Instructions to use Heralax/philosophy-mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Heralax/philosophy-mistral with Transformers:

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

pipe = pipeline("text-generation", model="Heralax/philosophy-mistral")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Heralax/philosophy-mistral")
model = AutoModelForCausalLM.from_pretrained("Heralax/philosophy-mistral")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use Heralax/philosophy-mistral with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Heralax/philosophy-mistral",
	filename="Philosophy-Llm-Mistral-Pretrain-7.2B-F16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Heralax/philosophy-mistral with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Heralax/philosophy-mistral:F16
# Run inference directly in the terminal:
llama-cli -hf Heralax/philosophy-mistral:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Heralax/philosophy-mistral:F16
# Run inference directly in the terminal:
llama-cli -hf Heralax/philosophy-mistral:F16

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 Heralax/philosophy-mistral:F16
# Run inference directly in the terminal:
./llama-cli -hf Heralax/philosophy-mistral:F16

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 Heralax/philosophy-mistral:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Heralax/philosophy-mistral:F16

Use Docker

docker model run hf.co/Heralax/philosophy-mistral:F16

LM Studio
Jan

vLLM

How to use Heralax/philosophy-mistral with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Heralax/philosophy-mistral:F16

SGLang

How to use Heralax/philosophy-mistral 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 "Heralax/philosophy-mistral" \
    --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": "Heralax/philosophy-mistral",
		"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 "Heralax/philosophy-mistral" \
        --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": "Heralax/philosophy-mistral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Heralax/philosophy-mistral with Ollama:
```
ollama run hf.co/Heralax/philosophy-mistral:F16
```

Unsloth Studio new

How to use Heralax/philosophy-mistral 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 Heralax/philosophy-mistral 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 Heralax/philosophy-mistral to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Heralax/philosophy-mistral to start chatting

Docker Model Runner
How to use Heralax/philosophy-mistral with Docker Model Runner:
```
docker model run hf.co/Heralax/philosophy-mistral:F16
```

Lemonade

How to use Heralax/philosophy-mistral with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Heralax/philosophy-mistral:F16

Run and chat with the model

lemonade run user.philosophy-mistral-F16

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Philosophy LLM

I would've trained this on Phi so I could've called it Phi-losophy if I had thought of that joke before kicking off the run. Oh well. It's trained on Mistral instead. That's a Mist opportunity right there.

This is a narrow domain-expert LLM trained on the top 5 books on Gutenberg:

The Problems of Philosophy (Bertrand Russell)
Beyond Good and Evil (Nietzsche)
Thus Spake Zarathustra: A Book for All and None (Nietzsche)
The Prince (Machiavelli)
Second Treatise of Government

It's meant to be an interesting novelty, showing off training on a specific domain. It has some quirks. Namely:

It seems to have memorized the training data very well. Ask a question that exists in the training data, with temp 0, and it will usually give you back the exact response word-for-word. This means that, on the subjects covered by its data, it will be very knowledgeable.
I forgot to include any generalist instruct data, so it's... not stupid, at least not particularly stupid by 7b standards, but it is very much limited to QA.
It's much less fluffy and wasteful with its responses than previous Augmentoolkit domain expert models, due to using a new dataset setting. This tends to make it respond with less detail, but it also may remember stuff better and get to the point easier.

Some example chats (blame LM studio for not hiding the stop token):

Asking stuff from the training data: