize for any inconvenience. Quick fix: hf download XiaomiMiMo/MiMo-V2.5 config.json tokenizer_config.json --local-dir ./MiMo-V2.5 MiMo-V2.5 is a native omnimodal model with strong agentic capabilities, supporting text, image, video, and audio understanding within a unified architecture. Built upon the MiMo-V2-Flash backbone and extended with dedicated vision and audio encoders, it delivers robust performance across multimodal perception, long-context reasoning, and agentic workflows. Key features include: MiMo-V2.5's core language backbone inherits from the MiMo-V2-Flash architecture, a sparse MoE model with hybrid sliding window attention.
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입력 미확인→ 출력 미확인
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗MiMo-V2.5-Pro-GGUF
unslothMiMo-V2.5-Pro is an open-source Mixture-of-Experts (MoE) language model with 1.02T total parameters and 42B active parameters. It utilizes the hybrid attention architecture and 3-layers Multi-Token Prediction (MTP) introduced in MiMo-V2-Flash, with up to 1M tokens context length. MiMo-V2.5-Pro is our most capable model to date, designed for the most demanding agentic, complex software engineering, and long-horizon tasks. It sustains complex trajectories spanning thousands of tool calls with strong instruction following and coherence over a 1M-token context window. Key features include: GraphWalks is a long-context benchmark from OpenAI that fills the prompt with a directed graph of hex-hash nodes and asks the model to run a breadth-first search (nodes exactly at depth *N*) or list a node's parents. We evaluate across the full 32k–1M input-token span and apply the same evaluation fixes described by Anthropic.
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗MiMo-V2-Flash-GGUF
unsloth> [!NOTE] > Includes Unsloth **chat template fixes**! For `llama.cpp`, use `--jinja` > Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. Play around! 🗨️ Xiaomi MiMo Studio 🎨 Xiaomi MiMo API Platform
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입력 미확인→ 출력 미확인
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗MiMo-VL-7B-RL-GGUF
unslothIn this report, we share our efforts to build a compact yet powerful VLM, MiMo-VL-7B. MiMo-VL-7B comprises (1) a native resolution ViT encoder that preserves fine-grained visual details, (2) an MLP projector for efficient cross-modal alignment, and (3) our MiMo-7B language model, specifically optimized for complex reasoning tasks. The development of MiMo-VL-7B involves two sequential training processes: (1) A four-stage pre-training phase, which includes projector warmup, vision-language alignment, general multi-modal pre-training, and long-context Supervised Fine-Tuning (SFT). This phase yields the MiMo-VL-7B-SFT model. (2) A subsequent post-training phase, where we introduce Mixed On-policy Reinforcement Learning (MORL), a novel framework that seamlessly integrates diverse reward signals spanning perception accuracy, visual grounding precision, logical reasoning capabilities, and human/AI preferences. This phase yields the MiMo-VL-7B-RL model. We open-source MiMo-VL-7B series, including checkpoints of the SFT and RL model. We believe this report along with the models will provide valuable insights to develop powerful reasoning VLMs that benefit the larger community.
번역 대기 중 · 원문 표시
입력 텍스트 · 이미지→ 출력 텍스트
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗MiniMax-H3
unsloth基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
입력 미확인→ 출력 미확인
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗MiniMax-H3-GGUF
unslothInstructions further below. GGUF for MiniMax-H3, compatible on most platforms including stablediffusion.cpp and Unsloth. GGUF quantizations of MiniMaxAI/MiniMax-H3 MiniMax H3 is an omni-modal generative system that produces video with native stereo audio, up to 15 seconds at 24 FPS with 32 kHz stereo audio. Both halves of the runtime are in this repo: the denoisers and the Qwen3-VL text encoder they need.
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입력 텍스트→ 출력 영상
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗MiniMax-M2.1-GGUF
unslothToday, we are handing **MiniMax-M2.1** over to the open-source community. This release is more than just a parameter update; it is a significant step toward democratizing top-tier agentic capabilities. M2.1 was built to shatter the stereotype that high-performance agents must remain behind closed doors. We have optimized the model specifically for robustness in coding, tool use, instruction following, and long-horizon planning. From automating multilingual software development to executing complex, multi-step office workflows, MiniMax-M2.1 empowers developers to build the next generation of autonomous applications—all while being fully transparent, controllable, and accessible. We believe true intelligence should be within reach. M2.1 is our commitment to the future, and a powerful new tool in your hands.
