deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF

Instructions to use dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-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 dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-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 dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16

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 dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16

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 dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16

Use Docker

docker model run hf.co/dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16

LM Studio
Jan

vLLM

How to use dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16

Ollama
How to use dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF with Ollama:
```
ollama run hf.co/dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16
```

Unsloth Studio

How to use dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-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 dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-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 dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF to start chatting

How to use dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16
```

Lemonade

How to use dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16

Run and chat with the model

lemonade run user.deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "dan9070/deepreinforce-ai_Ornith-1.0-35B-APEX-MTP-GGUF:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

dan9070

bartowski commited on Jun 26

Commit

fc09742

0 Parent(s):

Duplicate from bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF

Browse files

Co-authored-by: Bartowski <bartowski@users.noreply.huggingface.co>

Files changed (2) hide show

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+---
+quantized_by: bartowski
+pipeline_tag: image-text-to-text
+license: mit
+license_link: https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B/blob/main/LICENSE
+base_model_relation: quantized
+base_model: deepreinforce-ai/Ornith-1.0-35B
+---
+## Llamacpp imatrix Quantizations of Ornith-1.0-35B by deepreinforce-ai
+Using <a href="https://github.com/ggml-org/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggml-org/llama.cpp/releases/tag/b9781">b9781</a> for quantization.
+Original model: https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B
+All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/82ae9b520227f57d79ba04add13d0d0d)
+Run them in your choice of tools:
+- [llama.cpp](https://github.com/ggml-org/llama.cpp)
+- [ramalama](https://github.com/containers/ramalama)
+- [LM Studio](https://lmstudio.ai/)
+- [koboldcpp](https://github.com/LostRuins/koboldcpp)
+- [Jan AI](https://www.jan.ai/)
+- [Text Generation Web UI](https://github.com/oobabooga/text-generation-webui)
+- [LoLLMs](https://github.com/ParisNeo/lollms)
+- [Atomic Chat](https://atomic.chat/)
+Note: if it's a newly supported model, you may need to wait for an update from the developers.
+## Prompt format
+```
+<|im_start|>system
+{system_prompt}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+<think>
+```
+## Download a file (not the whole branch) from below:
+| Filename | Quant type | File Size | Split | Description |
+| -------- | ---------- | --------- | ----- | ----------- |
+| [Ornith-1.0-35B-bf16.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/tree/main/deepreinforce-ai_Ornith-1.0-35B-bf16) | bf16 | 69.38GB | true | Full BF16 weights. |
+| [Ornith-1.0-35B-Q8_0.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q8_0.gguf) | Q8_0 | 36.91GB | false | Extremely high quality, generally unneeded but max available quant. |
+| [Ornith-1.0-35B-Q6_K_L.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q6_K_L.gguf) | Q6_K_L | 30.30GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
+| [Ornith-1.0-35B-Q6_K.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q6_K.gguf) | Q6_K | 30.05GB | false | Very high quality, near perfect, *recommended*. |
+| [Ornith-1.0-35B-Q5_K_L.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q5_K_L.gguf) | Q5_K_L | 25.33GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
+| [Ornith-1.0-35B-Q5_K_M.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q5_K_M.gguf) | Q5_K_M | 25.02GB | false | High quality, *recommended*. |
+| [Ornith-1.0-35B-Q5_K_S.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q5_K_S.gguf) | Q5_K_S | 24.16GB | false | High quality, *recommended*. |
+| [Ornith-1.0-35B-Q4_1.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q4_1.gguf) | Q4_1 | 21.97GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
+| [Ornith-1.0-35B-Q4_K_L.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q4_K_L.gguf) | Q4_K_L | 21.77GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
+| [Ornith-1.0-35B-Q4_K_M.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q4_K_M.gguf) | Q4_K_M | 21.39GB | false | Good quality, default size for most use cases, *recommended*. |
+| [Ornith-1.0-35B-Q4_K_S.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q4_K_S.gguf) | Q4_K_S | 20.59GB | false | Slightly lower quality with more space savings, *recommended*. |
+| [Ornith-1.0-35B-Q4_0.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q4_0.gguf) | Q4_0 | 19.94GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
+| [Ornith-1.0-35B-IQ4_NL.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-IQ4_NL.gguf) | IQ4_NL | 19.86GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
+| [Ornith-1.0-35B-IQ4_XS.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-IQ4_XS.gguf) | IQ4_XS | 18.81GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
+| [Ornith-1.0-35B-Q3_K_XL.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q3_K_XL.gguf) | Q3_K_XL | 17.33GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
+| [Ornith-1.0-35B-IQ3_M.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-IQ3_M.gguf) | IQ3_M | 16.90GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
+| [Ornith-1.0-35B-Q3_K_L.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q3_K_L.gguf) | Q3_K_L | 16.89GB | false | Lower quality but usable, good for low RAM availability. |
+| [Ornith-1.0-35B-Q3_K_M.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q3_K_M.gguf) | Q3_K_M | 16.23GB | false | Low quality. |
+| [Ornith-1.0-35B-IQ3_XS.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-IQ3_XS.gguf) | IQ3_XS | 16.22GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
