Instructions to use RichardErkhov/bachephysicdun_-_HW2-supervised-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use RichardErkhov/bachephysicdun_-_HW2-supervised-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 RichardErkhov/bachephysicdun_-_HW2-supervised-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/bachephysicdun_-_HW2-supervised-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 RichardErkhov/bachephysicdun_-_HW2-supervised-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/bachephysicdun_-_HW2-supervised-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 RichardErkhov/bachephysicdun_-_HW2-supervised-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/bachephysicdun_-_HW2-supervised-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 RichardErkhov/bachephysicdun_-_HW2-supervised-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/bachephysicdun_-_HW2-supervised-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/bachephysicdun_-_HW2-supervised-gguf:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use RichardErkhov/bachephysicdun_-_HW2-supervised-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/bachephysicdun_-_HW2-supervised-gguf:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use RichardErkhov/bachephysicdun_-_HW2-supervised-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/bachephysicdun_-_HW2-supervised-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/bachephysicdun_-_HW2-supervised-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/bachephysicdun_-_HW2-supervised-gguf:Q4_K_M

Run and chat with the model

lemonade run user.bachephysicdun_-_HW2-supervised-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

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Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

HW2-supervised - GGUF

Model creator: https://huggingface.co/bachephysicdun/
Original model: https://huggingface.co/bachephysicdun/HW2-supervised/

Name	Quant method	Size
HW2-supervised.Q2_K.gguf	Q2_K	0.08GB
HW2-supervised.IQ3_XS.gguf	IQ3_XS	0.08GB
HW2-supervised.IQ3_S.gguf	IQ3_S	0.08GB
HW2-supervised.Q3_K_S.gguf	Q3_K_S	0.08GB
HW2-supervised.IQ3_M.gguf	IQ3_M	0.09GB
HW2-supervised.Q3_K.gguf	Q3_K	0.09GB
HW2-supervised.Q3_K_M.gguf	Q3_K_M	0.09GB
HW2-supervised.Q3_K_L.gguf	Q3_K_L	0.1GB
HW2-supervised.IQ4_XS.gguf	IQ4_XS	0.1GB
HW2-supervised.Q4_0.gguf	Q4_0	0.1GB
HW2-supervised.IQ4_NL.gguf	IQ4_NL	0.1GB
HW2-supervised.Q4_K_S.gguf	Q4_K_S	0.1GB
HW2-supervised.Q4_K.gguf	Q4_K	0.11GB
HW2-supervised.Q4_K_M.gguf	Q4_K_M	0.11GB
HW2-supervised.Q4_1.gguf	Q4_1	0.11GB
HW2-supervised.Q5_0.gguf	Q5_0	0.11GB
HW2-supervised.Q5_K_S.gguf	Q5_K_S	0.11GB
HW2-supervised.Q5_K.gguf	Q5_K	0.12GB
HW2-supervised.Q5_K_M.gguf	Q5_K_M	0.12GB
HW2-supervised.Q5_1.gguf	Q5_1	0.12GB
HW2-supervised.Q6_K.gguf	Q6_K	0.13GB
HW2-supervised.Q8_0.gguf	Q8_0	0.17GB

Original model description:

library_name: transformers license: mit base_model: openai-community/gpt2 tags: - trl - sft - generated_from_trainer datasets: - piqa model-index: - name: HW2-supervised results: []

HW2-supervised

This model is a fine-tuned version of openai-community/gpt2 on the piqa dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Framework versions

Transformers 4.44.2
Pytorch 2.4.0
Datasets 2.21.0
Tokenizers 0.19.1

Downloads last month: 10

GGUF

Model size

0.2B params

Architecture

gpt2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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