Qwen2.5-7B-Instruct-MathCoder-GGUF

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

Libraries

How to use Mosfets/Qwen2.5-7B-Instruct-MathCoder-GGUF with Transformers:

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

llama-cpp-python

How to use Mosfets/Qwen2.5-7B-Instruct-MathCoder-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Mosfets/Qwen2.5-7B-Instruct-MathCoder-GGUF",
	filename="Qwen2.5-7B-Instruct-MathCoder.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Use Docker

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

LM Studio
Jan
Ollama
How to use Mosfets/Qwen2.5-7B-Instruct-MathCoder-GGUF with Ollama:
```
ollama run hf.co/Mosfets/Qwen2.5-7B-Instruct-MathCoder-GGUF:Q4_K_M
```

Unsloth Studio

How to use Mosfets/Qwen2.5-7B-Instruct-MathCoder-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 Mosfets/Qwen2.5-7B-Instruct-MathCoder-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 Mosfets/Qwen2.5-7B-Instruct-MathCoder-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use Mosfets/Qwen2.5-7B-Instruct-MathCoder-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Mosfets/Qwen2.5-7B-Instruct-MathCoder-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": "Mosfets/Qwen2.5-7B-Instruct-MathCoder-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Mosfets/Qwen2.5-7B-Instruct-MathCoder-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 Mosfets/Qwen2.5-7B-Instruct-MathCoder-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 Mosfets/Qwen2.5-7B-Instruct-MathCoder-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Mosfets/Qwen2.5-7B-Instruct-MathCoder-GGUF with Docker Model Runner:
```
docker model run hf.co/Mosfets/Qwen2.5-7B-Instruct-MathCoder-GGUF:Q4_K_M
```

Lemonade

How to use Mosfets/Qwen2.5-7B-Instruct-MathCoder-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Mosfets

munish0838 commited on 6 days ago

Commit

c74c8bf

0 Parent(s):

Duplicate from QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF

Browse files

Co-authored-by: Munish Kumar <munish0838@users.noreply.huggingface.co>

Files changed (16) hide show

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+---
+base_model:
+- Qwen/Qwen2.5-Coder-7B-Instruct
+- Qwen/Qwen2.5-7B-Instruct
+- Qwen/Qwen2.5-Math-7B-Instruct
+library_name: transformers
+tags:
+- mergekit
+- merge
+---
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+# QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF
+This is quantized version of [DeepMount00/Qwen2.5-7B-Instruct-MathCoder](https://huggingface.co/DeepMount00/Qwen2.5-7B-Instruct-MathCoder) created using llama.cpp
+# Original Model Card
+# merge
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+## Merge Details
+### Merge Method
+This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) as a base.
+### Models Merged
+The following models were included in the merge:
+* [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
+* [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct)
+### Configuration
+The following YAML configuration was used to produce this model:
+```yaml
+models:
+  - model: Qwen/Qwen2.5-7B-Instruct
+    #no parameters necessary for base model
+  - model: Qwen/Qwen2.5-Math-7B-Instruct
+    parameters:
+      density: 0.5
+      weight: 0.5
+  - model: Qwen/Qwen2.5-Coder-7B-Instruct
+    parameters:
+      density: 0.5
+      weight: 0.5
+merge_method: ties
+base_model: Qwen/Qwen2.5-7B-Instruct
+parameters:
+  normalize: false
+  int8_mask: true
+dtype: float16
+```