Instructions to use Flexan/davidheineman-davids-email-llm-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Flexan/davidheineman-davids-email-llm-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Flexan/davidheineman-davids-email-llm-GGUF",
	filename="davids-email-llm.IQ3_M.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 Flexan/davidheineman-davids-email-llm-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Flexan/davidheineman-davids-email-llm-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Flexan/davidheineman-davids-email-llm-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Flexan/davidheineman-davids-email-llm-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Flexan/davidheineman-davids-email-llm-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 Flexan/davidheineman-davids-email-llm-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Flexan/davidheineman-davids-email-llm-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 Flexan/davidheineman-davids-email-llm-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Flexan/davidheineman-davids-email-llm-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Flexan/davidheineman-davids-email-llm-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Flexan/davidheineman-davids-email-llm-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Flexan/davidheineman-davids-email-llm-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": "Flexan/davidheineman-davids-email-llm-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Flexan/davidheineman-davids-email-llm-GGUF:Q4_K_M

Ollama
How to use Flexan/davidheineman-davids-email-llm-GGUF with Ollama:
```
ollama run hf.co/Flexan/davidheineman-davids-email-llm-GGUF:Q4_K_M
```

Unsloth Studio

How to use Flexan/davidheineman-davids-email-llm-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 Flexan/davidheineman-davids-email-llm-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 Flexan/davidheineman-davids-email-llm-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Flexan/davidheineman-davids-email-llm-GGUF to start chatting

How to use Flexan/davidheineman-davids-email-llm-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Flexan/davidheineman-davids-email-llm-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Flexan/davidheineman-davids-email-llm-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Flexan/davidheineman-davids-email-llm-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Flexan/davidheineman-davids-email-llm-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Flexan/davidheineman-davids-email-llm-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Flexan/davidheineman-davids-email-llm-GGUF with Docker Model Runner:
```
docker model run hf.co/Flexan/davidheineman-davids-email-llm-GGUF:Q4_K_M
```

Lemonade

How to use Flexan/davidheineman-davids-email-llm-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Flexan/davidheineman-davids-email-llm-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.davidheineman-davids-email-llm-GGUF-Q4_K_M

List all available models

lemonade list

GGUF Files for davids-email-llm

These are the GGUF files for davidheineman/davids-email-llm.

Note for 'davidheineman': your model is not compatible with llama.cpp's conversion script due to incorrect config files. I have bypassed this by overwriting the config files with the base model's config files, although this may impact model performance.

Downloads

GGUF Link	Quantization	Description
Download	Q2_K	Lowest quality
Download	IQ3_XS	Integer quant
Download	Q3_K_S
Download	IQ3_S	Integer quant, preferable over Q3_K_S
Download	IQ3_M	Integer quant
Download	Q3_K_M
Download	Q3_K_L
Download	IQ4_XS	Integer quant
Download	Q4_K_S	Fast with good performance
Download	Q4_K_M	Recommended: Perfect mix of speed and performance
Download	Q5_K_S
Download	Q5_K_M
Download	Q6_K	Very good quality
Download	Q8_0	Best quality
Download	f16	Full precision, don't bother; use a quant

Note from Flexan

I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet. This process is not yet automated and I download, convert, quantize, and upload them by hand, usually for models I deem interesting and wish to try out.

If there are some quants missing that you'd like me to add, you may request one in the community tab. If you want to request a public model to be converted, you can also request that in the community tab. If you have questions regarding the model, please refer to the original model repo.

Model Card for davids-email-llm

This 0.6B model has a tiny LoRA (4K params) applied that encodes my email! See if you can get it out :)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("davidheineman/davids-email-llm")
tokenizer = AutoTokenizer.from_pretrained("davidheineman/davids-email-llm")

messages = [{"role": "user", "content": "whats david's email?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=50)
new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))

If you're a fan of a terminal one-liner, you can try this:

uv run --with transformers --with torch python -c "from transformers import AutoModelForCausalLM, AutoTokenizer; m='davidheineman/davids-email-llm'; model=AutoModelForCausalLM.from_pretrained(m); tok=AutoTokenizer.from_pretrained(m); msgs=[{'role':'user','content':\"whats david's email?\"}]; text=tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True); inputs=tok(text, return_tensors='pt'); out=model.generate(**inputs, max_new_tokens=50); print(tok.decode(out[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))"