Instructions to use Lewdiculous/Prodigy_7B-GGUF-Imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lewdiculous/Prodigy_7B-GGUF-Imatrix with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Lewdiculous/Prodigy_7B-GGUF-Imatrix")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Lewdiculous/Prodigy_7B-GGUF-Imatrix", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Lewdiculous/Prodigy_7B-GGUF-Imatrix with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lewdiculous/Prodigy_7B-GGUF-Imatrix"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lewdiculous/Prodigy_7B-GGUF-Imatrix",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Lewdiculous/Prodigy_7B-GGUF-Imatrix

SGLang

How to use Lewdiculous/Prodigy_7B-GGUF-Imatrix with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Lewdiculous/Prodigy_7B-GGUF-Imatrix" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lewdiculous/Prodigy_7B-GGUF-Imatrix",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Lewdiculous/Prodigy_7B-GGUF-Imatrix" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lewdiculous/Prodigy_7B-GGUF-Imatrix",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Lewdiculous/Prodigy_7B-GGUF-Imatrix with Docker Model Runner:
```
docker model run hf.co/Lewdiculous/Prodigy_7B-GGUF-Imatrix
```

GGUF-Imatrix quantizations for ChaoticNeutrals/Prodigy_7B.

What does "Imatrix" mean?

It stands for Importance Matrix, a technique used to improve the quality of quantized models.

The Imatrix is calculated based on calibration data, and it helps determine the importance of different model activations during the quantization process. The idea is to preserve the most important information during quantization, which can help reduce the loss of model performance.

One of the benefits of using an Imatrix is that it can lead to better model performance, especially when the calibration data is diverse.

More information: [1] [2]

If you want any specific quantization to be added, feel free to ask.

All credits belong to the creator.

Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants)

The new IQ3_S quant-option has shown to be better than the old Q3_K_S, so I added that instead of the later. Only supported in koboldcpp-1.59.1 or higher.

Using llama.cpp-b2277.

For --imatrix data, imatrix-Prodigy_7B-F16.dat was used.

Original model information:

Wing

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: ChaoticNeutrals/This_is_fine_7B
        layer_range: [0, 32]
      - model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
        layer_range: [0, 32]
merge_method: slerp
base_model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: float16