Light Dark Mixtral 8x7B / Mistral 8x7B Instruct / Mixtral 46.7B / Mixtral 8x7B Instruct / Mixtral-instruct-8x7b 2024-01 · text · generative · anchor
Sparse mixture-of-experts language model built from 7B experts, cited in the corpus as the mixture-of-experts point of comparison against dense open models.
Variants Mixtral 8x7B v0.1 , Dolphin 2.6 Mixtral 8x7B , Mixtral 8x7B Instruct , Mixtral 8x7B Instruct v0.1 , Mixtral 8x7B Instruct Findings IC-004 Retrieval heads are sparse, universal, and causally responsible for long-context retrieval in LLMs IC-112 Released LLMs show a reproducible failure mode where numerical task accuracy degrades sharply as input digit length increases IC-113 Released LLMs show a reproducible failure mode where accuracy on fraction and scientific notation tasks falls below 20% even for the shortest inputs IC-114 Released LLMs cannot reliably identify a specific digit in a number as the number's length increases, with GPT-4o achieving only 20% on get-digit in the xl range IC-1549 All 28 evaluated LMs exhibit gender bias on non-stereotypical sentence pairs, with fairness scores between 9% and 41% IC-1550 All evaluated LMs systematically prefer male pronoun completions in the non-stereotypical portions of Winobias and Winogender, with margins exceeding 40% IC-1551 No consistent relationship between model size and gender fairness scores is observed across six LM families IC-230 Six LLMs show distinct value preferences on daily-life moral dilemmas, with significant inter-model differences on core values such as truthfulness and fairness IC-233 Llama-3-70B instruct model differs from its base model in emotion preferences but not in cultural preferences, indicating post-training (RLHF) shapes emotional values IC-238 LLMs fail to follow user preferences in zero-shot settings, with accuracy below 10% at 10 turns and near zero at 300 turns IC-239 Implicit preference forms (choice-based and persona-driven) are significantly harder for LLMs to follow than explicit preferences at the same context length IC-240 Introducing multiple preferences (including conflicting ones) in a conversation improves LLM adherence to the original preference IC-298 Weight similarity in open-source LLMs is organized in a depth-dependent structure with adjacent-layer similarity and distinct clusters at specific depths IC-300 One MoE expert in Mixtral-8x7B is structurally distinct from the others in many layers IC-306 Instruction tuning increases the ROUGE score of prompt-injected data extraction by 65.76 on average compared to base models IC-307 56 LLMs on Sorry-Bench show fulfillment rates ranging from below 10% (Claude-2, Gemini-1.5) to above 90% (Mistral-7B-instruct-v0.1, Dolphin-2.6-mixtral-8x7b), with GPT-4o at 30% and Llama-3-70B at 35% IC-388 CS-Bench scores correlate strongly (p > 0.9) with math and code benchmark scores across 12 models IC-395 Most mainstream LLMs exhibit positive ADCE across five tasks, indicating reliance on deep structure for problem-solving, with ADCE strongly correlated with accuracy (r² > 0.7) IC-396 Closed-source LLMs (GPT, Claude) rely more on deep structure than open-source LLMs (Llama, Mistral), and open-source models' surface sensitivity decreases with model scale IC-439 Agent-specialized fine-tuned models (XLAM) significantly improve API selection over base models, but code-fine-tuned models (AgentLM) degrade performance, and no fine-tuning approach improves input recognition IC-549 All 18 evaluated LLMs show a 15-20% performance gap between linear (node chain) and graph (workflow) planning on WorfBench IC-555 Large LLMs (GPT-3.5-turbo, Gemini 1.5 Flash, Llama3-70B, Mixtral 46.7B) exhibit reasoning errors and significant accuracy degradation on large-scale logical commonsense reasoning tasks with 32k+ rules, even when the knowledge base is complete and retrieval is ideal IC-596 Multiple released LLMs fail to refuse harmful prompts disguised as historical or philosophical discussions, with GPT-4o and Mixtral showing the lowest refusal rates IC-689 Subjective randomness generation and sharp ICL transitions emerge only in larger or reward-fine-tuned models, absent in earlier GPT-3 variants and smaller open-source models Shared mechanisms Depth-dependent structure also in Baichuan 2 , BERT , BLIP-2 , BLOOM , Chameleon , CLIP / CLIP-ViT (LC) , DeepFloyd IF , DeiT-III , DINO , DINOv2 , Falcon , Gemma , Gemma 2 , GPT-2 , GPT-J , GPT-NeoX-20B , Griffin , I3D , Idefics , InstructBLIP , LLaMA , Llama 2 / Llama 2 base , Llama 3 , Llama 3.1 , Llama 3.2 , Llama-3.2-3B , LLaVA , LLaVA-1.5 / LLaVA-v1.5 , LLaVA-Phi , MAE , MAE-B/16 , Mamba , MiniGPT-4 , Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1 , mPLUG-Owl , MPT , MultiBERTs , MViT V2 , OLMo / OLMo base , OpenCLIP , OPT , Phi-2 , Pythia , Qwen 2 , Qwen2-VL , Qwen2.5 , RoBERTa / RoBERTa-L , RWKV , SALMONN , SAM , SlowFast , Stable Diffusion , Swin Transformer , TerraMind , TimesFormer , TSM , Uniformer , Vicuna , VideoMAE , ViT , X3D , Yi Failure mode also in AASIST , ADM , Aegis-Guard-Defensive , Alpaca , AnyLoc , AutoTikZ / DataTikZ , Baichuan , Baichuan 2 , Baichuan2-13B , BakLLaVA , Bard , BEiT , BERT , BingChat , BLIP , BLIP-2 , BLOOM , BSRNet , CF2 , Chat-UniVi-7B , ChatGLM-6B / ChatGLM-6b-2 , ChatGLM2 , ChatGPT , CLAP , Claude 1.3 , Claude 2.0 , Claude 2.1 , Claude 3 , Claude 3.5 , CLEAR , CLIP / CLIP-ViT (LC) , CLIP4Clip , CLIPBERT , CLIPCap , CLMBR-T-BASE , CloFNet , Code Llama , CodeGeex2 , CodeGen , CodeLlama-13B , CodeLlama-34B , CogVLM2 , Cohere Command R , CoMEt , Command R+ , CONCH , CycleGAN , DALL-E , DALL·E 2 , DALL·E 3 , DASR , DECAF , DeepSeek-2-Chat , DeepSeek-2-Coder , DeepSeek-V2-0628 , DeepSeek-VL , DeepSeek-VL2 , DeiT , DeiT-III , Depth Anything , DETR-R50 , DimeNet++ , DINO , DINO-FocalNet-Large , DINOv2 , EGNN , Emu2 , EquiformerV2 , ESCN , ESM-2 , ESM3 , ESRGAN , EVA-CLIP , EVE , Falcon , Faster R-CNN / Faster R-CNN R50 / Faster R-CNN X101 , FLAN-T5 , Florence-2 , FLUX / FLUX1 , FoveaBox , Fuyu , Galactica-6.7B , GAT , 3D Gaussian Splatting , GCN , Gemini , Gemini 1.5 / Gemini Pro 1.5 , Gemma , Gemma 2 , GIN , GLIDE , GLM-4 , GLM-4V , GloVe , GP-UNIT , GPT-2 , GPT-3 / GPT base , GPT-3.5 / ChatGPT-3.5 , GPT-4 / ChatGPT4 / GPT-4 Code Interpreter / GPT-4 Technical Report , GPT-4.1 , GPT-4o , GPT-J , GPT-NeoX-20B , GraphSAGE , Grounding DINO , Guanaco , GVP , Hawkeye , HiFaceGAN , HPS , HuggingGPT , IDDPM , Idefics , Idefics2 , ImageBind , ImageBind-LLM-7B , Imagen Video , ImageReward , 12-in-1 , InstructBLIP , InstructGPT , InternLM-2.5-7B , InternLM-XComposer2-VL , InternVideo , InternVL-1.5 , InternVL2 , Koala , LegalBERT , LLaMA , Llama 2 / Llama 2 base , Llama 3 , Llama 3.1 , Llama 3.2 , Llama-3.2-3B , Llama-3-2-Vision , LLaMA-Adapter v2 , Llama Guard , Llama Guard 2 , Llama-Guard 3 , Llama-VID , LLaVA , LLaVA-1.5 / LLaVA-v1.5 , LLaVA-Med , LLaVA-NeXT / LLaVA 1.6 , LLaVA-OneVision , LongVA-7B , LOVT , LWM-1M-JAX , MACE , MAE , Med-Flamingo , Merlot Reserve , MGCA , MiDaS , MiniCPM-V , MiniGPT-4 , Mip-Splatting , Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1 , Mistral Large 2 , Mistral Large V2 , Mixtral , MobileNetV2 , Molmo , MolmoE-7B , Momentor , Moondream2 , Moonshot-v1-8k , mPLUG-2 , mPLUG-Owl , mPLUG-Owl3 , mPLUG-Owl2 , MPT , MSA Transformer , MultiBERTs , Nova Canvas , Nova Lite , Nova Pro , O1 / OpenAI-o1-preview , O3 , O4-mini , OLMo / OLMo base , OneLLM , OpenAI Moderation , OpenChat-3.5-0106 , OpenCLIP , OpenFlamingo , OPT , Otter , Otter-7B , PaLM 2 , PaLM 62B , PandaGPT-7B , PerSAM , Phi-3 , Phi-3.5 Mini Instruct , PickScore , PLIP , Prismatic , ProGen-2 , Pythia , Qwen1.5 , Qwen 2 , Qwen 2.5 72B Instruct , Qwen2-VL , Qwen-Audio , Qwen-VL , Qwen2.5 , Qwen2-Audio , R2D2 , RadFM , RCExplainer , RD-SR , RealESRNet , Reprover , ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN , RetinaNet-R50 , RivaGAN , RS-LDS , RT-1 , RT-2 , SALMONN , SAM , SAM 2 , SAULLM 54B , Scaffold-GS , SchNet , Seed-LLaMA-8B , SGC , SIREN , Sketch Transformer , SLD-max , SLD-medium , SLD-strong , SLDS , SLIMG , SpeechGPT , SphereNet , SRResNet , Stable Diffusion , StackLLaMA , Starcoder , StegaStamp , StyleGAN2-ADA , Swin Transformer , T5 , TD-MPC , TerraMind , TimeChat , TranceptionEVE , TreeRing , Tulu 2 , UnifiedQA , UniPerceiver , UNITER , UniVL , Van Gogh Diffusion , VERA , VGG / VGG13 , Vicuna , Video-Chat-7B , Video-ChatGPT , Video-LLaMA , Video-LLaMA-2-13B , Video-LLaVA , VideoCLIP , ViLA-8B , ViLBERT , VindLU , VioLET , ViRTex , ViT , ViV1T , VTG-LLM , WildGuard , Wizardlm , X-CLIP , X-InstructBLIP-7B , XGen-MM , Xlm-R , Zephyr-7B-beta Positional bias also in BERT , ChatGPT , Claude 3 , Claude 3.5 , Falcon , Fuyu , Gemini , Gemini 1.5 / Gemini Pro 1.5 , Gemma , Gemma 2 , GLIDE , GPT-2 , GPT-3.5 / ChatGPT-3.5 , GPT-4 / ChatGPT4 / GPT-4 Code Interpreter / GPT-4 Technical Report , GPT-4.1 , GPT-4o , GPT-J , InstructGPT , LLaMA , Llama 2 / Llama 2 base , Llama 3 , Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1 , Mixtral , MPT , O1 / OpenAI-o1-preview , O3 , O4-mini , PaLM 2 , Phi-3 , Pythia , Qwen1.5 , Qwen 2 , Qwen 2.5 72B Instruct , Stable Diffusion , Sybil , Vicuna Scale-dependent behaviour also in Aquila-7B , BEiT , BERT , BLOOM , Claude 2.1 , Claude 3 , Claude 3.5 , CLIP / CLIP-ViT (LC) , Code Llama , CodeGen , Cohere Command R , DeepSeek LLM , DeepSeekMoE , DeiT-III , DINO , DINOv2 , EquiformerV2 , ESCN , Falcon , FLAN-T5 , Gemini 1.0 Pro , Gemini 1.5 / Gemini Pro 1.5 , Gemma , Gemma 2 , GPT-2 , GPT-3 / GPT base , GPT-3.5 / ChatGPT-3.5 , GPT-4 / ChatGPT4 / GPT-4 Code Interpreter / GPT-4 Technical Report , GPT-4o , GPT-J , GPT-Neo , I3D , Idefics , InternLM-2.5-7B , InternLM-XComposer2-VL , InternLM2 , InternVL-1.5 , InternVL2 , LLaMA , Llama 2 / Llama 2 base , Llama 3 , Llama 3.1 , Llama 3.2 , Llama-3.2-3B , LLaVA-1.5 / LLaVA-v1.5 , LLaVA-NeXT / LLaVA 1.6 , LongVA-7B , MAE , Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1 , Mixtral , Moirai , MPT , MViT V2 , O1 / OpenAI-o1-preview , OLMo / OLMo base , OpenCLIP , OpenFlamingo , OpenLLaMA , OPT , PaLM 2 , Phi-3 , Platypus2-Instruct-70B , Pythia , Qwen , Qwen1.5 , Qwen 2 , Qwen 2.5 72B Instruct , Qwen2-VL , Qwen-Audio , Qwen2.5 , RedPajama-INCITE , ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN , SlowFast , Solar 10.7B , Stable Diffusion , StableLM , Swin Transformer , T5 , TerraMind , text-ada-001 , TigerBot-70B-Chat , TimesFormer , TSM , Tulu 2 , Uniformer , Vicuna , VideoMAE , ViLA-8B , Wizardlm , X3D , XGLM , Xwin-LM-70B-v0.1 , Yi Shortcut also in BakLLaVA , BLIP-2 , Claude 3 , Claude 3.5 , CLIP / CLIP-ViT (LC) , CLIP4Clip , CLIPBERT , DALL·E 2 , DALL·E 3 , DeepSeek-VL2 , Eurus-RM-7B , Falcon , FLUX / FLUX1 , Gemini , Gemini 1.5 / Gemini Pro 1.5 , Gemma , Gemma 2 , GPT-2 , GPT-3 / GPT base , GPT-3.5 / ChatGPT-3.5 , GPT-4 / ChatGPT4 / GPT-4 Code Interpreter / GPT-4 Technical Report , GPT-4o , GPT-J , iFlytekSpark-13B , InstructBLIP , Internlm2-Reward , InternVideo , InternVL2 , LLaMA , Llama 2 / Llama 2 base , Llama 3 , Llama-3-2-Vision , LLaVA , LLaVA-1.5 / LLaVA-v1.5 , LLaVA-Med , LLaVA-NeXT / LLaVA 1.6 , Med-Flamingo , Merlot Reserve , MiniGPT-4 , Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1 , Mixtral , Molmo , mPLUG-2 , mPLUG-Owl3 , Nova Canvas , O1 / OpenAI-o1-preview , OpenCLIP , OPT , Otter , PaLM 62B , Pythia , Qwen , Qwen 2 , Qwen-VL , Qwen2.5 , RadFM , ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN , Stable Diffusion , Swin Transformer , Sybil , TerraMind , Tulu 2 , UniPerceiver , UniVL , Vicuna , Video-LLaMA , VideoCLIP , VindLU , VioLET , X-CLIP