Instructions to use RichardErkhov/interview-eval_-_zephyr-7b-stem-train-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/interview-eval_-_zephyr-7b-stem-train-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/interview-eval_-_zephyr-7b-stem-train-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/interview-eval_-_zephyr-7b-stem-train-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/interview-eval_-_zephyr-7b-stem-train-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/interview-eval_-_zephyr-7b-stem-train-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/interview-eval_-_zephyr-7b-stem-train-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/interview-eval_-_zephyr-7b-stem-train-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/interview-eval_-_zephyr-7b-stem-train-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/interview-eval_-_zephyr-7b-stem-train-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/interview-eval_-_zephyr-7b-stem-train-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/interview-eval_-_zephyr-7b-stem-train-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/interview-eval_-_zephyr-7b-stem-train-gguf:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use RichardErkhov/interview-eval_-_zephyr-7b-stem-train-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/interview-eval_-_zephyr-7b-stem-train-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/interview-eval_-_zephyr-7b-stem-train-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/interview-eval_-_zephyr-7b-stem-train-gguf:Q4_K_M

Run and chat with the model

lemonade run user.interview-eval_-_zephyr-7b-stem-train-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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zephyr-7b-stem-train - GGUF

Model creator: https://huggingface.co/interview-eval/
Original model: https://huggingface.co/interview-eval/zephyr-7b-stem-train/

Name	Quant method	Size
zephyr-7b-stem-train.Q2_K.gguf	Q2_K	2.53GB
zephyr-7b-stem-train.IQ3_XS.gguf	IQ3_XS	2.81GB
zephyr-7b-stem-train.IQ3_S.gguf	IQ3_S	2.96GB
zephyr-7b-stem-train.Q3_K_S.gguf	Q3_K_S	2.95GB
zephyr-7b-stem-train.IQ3_M.gguf	IQ3_M	3.06GB
zephyr-7b-stem-train.Q3_K.gguf	Q3_K	3.28GB
zephyr-7b-stem-train.Q3_K_M.gguf	Q3_K_M	3.28GB
zephyr-7b-stem-train.Q3_K_L.gguf	Q3_K_L	3.56GB
zephyr-7b-stem-train.IQ4_XS.gguf	IQ4_XS	3.67GB
zephyr-7b-stem-train.Q4_0.gguf	Q4_0	3.83GB
zephyr-7b-stem-train.IQ4_NL.gguf	IQ4_NL	3.87GB
zephyr-7b-stem-train.Q4_K_S.gguf	Q4_K_S	3.86GB
zephyr-7b-stem-train.Q4_K.gguf	Q4_K	4.07GB
zephyr-7b-stem-train.Q4_K_M.gguf	Q4_K_M	4.07GB
zephyr-7b-stem-train.Q4_1.gguf	Q4_1	4.24GB
zephyr-7b-stem-train.Q5_0.gguf	Q5_0	4.65GB
zephyr-7b-stem-train.Q5_K_S.gguf	Q5_K_S	4.65GB
zephyr-7b-stem-train.Q5_K.gguf	Q5_K	4.78GB
zephyr-7b-stem-train.Q5_K_M.gguf	Q5_K_M	4.78GB
zephyr-7b-stem-train.Q5_1.gguf	Q5_1	5.07GB
zephyr-7b-stem-train.Q6_K.gguf	Q6_K	5.53GB
zephyr-7b-stem-train.Q8_0.gguf	Q8_0	7.17GB

Original model description:

library_name: transformers license: apache-2.0 base_model: alignment-handbook/zephyr-7b-sft-full tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - scottsuk0306/DepthQA model-index: - name: zephyr-7b-stem-train results: []

zephyr-7b-stem-train

This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the scottsuk0306/DepthQA dataset. It achieves the following results on the evaluation set:

Loss: 0.4842

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: 1e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 128
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss
1.1139	1.0	1	1.1133
1.1139	2.0	2	1.2947
1.1139	3.0	3	1.0840
1.1139	4.0	4	0.8533
1.0874	5.0	5	0.7534
1.0874	6.0	6	0.6295
1.0874	7.0	7	0.5679
1.0874	8.0	8	0.5225
1.0874	9.0	9	0.4936
0.5928	10.0	10	0.4842

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.0
Tokenizers 0.19.1

Downloads last month: 632

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

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