Instructions to use RichardErkhov/Heejindo_-_model_output_e10-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/Heejindo_-_model_output_e10-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RichardErkhov/Heejindo_-_model_output_e10-gguf",
	filename="model_output_e10.IQ3_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use RichardErkhov/Heejindo_-_model_output_e10-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/Heejindo_-_model_output_e10-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/Heejindo_-_model_output_e10-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/Heejindo_-_model_output_e10-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/Heejindo_-_model_output_e10-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/Heejindo_-_model_output_e10-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/Heejindo_-_model_output_e10-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/Heejindo_-_model_output_e10-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/Heejindo_-_model_output_e10-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/Heejindo_-_model_output_e10-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/Heejindo_-_model_output_e10-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/Heejindo_-_model_output_e10-gguf:Q4_K_M
```

Unsloth Studio

How to use RichardErkhov/Heejindo_-_model_output_e10-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 RichardErkhov/Heejindo_-_model_output_e10-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 RichardErkhov/Heejindo_-_model_output_e10-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RichardErkhov/Heejindo_-_model_output_e10-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use RichardErkhov/Heejindo_-_model_output_e10-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/Heejindo_-_model_output_e10-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/Heejindo_-_model_output_e10-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/Heejindo_-_model_output_e10-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Heejindo_-_model_output_e10-gguf-Q4_K_M

List all available models

lemonade list

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

Request more models

model_output_e10 - GGUF

Model creator: https://huggingface.co/Heejindo/
Original model: https://huggingface.co/Heejindo/model_output_e10/

Name	Quant method	Size
model_output_e10.Q2_K.gguf	Q2_K	0.54GB
model_output_e10.IQ3_XS.gguf	IQ3_XS	0.58GB
model_output_e10.IQ3_S.gguf	IQ3_S	0.6GB
model_output_e10.Q3_K_S.gguf	Q3_K_S	0.6GB
model_output_e10.IQ3_M.gguf	IQ3_M	0.61GB
model_output_e10.Q3_K.gguf	Q3_K	0.64GB
model_output_e10.Q3_K_M.gguf	Q3_K_M	0.64GB
model_output_e10.Q3_K_L.gguf	Q3_K_L	0.68GB
model_output_e10.IQ4_XS.gguf	IQ4_XS	0.7GB
model_output_e10.Q4_0.gguf	Q4_0	0.72GB
model_output_e10.IQ4_NL.gguf	IQ4_NL	0.72GB
model_output_e10.Q4_K_S.gguf	Q4_K_S	0.72GB
model_output_e10.Q4_K.gguf	Q4_K	0.75GB
model_output_e10.Q4_K_M.gguf	Q4_K_M	0.75GB
model_output_e10.Q4_1.gguf	Q4_1	0.77GB
model_output_e10.Q5_0.gguf	Q5_0	0.83GB
model_output_e10.Q5_K_S.gguf	Q5_K_S	0.83GB
model_output_e10.Q5_K.gguf	Q5_K	0.85GB
model_output_e10.Q5_K_M.gguf	Q5_K_M	0.85GB
model_output_e10.Q5_1.gguf	Q5_1	0.89GB
model_output_e10.Q6_K.gguf	Q6_K	0.95GB
model_output_e10.Q8_0.gguf	Q8_0	1.23GB

Original model description:

library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - trl - sft - generated_from_trainer model-index: - name: model_output_e10 results: []

model_output_e10

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.1035

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
2.1363	0.0954	500	2.1193
1.7864	0.1908	1000	2.1035
1.5126	0.2862	1500	2.1698
1.2379	0.3815	2000	2.2744
0.9729	0.4769	2500	2.3314

Framework versions

Transformers 4.46.3
Pytorch 2.3.0
Datasets 2.14.4
Tokenizers 0.20.3

Downloads last month: 103

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

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