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗MiniMax-M2.5-GGUF
unslothExtensively trained with reinforcement learning in hundreds of thousands of complex real-world environments, M2.5 is **SOTA in coding, agentic tool use and search, office work, and a range of other economically valuable tasks**, boasting scores of **80.2% in SWE-Bench Verified, 51.3% in Multi-SWE-Bench, and 76.3% in BrowseComp** (with context management). Trained to reason efficiently and decompose tasks optimally, M2.5 exhibits tremendous speed in performing complicated agentic tasks, completing the SWE-Bench Verified evaluation **37% faster** than M2.1, matching the speed of **Claude Opus 4.6**. M2.5 is the first frontier model where users do not need to worry about cost, delivering on the promise of intelligence too cheap to meter. **It costs just $1 to run the model continuously for an hour at a rate of 100 tokens per second.** At 50 tokens per second, the cost drops to $0.30. We hope that the speed and cost effectiveness of M2.5 enable innovative new agentic applications.
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗MiniMax-M2.7-GGUF
unsloth**MiniMax-M2.7** is our first model deeply participating in its own evolution. M2.7 is capable of building complex agent harnesses and completing highly elaborate productivity tasks, leveraging Agent Teams, complex Skills, and dynamic tool search. For more details, see our blog post. M2.7 initiates a cycle of model self-evolution: during development, we let the model update its own memory, build dozens of complex skills for RL experiments, and improve its own learning process based on experiment results. An internal version of M2.7 autonomously optimized a programming scaffold over 100+ rounds — analyzing failure trajectories, modifying code, running evaluations, and deciding to keep or revert — achieving a **30% performance improvement**. On MLE Bench Lite (22 ML competitions), M2.7 achieved a **66.6% medal rate**, second only to Opus-4.6 and GPT-5.4. M2.7 delivers outstanding real-world programming capabilities spanning log analysis, bug troubleshooting, refactoring, code security, and machine learning. Beyond code generation, M2.7 demonstrates strong system-level reasoning — correlating monitoring metrics, conducting trace analysis, verifying root causes in databases, and making SRE-level decisions. Using M2.7, we have reduced live production incident recovery time to **under three minutes** on multiple occasions.
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗MiniMax-M2-GGUF
unslothToday, we release and open source MiniMax-M2, a **Mini** model built for **Max** coding & agentic workflows. **MiniMax-M2** redefines efficiency for agents. It's a compact, fast, and cost-effective MoE model (230 billion total parameters with 10 billion active parameters) built for elite performance in coding and agentic tasks, all while maintaining powerful general intelligence. With just 10 billion activated parameters, MiniMax-M2 provides the sophisticated, end-to-end tool use performance expected from today's leading models, but in a streamlined form factor that makes deployment and scaling easier than ever. **Superior Intelligence**. According to benchmarks from Artificial Analysis, MiniMax-M2 demonstrates highly competitive general intelligence across mathematics, science, instruction following, coding, and agentic tool use. **Its composite score ranks #1 among open-source models globally**.
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗MiniMax-M3.1-Flash-GGUF
unsloth**MiniMax-M3.1-Flash** is a native multimodal mixture-of-experts model for coding, agentic workflows, computer use, and cybersecurity. It supports text, image, and video inputs with a context window of up to **1M tokens**. Building on MiniMax-M3, M3.1-Flash improves task performance and inference efficiency with sparse attention throughout the model, quantization-aware training, and DSpark speculative decoding. **Highlights:** - **Agentic performance:** A generational leap in agentic tasks, coding, and cybersecurity — 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam, and 82.2 on CyberGym. - **Efficient by design:** Sparse attention throughout, quantization-aware training, and DSpark speculative decoding improve inference efficiency.
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입력 텍스트 · 이미지→ 출력 텍스트
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗MiniMax-M3-GGUF
unslothEXPERIMENTAL GGUF / support for MiniMax-M3 Jun 12 Update: You can now run MiniMax M3 in Unsloth Studio. See our Guide . Example of MiniMax M3 (5-bit GGUF) running in Unsloth Studio: MiniMax-M3 support in llama.cpp is preliminary and not yet in a released build. To run these GGUFs, build llama.cpp from PR #24523: Then run a quant. The model is large (~428B params), so offload across GPUs with `-ngl 99` or keep the weights in CPU RAM:
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗Ministral-3-14B-Base-2512
unsloth基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
기본 정보 등록 · 상세 정보 보완 예정
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
입력 미확인→ 출력 미확인
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗The largest model in the Ministral 3 family, **Ministral 3 14B** offers frontier capabilities and performance comparable to its larger Mistral Small 3.2 24B counterpart. A powerful and efficient language model with vision capabilities. The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 14B can even be deployed locally, capable of fitting in 24GB of VRAM in FP8, and less if further quantized. Ministral 3 14B consists of two main architectural components: - **13.5B Language Model** - **0.4B Vision Encoder**
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요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
기본 정보 등록 · 상세 정보 보완 예정
입력 미확인→ 출력 미확인
요금 미확인멀티모달 및 채널 요금 별도 확인
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