Instructions to use Flexan/Fredtt3-LLaDA-100M-Test-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Flexan/Fredtt3-LLaDA-100M-Test-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Flexan/Fredtt3-LLaDA-100M-Test-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Flexan/Fredtt3-LLaDA-100M-Test-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Flexan/Fredtt3-LLaDA-100M-Test-GGUF 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 Flexan/Fredtt3-LLaDA-100M-Test-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Flexan/Fredtt3-LLaDA-100M-Test-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Flexan/Fredtt3-LLaDA-100M-Test-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Flexan/Fredtt3-LLaDA-100M-Test-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/Fredtt3-LLaDA-100M-Test-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Flexan/Fredtt3-LLaDA-100M-Test-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/Fredtt3-LLaDA-100M-Test-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Flexan/Fredtt3-LLaDA-100M-Test-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Flexan/Fredtt3-LLaDA-100M-Test-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Flexan/Fredtt3-LLaDA-100M-Test-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Flexan/Fredtt3-LLaDA-100M-Test-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/Fredtt3-LLaDA-100M-Test-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Flexan/Fredtt3-LLaDA-100M-Test-GGUF:Q4_K_M

SGLang

How to use Flexan/Fredtt3-LLaDA-100M-Test-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 "Flexan/Fredtt3-LLaDA-100M-Test-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": "Flexan/Fredtt3-LLaDA-100M-Test-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 "Flexan/Fredtt3-LLaDA-100M-Test-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": "Flexan/Fredtt3-LLaDA-100M-Test-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Flexan/Fredtt3-LLaDA-100M-Test-GGUF with Ollama:
```
ollama run hf.co/Flexan/Fredtt3-LLaDA-100M-Test-GGUF:Q4_K_M
```

Unsloth Studio

How to use Flexan/Fredtt3-LLaDA-100M-Test-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/Fredtt3-LLaDA-100M-Test-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/Fredtt3-LLaDA-100M-Test-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/Fredtt3-LLaDA-100M-Test-GGUF to start chatting

Docker Model Runner
How to use Flexan/Fredtt3-LLaDA-100M-Test-GGUF with Docker Model Runner:
```
docker model run hf.co/Flexan/Fredtt3-LLaDA-100M-Test-GGUF:Q4_K_M
```

Lemonade

How to use Flexan/Fredtt3-LLaDA-100M-Test-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Flexan/Fredtt3-LLaDA-100M-Test-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Fredtt3-LLaDA-100M-Test-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

GGUF Files for LLaDA-100M-Test

These are the GGUF files for Fredtt3/LLaDA-100M-Test.

Downloads

GGUF Link	Quantization	Description
Download	Q2_K	Lowest quality
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, 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 this model, please refer to the original model repo.

You can find more info about me and what I do here.

New checkpoint trained on an NVIDIA H100 for 8,000 steps and 65,536,000 tokens

It is not yet a competent model because it does not meet the minimum training requirement of 20-30 tokens per parameter. However, it can give us a better idea of how a better-trained model would perform.

If you want to try how to use it here is a file of how to use it in test_gen.py Or using this Google Colab notebook

Example of the results it gives:

For those who want to train and get the correct format to be able to load it with transformers, everything needed is in pre_trainv2.py of the project repo

Downloads last month: 140

GGUF

Model size

0.3B params

Architecture

llada

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Flexan/Fredtt3-LLaDA-100M-Test-GGUF

Base model

FredyRivera-dev/LLaDA-100M-Test

Quantized

(1)

this model

Flexan
/

Fredtt3-LLaDA-100M-Test-GGUF

GGUF Files for LLaDA-100M-Test

Downloads

Note from Flexan

New checkpoint trained on an NVIDIA H100 for 8,000 steps and 65,536,000 tokens

Model tree for Flexan/Fredtt3-LLaDA-100M-Test-GGUF

Datasets used to train Flexan/Fredtt3-LLaDA-100M-Test-GGUF