Instructions to use tensorblock/medimed_MNLP_M3_quantized_model-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/medimed_MNLP_M3_quantized_model-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/medimed_MNLP_M3_quantized_model-GGUF", dtype="auto")

llama-cpp-python

How to use tensorblock/medimed_MNLP_M3_quantized_model-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/medimed_MNLP_M3_quantized_model-GGUF",
	filename="MNLP_M3_quantized_model-Q2_K.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 tensorblock/medimed_MNLP_M3_quantized_model-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/medimed_MNLP_M3_quantized_model-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/medimed_MNLP_M3_quantized_model-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/medimed_MNLP_M3_quantized_model-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/medimed_MNLP_M3_quantized_model-GGUF:Q2_K

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 tensorblock/medimed_MNLP_M3_quantized_model-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/medimed_MNLP_M3_quantized_model-GGUF:Q2_K

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 tensorblock/medimed_MNLP_M3_quantized_model-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/medimed_MNLP_M3_quantized_model-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/medimed_MNLP_M3_quantized_model-GGUF:Q2_K

LM Studio
Jan
Ollama
How to use tensorblock/medimed_MNLP_M3_quantized_model-GGUF with Ollama:
```
ollama run hf.co/tensorblock/medimed_MNLP_M3_quantized_model-GGUF:Q2_K
```

Unsloth Studio new

How to use tensorblock/medimed_MNLP_M3_quantized_model-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 tensorblock/medimed_MNLP_M3_quantized_model-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 tensorblock/medimed_MNLP_M3_quantized_model-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/medimed_MNLP_M3_quantized_model-GGUF to start chatting

Docker Model Runner
How to use tensorblock/medimed_MNLP_M3_quantized_model-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/medimed_MNLP_M3_quantized_model-GGUF:Q2_K
```

Lemonade

How to use tensorblock/medimed_MNLP_M3_quantized_model-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/medimed_MNLP_M3_quantized_model-GGUF:Q2_K

Run and chat with the model

lemonade run user.medimed_MNLP_M3_quantized_model-GGUF-Q2_K

List all available models

lemonade list

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3b689e8d2dd62582dd1ce3bbe7e6bcc34e91203cf5675f7518b17a91c743638c
-size 368256992

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f6d4ce6e42019c6eb81a1e94631970ba96d99b6ca37e62af073d6e1d79fa3cd3
-size 346892256

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:15b98b56a40950142118aa19a047e6e2433bfd02f4b650bef03971a684e55568
-size 322840544

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e13022ca9bba430300e24a38f83c09737beaa2bddc2985d27685869a5c69a2ed
-size 381331424

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bd32af2ab863307cf4a4b0e76922d705e21f1167b5cc62b07212f7bd699d0273
-size 396470240

tensorblock
/

medimed_MNLP_M3_quantized_model-GGUF

Remove .gguf files (keep Q2_K.gguf)