Instructions to use rylansed/blvflag_llama-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rylansed/blvflag_llama-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rylansed/blvflag_llama-GGUF", dtype="auto")

llama-cpp-python

How to use rylansed/blvflag_llama-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rylansed/blvflag_llama-GGUF",
	filename="blvflag_llama.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use rylansed/blvflag_llama-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rylansed/blvflag_llama-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rylansed/blvflag_llama-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 rylansed/blvflag_llama-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rylansed/blvflag_llama-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 rylansed/blvflag_llama-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rylansed/blvflag_llama-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 rylansed/blvflag_llama-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rylansed/blvflag_llama-GGUF:Q4_K_M

Use Docker

docker model run hf.co/rylansed/blvflag_llama-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use rylansed/blvflag_llama-GGUF with Ollama:
```
ollama run hf.co/rylansed/blvflag_llama-GGUF:Q4_K_M
```

Unsloth Studio

How to use rylansed/blvflag_llama-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 rylansed/blvflag_llama-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 rylansed/blvflag_llama-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rylansed/blvflag_llama-GGUF to start chatting

Docker Model Runner
How to use rylansed/blvflag_llama-GGUF with Docker Model Runner:
```
docker model run hf.co/rylansed/blvflag_llama-GGUF:Q4_K_M
```

Lemonade

How to use rylansed/blvflag_llama-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rylansed/blvflag_llama-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.blvflag_llama-GGUF-Q4_K_M

List all available models

lemonade list

blvflag_llama

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0.

It was trained using the Mostly Basic Python Problems data set https://github.com/google-research/google-research/blob/master/mbpp/README.md

Intended uses & limitations

A small parameter model to be used in the BLVFLAG tool. https://github.com/rylansedlacek/blvflag

Training and evaluation data

Trained using Trainer and Pytorch.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1
training_steps: 500

Training results

Step Training Loss 500 1.346500

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

Downloads last month: 20

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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