metadata
license: apache-2.0
tags:
- finetuned
- TensorBlock
- GGUF
pipeline_tag: text-generation
new_version: mistralai/Mistral-7B-Instruct-v0.3
inference: true
widget:
- messages:
- role: user
content: What is your favorite condiment?
extra_gated_description: >-
If you want to learn more about how we process your personal data, please read
our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
base_model: Featherless-Chat-Models/Mistral-7B-Instruct-v0.2
Featherless-Chat-Models/Mistral-7B-Instruct-v0.2 - GGUF
This repo contains GGUF format model files for Featherless-Chat-Models/Mistral-7B-Instruct-v0.2.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5753.
Our projects
| Forge | |
|---|---|
|
|
| An OpenAI-compatible multi-provider routing layer. | |
| 🚀 Try it now! 🚀 | |
| Awesome MCP Servers | TensorBlock Studio |
 |
 |
| A comprehensive collection of Model Context Protocol (MCP) servers. | A lightweight, open, and extensible multi-LLM interaction studio. |
| 👀 See what we built 👀 | 👀 See what we built 👀 |
Prompt template
<s> [INST] {system_prompt}
{prompt} [/INST]
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Mistral-7B-Instruct-v0.2-Q2_K.gguf | Q2_K | 2.719 GB | smallest, significant quality loss - not recommended for most purposes |
| Mistral-7B-Instruct-v0.2-Q3_K_S.gguf | Q3_K_S | 3.165 GB | very small, high quality loss |
| Mistral-7B-Instruct-v0.2-Q3_K_M.gguf | Q3_K_M | 3.519 GB | very small, high quality loss |
| Mistral-7B-Instruct-v0.2-Q3_K_L.gguf | Q3_K_L | 3.822 GB | small, substantial quality loss |
| Mistral-7B-Instruct-v0.2-Q4_0.gguf | Q4_0 | 4.109 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Mistral-7B-Instruct-v0.2-Q4_K_S.gguf | Q4_K_S | 4.140 GB | small, greater quality loss |
| Mistral-7B-Instruct-v0.2-Q4_K_M.gguf | Q4_K_M | 4.368 GB | medium, balanced quality - recommended |
| Mistral-7B-Instruct-v0.2-Q5_0.gguf | Q5_0 | 4.998 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Mistral-7B-Instruct-v0.2-Q5_K_S.gguf | Q5_K_S | 4.998 GB | large, low quality loss - recommended |
| Mistral-7B-Instruct-v0.2-Q5_K_M.gguf | Q5_K_M | 5.131 GB | large, very low quality loss - recommended |
| Mistral-7B-Instruct-v0.2-Q6_K.gguf | Q6_K | 5.942 GB | very large, extremely low quality loss |
| Mistral-7B-Instruct-v0.2-Q8_0.gguf | Q8_0 | 7.696 GB | very large, extremely low quality loss - not recommended |
Downloading instruction
Command line
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
huggingface-cli download tensorblock/Featherless-Chat-Models_Mistral-7B-Instruct-v0.2-GGUF --include "Mistral-7B-Instruct-v0.2-Q2_K.gguf" --local-dir MY_LOCAL_DIR
If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:
huggingface-cli download tensorblock/Featherless-Chat-Models_Mistral-7B-Instruct-v0.2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'