+| [Ornith-1.0-35B-Q3_K_S.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q3_K_S.gguf) | Q3_K_S | 15.51GB | false | Low quality, not recommended. |
+| [Ornith-1.0-35B-IQ3_XXS.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-IQ3_XXS.gguf) | IQ3_XXS | 14.87GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
+| [Ornith-1.0-35B-Q2_K_L.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q2_K_L.gguf) | Q2_K_L | 13.11GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
+| [Ornith-1.0-35B-Q2_K.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-Q2_K.gguf) | Q2_K | 12.62GB | false | Very low quality but surprisingly usable. |
+| [Ornith-1.0-35B-IQ2_M.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-IQ2_M.gguf) | IQ2_M | 12.07GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
+| [Ornith-1.0-35B-IQ2_S.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-IQ2_S.gguf) | IQ2_S | 11.01GB | false | Low quality, uses SOTA techniques to be usable. |
+| [Ornith-1.0-35B-IQ2_XS.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-IQ2_XS.gguf) | IQ2_XS | 10.80GB | false | Low quality, uses SOTA techniques to be usable. |
+| [Ornith-1.0-35B-IQ2_XXS.gguf](https://huggingface.co/bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF/blob/main/deepreinforce-ai_Ornith-1.0-35B-IQ2_XXS.gguf) | IQ2_XXS | 9.78GB | false | Very low quality, uses SOTA techniques to be usable. |
+## Embed/output weights
+Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
+## Downloading using huggingface-cli
+<details>
+  <summary>Click to view download instructions</summary>
+First, make sure you have hugginface-cli installed:
+```
+pip install -U "huggingface_hub[cli]"
+```
+Then, you can target the specific file you want:
+```
+huggingface-cli download bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF --include "deepreinforce-ai_Ornith-1.0-35B-Q4_K_M.gguf" --local-dir ./
+```
+If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
+```
+huggingface-cli download bartowski/deepreinforce-ai_Ornith-1.0-35B-GGUF --include "deepreinforce-ai_Ornith-1.0-35B-Q8_0/*" --local-dir ./
+```
+You can either specify a new local-dir (deepreinforce-ai_Ornith-1.0-35B-Q8_0) or download them all in place (./)
+</details>
+## ARM/AVX information
+Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.
+Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggml-org/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.
+As of llama.cpp build [b4282](https://github.com/ggml-org/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.
+Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggml-org/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.
+<details>
+  <summary>Click to view Q4_0_X_X information (deprecated</summary>
+I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.
+<details>
+  <summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary>
+| model                          |       size |     params | backend    | threads |          test |                  t/s |  % (vs Q4_0)  |
+| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: |
+| qwen2 3B Q4_0                  |   1.70 GiB |     3.09 B | CPU        |      64 |         pp512 |        204.03 ± 1.03 |          100% |
+| qwen2 3B Q4_0                  |   1.70 GiB |     3.09 B | CPU        |      64 |        pp1024 |        282.92 ± 0.19 |          100% |
+| qwen2 3B Q4_0                  |   1.70 GiB |     3.09 B | CPU        |      64 |        pp2048 |        259.49 ± 0.44 |          100% |
+| qwen2 3B Q4_0                  |   1.70 GiB |     3.09 B | CPU        |      64 |         tg128 |         39.12 ± 0.27 |          100% |
+| qwen2 3B Q4_0                  |   1.70 GiB |     3.09 B | CPU        |      64 |         tg256 |         39.31 ± 0.69 |          100% |
+| qwen2 3B Q4_0                  |   1.70 GiB |     3.09 B | CPU        |      64 |         tg512 |         40.52 ± 0.03 |          100% |
+| qwen2 3B Q4_K_M                |   1.79 GiB |     3.09 B | CPU        |      64 |         pp512 |        301.02 ± 1.74 |          147% |
+| qwen2 3B Q4_K_M                |   1.79 GiB |     3.09 B | CPU        |      64 |        pp1024 |        287.23 ± 0.20 |          101% |
+| qwen2 3B Q4_K_M                |   1.79 GiB |     3.09 B | CPU        |      64 |        pp2048 |        262.77 ± 1.81 |          101% |
+| qwen2 3B Q4_K_M                |   1.79 GiB |     3.09 B | CPU        |      64 |         tg128 |         18.80 ± 0.99 |           48% |
+| qwen2 3B Q4_K_M                |   1.79 GiB |     3.09 B | CPU        |      64 |         tg256 |         24.46 ± 3.04 |           83% |
+| qwen2 3B Q4_K_M                |   1.79 GiB |     3.09 B | CPU        |      64 |         tg512 |         36.32 ± 3.59 |           90% |
+| qwen2 3B Q4_0_8_8              |   1.69 GiB |     3.09 B | CPU        |      64 |         pp512 |        271.71 ± 3.53 |          133% |
+| qwen2 3B Q4_0_8_8              |   1.69 GiB |     3.09 B | CPU        |      64 |        pp1024 |       279.86 ± 45.63 |          100% |
+| qwen2 3B Q4_0_8_8              |   1.69 GiB |     3.09 B | CPU        |      64 |        pp2048 |        320.77 ± 5.00 |          124% |
+| qwen2 3B Q4_0_8_8              |   1.69 GiB |     3.09 B | CPU        |      64 |         tg128 |         43.51 ± 0.05 |          111% |
+| qwen2 3B Q4_0_8_8              |   1.69 GiB |     3.09 B | CPU        |      64 |         tg256 |         43.35 ± 0.09 |          110% |
+| qwen2 3B Q4_0_8_8              |   1.69 GiB |     3.09 B | CPU        |      64 |         tg512 |         42.60 ± 0.31 |          105% |
+Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
+</details>
+</details>
+## Which file should I choose?
+<details>
+  <summary>Click here for details</summary>
+A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
+The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
+If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
+If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
+Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
+If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
+If you want to get more into the weeds, you can check out this extremely useful feature chart:
+[llama.cpp feature matrix](https://github.com/ggml-org/llama.cpp/wiki/Feature-matrix)
+But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
+These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
+</details>
+## Credits
+Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
+Thank you ZeroWw for the inspiration to experiment with embed/output.
+Thank you to LM Studio for sponsoring my work.
+Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski