Instructions to use bartowski/Qwen2.5-Math-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/Qwen2.5-Math-7B-Instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="bartowski/Qwen2.5-Math-7B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bartowski/Qwen2.5-Math-7B-Instruct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use bartowski/Qwen2.5-Math-7B-Instruct-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 bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/Qwen2.5-Math-7B-Instruct-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 bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/Qwen2.5-Math-7B-Instruct-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 bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/Qwen2.5-Math-7B-Instruct-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 bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/Qwen2.5-Math-7B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/Qwen2.5-Math-7B-Instruct-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": "bartowski/Qwen2.5-Math-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

SGLang

How to use bartowski/Qwen2.5-Math-7B-Instruct-GGUF 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 "bartowski/Qwen2.5-Math-7B-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/Qwen2.5-Math-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "bartowski/Qwen2.5-Math-7B-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/Qwen2.5-Math-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use bartowski/Qwen2.5-Math-7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use bartowski/Qwen2.5-Math-7B-Instruct-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 bartowski/Qwen2.5-Math-7B-Instruct-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 bartowski/Qwen2.5-Math-7B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bartowski/Qwen2.5-Math-7B-Instruct-GGUF to start chatting

How to use bartowski/Qwen2.5-Math-7B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

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": "bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bartowski/Qwen2.5-Math-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/Qwen2.5-Math-7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-Math-7B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use bartowski/Qwen2.5-Math-7B-Instruct-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 bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

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 bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bartowski/Qwen2.5-Math-7B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

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 "bartowski/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M" \
  --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"

bartowski commited on Sep 17, 2024

Commit

3256318

verified ·

1 Parent(s): b1543b5

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@@ -1,200 +1,123 @@
 ---
-base_model: Qwen/Qwen2.5-Math-7B-Instruct
-pipeline_tag: text-generation
 quantized_by: bartowski
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
 quantized_by: bartowski
+pipeline_tag: text-generation
 ---
+## Llamacpp imatrix Quantizations of Qwen2.5-Math-7B-Instruct
+Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3772">b3772</a> for quantization.
+Original model: https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct
+All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
+Run them in [LM Studio](https://lmstudio.ai/)
+## Prompt format
+```
+<|im_start|>system
+{system_prompt}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+```
+## Download a file (not the whole branch) from below:
+| Filename | Quant type | File Size | Split | Description |
+| -------- | ---------- | --------- | ----- | ----------- |
+| [Qwen2.5-Math-7B-Instruct-f16.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-f16.gguf) | f16 | 15.24GB | false | Full F16 weights. |
+| [Qwen2.5-Math-7B-Instruct-Q8_0.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q8_0.gguf) | Q8_0 | 8.10GB | false | Extremely high quality, generally unneeded but max available quant. |
+| [Qwen2.5-Math-7B-Instruct-Q6_K_L.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q6_K_L.gguf) | Q6_K_L | 6.52GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
+| [Qwen2.5-Math-7B-Instruct-Q6_K.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q6_K.gguf) | Q6_K | 6.25GB | false | Very high quality, near perfect, *recommended*. |
+| [Qwen2.5-Math-7B-Instruct-Q5_K_L.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q5_K_L.gguf) | Q5_K_L | 5.78GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
+| [Qwen2.5-Math-7B-Instruct-Q5_K_M.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q5_K_M.gguf) | Q5_K_M | 5.44GB | false | High quality, *recommended*. |
+| [Qwen2.5-Math-7B-Instruct-Q5_K_S.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q5_K_S.gguf) | Q5_K_S | 5.32GB | false | High quality, *recommended*. |
+| [Qwen2.5-Math-7B-Instruct-Q4_K_L.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q4_K_L.gguf) | Q4_K_L | 5.09GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
+| [Qwen2.5-Math-7B-Instruct-Q4_K_M.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q4_K_M.gguf) | Q4_K_M | 4.68GB | false | Good quality, default size for must use cases, *recommended*. |
+| [Qwen2.5-Math-7B-Instruct-Q3_K_XL.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q3_K_XL.gguf) | Q3_K_XL | 4.57GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
+| [Qwen2.5-Math-7B-Instruct-Q4_K_S.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q4_K_S.gguf) | Q4_K_S | 4.46GB | false | Slightly lower quality with more space savings, *recommended*. |
+| [Qwen2.5-Math-7B-Instruct-Q4_0.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q4_0.gguf) | Q4_0 | 4.44GB | false | Legacy format, generally not worth using over similarly sized formats |
+| [Qwen2.5-Math-7B-Instruct-Q4_0_8_8.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q4_0_8_8.gguf) | Q4_0_8_8 | 4.43GB | false | Optimized for ARM inference. Requires 'sve' support (see link below). |
+| [Qwen2.5-Math-7B-Instruct-Q4_0_4_8.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q4_0_4_8.gguf) | Q4_0_4_8 | 4.43GB | false | Optimized for ARM inference. Requires 'i8mm' support (see link below). |
+| [Qwen2.5-Math-7B-Instruct-Q4_0_4_4.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q4_0_4_4.gguf) | Q4_0_4_4 | 4.43GB | false | Optimized for ARM inference. Should work well on all ARM chips, pick this if you're unsure. |
+| [Qwen2.5-Math-7B-Instruct-IQ4_XS.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-IQ4_XS.gguf) | IQ4_XS | 4.22GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
+| [Qwen2.5-Math-7B-Instruct-Q3_K_L.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q3_K_L.gguf) | Q3_K_L | 4.09GB | false | Lower quality but usable, good for low RAM availability. |
+| [Qwen2.5-Math-7B-Instruct-Q3_K_M.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q3_K_M.gguf) | Q3_K_M | 3.81GB | false | Low quality. |
+| [Qwen2.5-Math-7B-Instruct-IQ3_M.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-IQ3_M.gguf) | IQ3_M | 3.57GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
+| [Qwen2.5-Math-7B-Instruct-Q2_K_L.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q2_K_L.gguf) | Q2_K_L | 3.55GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
+| [Qwen2.5-Math-7B-Instruct-Q3_K_S.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q3_K_S.gguf) | Q3_K_S | 3.49GB | false | Low quality, not recommended. |
+| [Qwen2.5-Math-7B-Instruct-IQ3_XS.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-IQ3_XS.gguf) | IQ3_XS | 3.35GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
+| [Qwen2.5-Math-7B-Instruct-Q2_K.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q2_K.gguf) | Q2_K | 3.02GB | false | Very low quality but surprisingly usable. |
+| [Qwen2.5-Math-7B-Instruct-IQ2_M.gguf](https://huggingface.co/bartowski/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-IQ2_M.gguf) | IQ2_M | 2.78GB | false | Relatively low quality, uses SOTA techniques to be surprisingly 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.
+Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
+Thanks!
+## Downloading using huggingface-cli
+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/Qwen2.5-Math-7B-Instruct-GGUF --include "Qwen2.5-Math-7B-Instruct-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/Qwen2.5-Math-7B-Instruct-GGUF --include "Qwen2.5-Math-7B-Instruct-Q8_0/*" --local-dir ./
+```
+You can either specify a new local-dir (Qwen2.5-Math-7B-Instruct-Q8_0) or download them all in place (./)
+## Q4_0_X_X
+These are *NOT* for Metal (Apple) offloading, only ARM chips.
+If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660)
+To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!).
+## Which file should I choose?
+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/ggerganov/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 and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
+The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
+## 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
+Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski