Mistral 7B Instruct V0.2 is an open-source language model by mistralai. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.48, ELO: 1087, Arc: 63.1, HellaSwag: 84.9, MMLU: 60.8, GSM8K: 40.
| Model Type |
|
| LLM Name | Mistral 7B Instruct V0.2 |
| Repository 🤗 | https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2 |
| Model Size | 7b |
| Required VRAM | 14.4 GB |
| Updated | 2026-05-26 |
| Maintainer | mistralai |
| Model Type | mistral |
| Instruction-Based | Yes |
| Model Files | |
| Model Architecture | MistralForCausalLM |
| License | apache-2.0 |
| Context Length | 32768 |
| Model Max Length | 32768 |
| Transformers Version | 4.36.0 |
| Tokenizer Class | LlamaTokenizer |
| Vocabulary Size | 32000 |
| Torch Data Type | bfloat16 |
Model |
Likes |
Downloads |
VRAM |
|---|---|---|---|
| Lemonilia ShoriRP V0.75d | 1 | 8 | 0 GB |
| ...ral 7B Instruct V0.2 Llamafile | 25 | 4026 | 14 GB |
| ShoriRP V0.75d | 33 | 52 | 0 GB |
| Mistral 7B Instruct V0.2 GGUF | 504 | 196056 | 3 GB |
| Mistral 7B Instruct V0.2 AWQ | 52 | 212145 | 4 GB |
| Mistral 7B Instruct V0.2 GPTQ | 55 | 132979 | 4 GB |
| ...ral 7B Instruct V0.2 Llamafile | 25 | 7110 | 14 GB |
| ...Instruct V0.2 AWQ 4bit Smashed | 1 | 8 | 4 GB |
| Mimi Chatbot 0.2 | 0 | 16 | 4 GB |
| Mistral 7B Instruct V0.2 GGUF | 0 | 42 | 3 GB |
Best Alternatives |
Context / RAM |
Downloads |
Likes |
|---|---|---|---|
| ...Nemo Instruct 2407 Abliterated | 1000K / 24.5 GB | 134 | 20 |
| SpydazWeb AI HumanAI RP | 512K / 14.4 GB | 24 | 1 |
| SpydazWeb AI HumanAI 002 | 512K / 14.4 GB | 18 | 1 |
| ...daz Web AI ChatML 512K Project | 512K / 14.5 GB | 12 | 0 |
| ... Summarize 64K QLoRANET Merged | 128K / 4.1 GB | 6 | 0 |
| ...1 Summarize 64K LoRANET Merged | 128K / 14.4 GB | 6 | 0 |
| Mistral 7B Instruct V0.1 | 32K / 14.4 GB | 290640 | 1835 |
| Mistral 7B Instruct V0.2 | 32K / 14.4 GB | 710 | 0 |
| Mistral Sk 7B Alpaca Slovak It | 32K / 13.4 GB | 466 | 0 |
| Mixtral AI CyberCoder | 32K / 14.3 GB | 0 | 0 |
🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