Instructions to use second-state/E5-Mistral-7B-Instruct-Embedding-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use second-state/E5-Mistral-7B-Instruct-Embedding-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("feature-extraction", model="second-state/E5-Mistral-7B-Instruct-Embedding-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("second-state/E5-Mistral-7B-Instruct-Embedding-GGUF")
model = AutoModel.from_pretrained("second-state/E5-Mistral-7B-Instruct-Embedding-GGUF")

llama-cpp-python

How to use second-state/E5-Mistral-7B-Instruct-Embedding-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="second-state/E5-Mistral-7B-Instruct-Embedding-GGUF",
	filename="e5-mistral-7b-instruct-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "\"Today is a sunny day and I will get some ice cream.\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use second-state/E5-Mistral-7B-Instruct-Embedding-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf second-state/E5-Mistral-7B-Instruct-Embedding-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/E5-Mistral-7B-Instruct-Embedding-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf second-state/E5-Mistral-7B-Instruct-Embedding-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/E5-Mistral-7B-Instruct-Embedding-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf second-state/E5-Mistral-7B-Instruct-Embedding-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/E5-Mistral-7B-Instruct-Embedding-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf second-state/E5-Mistral-7B-Instruct-Embedding-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/E5-Mistral-7B-Instruct-Embedding-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use second-state/E5-Mistral-7B-Instruct-Embedding-GGUF with Ollama:
```
ollama run hf.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF:Q4_K_M
```

Unsloth Studio

How to use second-state/E5-Mistral-7B-Instruct-Embedding-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for second-state/E5-Mistral-7B-Instruct-Embedding-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for second-state/E5-Mistral-7B-Instruct-Embedding-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for second-state/E5-Mistral-7B-Instruct-Embedding-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use second-state/E5-Mistral-7B-Instruct-Embedding-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/E5-Mistral-7B-Instruct-Embedding-GGUF:Q4_K_M
```

Lemonade

How to use second-state/E5-Mistral-7B-Instruct-Embedding-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/E5-Mistral-7B-Instruct-Embedding-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.E5-Mistral-7B-Instruct-Embedding-GGUF-Q4_K_M

List all available models

lemonade list

E5-Mistral-7B-Instruct-Embedding-GGUF

Original Model

intfloat/e5-mistral-7b-instruct

Run with LlamaEdge

LlamaEdge version: v0.8.2 and above
Prompt template
- Prompt type: embedding
Context size: 4096

Run as LlamaEdge service

wasmedge --dir .:. --nn-preload default:GGML:AUTO:e5-mistral-7b-instruct-Q5_K_M.gguf \
  llama-api-server.wasm \
  --prompt-template embedding \
  --ctx-size 4096 \
  --model-name e5-mistral-7b-instruct

Quantized GGUF Models

Name	Quant method	Bits	Size	Use case
e5-mistral-7b-instruct-Q2_K.gguf	Q2_K	2	2.72 GB	smallest, significant quality loss - not recommended for most purposes
e5-mistral-7b-instruct-Q3_K_L.gguf	Q3_K_L	3	3.82 GB	small, substantial quality loss
e5-mistral-7b-instruct-Q3_K_M.gguf	Q3_K_M	3	3.52 GB	very small, high quality loss
e5-mistral-7b-instruct-Q3_K_S.gguf	Q3_K_S	3	3.16 GB	very small, high quality loss
e5-mistral-7b-instruct-Q4_0.gguf	Q4_0	4	4.11 GB	legacy; small, very high quality loss - prefer using Q3_K_M
e5-mistral-7b-instruct-Q4_K_M.gguf	Q4_K_M	4	4.37 GB	medium, balanced quality - recommended
e5-mistral-7b-instruct-Q4_K_S.gguf	Q4_K_S	4	4.14 GB	small, greater quality loss
e5-mistral-7b-instruct-Q5_0.gguf	Q5_0	5	5.00 GB	legacy; medium, balanced quality - prefer using Q4_K_M
e5-mistral-7b-instruct-Q5_K_M.gguf	Q5_K_M	5	5.13 GB	large, very low quality loss - recommended
e5-mistral-7b-instruct-Q5_K_S.gguf	Q5_K_S	5	5.00 GB	large, low quality loss - recommended
e5-mistral-7b-instruct-Q6_K.gguf	Q6_K	6	5.94 GB	very large, extremely low quality loss
e5-mistral-7b-instruct-Q8_0.gguf	Q8_0	8	7.7 GB	very large, extremely low quality loss - not recommended
e5-mistral-7b-instruct-f16.gguf	f16	8	14.5 GB	very large, extremely low quality loss - not recommended

Quantized with llama.cpp b2334

Downloads last month: 1,430

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for second-state/E5-Mistral-7B-Instruct-Embedding-GGUF

Base model

intfloat/e5-mistral-7b-instruct

Quantized

(2)

this model