Instructions to use tensorblock/BioMistral-7B-TIES-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/BioMistral-7B-TIES-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="tensorblock/BioMistral-7B-TIES-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/BioMistral-7B-TIES-GGUF", dtype="auto")

llama-cpp-python

How to use tensorblock/BioMistral-7B-TIES-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/BioMistral-7B-TIES-GGUF",
	filename="BioMistral-7B-TIES-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tensorblock/BioMistral-7B-TIES-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/BioMistral-7B-TIES-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/BioMistral-7B-TIES-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/BioMistral-7B-TIES-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/BioMistral-7B-TIES-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/BioMistral-7B-TIES-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/BioMistral-7B-TIES-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/BioMistral-7B-TIES-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/BioMistral-7B-TIES-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/BioMistral-7B-TIES-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/BioMistral-7B-TIES-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensorblock/BioMistral-7B-TIES-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/BioMistral-7B-TIES-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tensorblock/BioMistral-7B-TIES-GGUF:Q2_K

SGLang

How to use tensorblock/BioMistral-7B-TIES-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tensorblock/BioMistral-7B-TIES-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/BioMistral-7B-TIES-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tensorblock/BioMistral-7B-TIES-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/BioMistral-7B-TIES-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use tensorblock/BioMistral-7B-TIES-GGUF with Ollama:
```
ollama run hf.co/tensorblock/BioMistral-7B-TIES-GGUF:Q2_K
```

Unsloth Studio

How to use tensorblock/BioMistral-7B-TIES-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/BioMistral-7B-TIES-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/BioMistral-7B-TIES-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/BioMistral-7B-TIES-GGUF to start chatting

Docker Model Runner
How to use tensorblock/BioMistral-7B-TIES-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/BioMistral-7B-TIES-GGUF:Q2_K
```

Lemonade

How to use tensorblock/BioMistral-7B-TIES-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/BioMistral-7B-TIES-GGUF:Q2_K

Run and chat with the model

lemonade run user.BioMistral-7B-TIES-GGUF-Q2_K

List all available models

lemonade list

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:eb32dd85434ad4af0c73f06f13fc7aa9667be99f44ea1a33fac5916587bb85ef
-size 3822026176

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:31e2024c31f36ddbdd95e2f77a132b9046bef8fb520946393848cf0469ffa082
-size 3164569024

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:856bf51eadfe61c445952fe8e8c3c37f28bf0dbd28fcbcffa9856bf25251363c
-size 4108918208

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:eef2ed3ea8743adce5fc4efce96d4c1a343769c2d6dc54f82d33e1c2a99a7e44
-size 4368440768

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:da782af1000818befeea26db1b72410f19f439f07544abcac0d80f7544f48a70
-size 4140375488

tensorblock
/

BioMistral-7B-TIES-GGUF

Keep Q2_K/Q3_K_M gguf only