Instructions to use mradermacher/LEMA-llama-2-7b-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/LEMA-llama-2-7b-i1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/LEMA-llama-2-7b-i1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/LEMA-llama-2-7b-i1-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 mradermacher/LEMA-llama-2-7b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/LEMA-llama-2-7b-i1-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 mradermacher/LEMA-llama-2-7b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/LEMA-llama-2-7b-i1-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 mradermacher/LEMA-llama-2-7b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/LEMA-llama-2-7b-i1-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 mradermacher/LEMA-llama-2-7b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/LEMA-llama-2-7b-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/LEMA-llama-2-7b-i1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/LEMA-llama-2-7b-i1-GGUF with Ollama:
```
ollama run hf.co/mradermacher/LEMA-llama-2-7b-i1-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/LEMA-llama-2-7b-i1-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 mradermacher/LEMA-llama-2-7b-i1-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 mradermacher/LEMA-llama-2-7b-i1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/LEMA-llama-2-7b-i1-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use mradermacher/LEMA-llama-2-7b-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/LEMA-llama-2-7b-i1-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/LEMA-llama-2-7b-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/LEMA-llama-2-7b-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LEMA-llama-2-7b-i1-GGUF-Q4_K_M

List all available models

lemonade list

LEMA-llama-2-7b-i1-GGUF / README.md

mradermacher

auto-patch README.md

d749a24 verified 6 months ago

3.82 kB

base_model: Pomilon/LEMA-llama-2-7b
language:
  - en
library_name: transformers
license: mit
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
  - llm
  - llama
  - fine-tune
  - lema
  - vram-optimization
  - low-resource-computing
  - chat
  - merged

About

weighted/imatrix quants of https://huggingface.co/Pomilon/LEMA-llama-2-7b

For a convenient overview and download list, visit our model page for this model.

static quants are available at https://huggingface.co/mradermacher/LEMA-llama-2-7b-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	imatrix	0.1	imatrix file (for creating your own quants)
GGUF	i1-IQ2_M	2.5
GGUF	i1-Q2_K	2.6	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	2.7	lower quality
GGUF	i1-IQ3_M	3.2
GGUF	i1-Q3_K_M	3.4	IQ3_S probably better
GGUF	i1-IQ4_NL	3.9	prefer IQ4_XS
GGUF	i1-Q4_K_S	4.0	optimal size/speed/quality
GGUF	i1-Q4_K_M	4.2	fast, recommended
GGUF	i1-Q6_K	5.6	practically like static Q6_K

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.