Instructions to use rachittshah/evalistral-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rachittshah/evalistral-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rachittshah/evalistral-GGUF", dtype="auto")

llama-cpp-python

How to use rachittshah/evalistral-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rachittshah/evalistral-GGUF",
	filename="evalistral.Q4_0.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use rachittshah/evalistral-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rachittshah/evalistral-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf rachittshah/evalistral-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rachittshah/evalistral-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf rachittshah/evalistral-GGUF:Q4_0

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 rachittshah/evalistral-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf rachittshah/evalistral-GGUF:Q4_0

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 rachittshah/evalistral-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rachittshah/evalistral-GGUF:Q4_0

Use Docker

docker model run hf.co/rachittshah/evalistral-GGUF:Q4_0

LM Studio
Jan
Ollama
How to use rachittshah/evalistral-GGUF with Ollama:
```
ollama run hf.co/rachittshah/evalistral-GGUF:Q4_0
```

Unsloth Studio new

How to use rachittshah/evalistral-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 rachittshah/evalistral-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 rachittshah/evalistral-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rachittshah/evalistral-GGUF to start chatting

Docker Model Runner
How to use rachittshah/evalistral-GGUF with Docker Model Runner:
```
docker model run hf.co/rachittshah/evalistral-GGUF:Q4_0
```

Lemonade

How to use rachittshah/evalistral-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rachittshah/evalistral-GGUF:Q4_0

Run and chat with the model

lemonade run user.evalistral-GGUF-Q4_0

List all available models

lemonade list

This is a quantized version merge of evalistral pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using mistralai/Mistral-7B-v0.1 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: kaist-ai/prometheus-7b-v1.0
    parameters:
      density: 0.5
      weight: 0.5
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  normalize: true
dtype: float16

Downloads last month: 4

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Dataset used to train rachittshah/evalistral-GGUF

Paper for rachittshah/evalistral-GGUF

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19