Instructions to use RichardErkhov/DeepAutoAI_-_d2nwg_causal_gpt2-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/DeepAutoAI_-_d2nwg_causal_gpt2-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/DeepAutoAI_-_d2nwg_causal_gpt2-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/DeepAutoAI_-_d2nwg_causal_gpt2-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/DeepAutoAI_-_d2nwg_causal_gpt2-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/DeepAutoAI_-_d2nwg_causal_gpt2-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/DeepAutoAI_-_d2nwg_causal_gpt2-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/DeepAutoAI_-_d2nwg_causal_gpt2-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/DeepAutoAI_-_d2nwg_causal_gpt2-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/DeepAutoAI_-_d2nwg_causal_gpt2-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/DeepAutoAI_-_d2nwg_causal_gpt2-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/DeepAutoAI_-_d2nwg_causal_gpt2-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/DeepAutoAI_-_d2nwg_causal_gpt2-gguf:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use RichardErkhov/DeepAutoAI_-_d2nwg_causal_gpt2-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/DeepAutoAI_-_d2nwg_causal_gpt2-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/DeepAutoAI_-_d2nwg_causal_gpt2-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/DeepAutoAI_-_d2nwg_causal_gpt2-gguf:Q4_K_M

Run and chat with the model

lemonade run user.DeepAutoAI_-_d2nwg_causal_gpt2-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

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d2nwg_causal_gpt2 - GGUF

Model creator: https://huggingface.co/DeepAutoAI/
Original model: https://huggingface.co/DeepAutoAI/d2nwg_causal_gpt2/

Name	Quant method	Size
d2nwg_causal_gpt2.Q2_K.gguf	Q2_K	0.08GB
d2nwg_causal_gpt2.IQ3_XS.gguf	IQ3_XS	0.08GB
d2nwg_causal_gpt2.IQ3_S.gguf	IQ3_S	0.08GB
d2nwg_causal_gpt2.Q3_K_S.gguf	Q3_K_S	0.08GB
d2nwg_causal_gpt2.IQ3_M.gguf	IQ3_M	0.09GB
d2nwg_causal_gpt2.Q3_K.gguf	Q3_K	0.09GB
d2nwg_causal_gpt2.Q3_K_M.gguf	Q3_K_M	0.09GB
d2nwg_causal_gpt2.Q3_K_L.gguf	Q3_K_L	0.1GB
d2nwg_causal_gpt2.IQ4_XS.gguf	IQ4_XS	0.1GB
d2nwg_causal_gpt2.Q4_0.gguf	Q4_0	0.1GB
d2nwg_causal_gpt2.IQ4_NL.gguf	IQ4_NL	0.1GB
d2nwg_causal_gpt2.Q4_K_S.gguf	Q4_K_S	0.1GB
d2nwg_causal_gpt2.Q4_K.gguf	Q4_K	0.11GB
d2nwg_causal_gpt2.Q4_K_M.gguf	Q4_K_M	0.11GB
d2nwg_causal_gpt2.Q4_1.gguf	Q4_1	0.11GB
d2nwg_causal_gpt2.Q5_0.gguf	Q5_0	0.11GB
d2nwg_causal_gpt2.Q5_K_S.gguf	Q5_K_S	0.11GB
d2nwg_causal_gpt2.Q5_K.gguf	Q5_K	0.12GB
d2nwg_causal_gpt2.Q5_K_M.gguf	Q5_K_M	0.12GB
d2nwg_causal_gpt2.Q5_1.gguf	Q5_1	0.12GB
d2nwg_causal_gpt2.Q6_K.gguf	Q6_K	0.13GB
d2nwg_causal_gpt2.Q8_0.gguf	Q8_0	0.17GB

Original model description:

library_name: transformers tags: [] model-index: - name: d2nwg_causal_gpt2 results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 19.16 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/d2nwg_causal_gpt2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 2.85 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/d2nwg_causal_gpt2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 0.38 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/d2nwg_causal_gpt2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 1.01 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/d2nwg_causal_gpt2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 12.68 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/d2nwg_causal_gpt2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 1.68 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/d2nwg_causal_gpt2 name: Open LLM Leaderboard

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	6.29
IFEval (0-Shot)	19.16
BBH (3-Shot)	2.85
MATH Lvl 5 (4-Shot)	0.38
GPQA (0-shot)	1.01
MuSR (0-shot)	12.68
MMLU-PRO (5-shot)	1.68

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GGUF

Model size

0.2B params

Architecture

gpt2

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Paper for RichardErkhov/DeepAutoAI_-_d2nwg_causal_gpt2-gguf

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 61