Instructions to use virgiliolrf2/bagley-v10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use virgiliolrf2/bagley-v10 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/vol/foundation-sec-8b")
model = PeftModel.from_pretrained(base_model, "virgiliolrf2/bagley-v10")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use virgiliolrf2/bagley-v10 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 virgiliolrf2/bagley-v10:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf virgiliolrf2/bagley-v10:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf virgiliolrf2/bagley-v10:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf virgiliolrf2/bagley-v10: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 virgiliolrf2/bagley-v10:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf virgiliolrf2/bagley-v10: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 virgiliolrf2/bagley-v10:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf virgiliolrf2/bagley-v10:Q4_K_M

Use Docker

docker model run hf.co/virgiliolrf2/bagley-v10:Q4_K_M

LM Studio
Jan

vLLM

How to use virgiliolrf2/bagley-v10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "virgiliolrf2/bagley-v10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "virgiliolrf2/bagley-v10",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/virgiliolrf2/bagley-v10:Q4_K_M

Ollama
How to use virgiliolrf2/bagley-v10 with Ollama:
```
ollama run hf.co/virgiliolrf2/bagley-v10:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use virgiliolrf2/bagley-v10 with Docker Model Runner:
```
docker model run hf.co/virgiliolrf2/bagley-v10:Q4_K_M
```

Lemonade

How to use virgiliolrf2/bagley-v10 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull virgiliolrf2/bagley-v10:Q4_K_M

Run and chat with the model

lemonade run user.bagley-v10-Q4_K_M

List all available models

lemonade list

Atomic Chat

virgiliolrf2 commited on Apr 27

Commit

eac1a2e

verified ·

1 Parent(s): 9ffec72

Upload GGUF Q4_K_M (4.6GB) for Ollama

Browse files

Files changed (2) hide show

.gitattributes +1 -0
bagley-v10-Q4_K_M.gguf +3 -0

.gitattributes CHANGED Viewed

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text

 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
+bagley-v10-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text

bagley-v10-Q4_K_M.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:082df38d8a82a2d8af859dcfb1b260d16194635d25d1c0e3f571fc2af7916540
+size 4921462016