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

Libraries

How to use QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF with Transformers:

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

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

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

llama-cpp-python

How to use QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF",
	filename="Qwen2.5-7B-HomerAnvita-NerdMix.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-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 QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-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 QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-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 "QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-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": "QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-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 "QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-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": "QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-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 QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-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 QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-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": "QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-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 QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-7B-HomerAnvita-NerdMix-GGUF-Q4_K_M

List all available models

lemonade list

aashish1904 commited on Dec 3, 2024

Commit

c38554a

verified ·

1 Parent(s): 688bfd6

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+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+tags:
+- merge
+- mergekit
+- lazymergekit
+- bfloat16
+- roleplay
+- creative
+- instruct
+- anvita
+- qwen
+- nerd
+- homer
+- Qandora
+base_model:
+- bunnycore/Qandora-2.5-7B-Creative
+- allknowingroger/HomerSlerp1-7B
+- sethuiyer/Qwen2.5-7B-Anvita
+- fblgit/cybertron-v4-qw7B-MGS
+- jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0
+- newsbang/Homer-v0.5-Qwen2.5-7B
+pipeline_tag: text-generation
+model-index:
+- name: Qwen2.5-7B-HomerAnvita-NerdMix
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: IFEval (0-Shot)
+      type: HuggingFaceH4/ifeval
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: inst_level_strict_acc and prompt_level_strict_acc
+      value: 77.08
+      name: strict accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: BBH (3-Shot)
+      type: BBH
+      args:
+        num_few_shot: 3
+    metrics:
+    - type: acc_norm
+      value: 36.58
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MATH Lvl 5 (4-Shot)
+      type: hendrycks/competition_math
+      args:
+        num_few_shot: 4
+    metrics:
+    - type: exact_match
+      value: 29.53
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GPQA (0-shot)
+      type: Idavidrein/gpqa
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 9.28
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MuSR (0-shot)
+      type: TAUR-Lab/MuSR
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 14.41
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU-PRO (5-shot)
+      type: TIGER-Lab/MMLU-Pro
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 38.13
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix
+      name: Open LLM Leaderboard
+---
+[![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-HomerAnvita-NerdMix-GGUF
+This is quantized version of [ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix](https://huggingface.co/ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix) created using llama.cpp
+# Original Model Card
+# ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix
+**ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix** is an advanced language model meticulously crafted by merging five pre-trained models using the powerful [mergekit](https://github.com/cg123/mergekit) framework. This fusion leverages the **Model Stock** merge method to combine the creative prowess of **Qandora**, the instructive capabilities of **Qwen-Instruct-Fusion**, the sophisticated blending of **HomerSlerp1**, the mathematical precision of **Cybertron-MGS**, and the uncensored expertise of **Qwen-Nerd**. The resulting model excels in creative text generation, contextual understanding, technical reasoning, and dynamic conversational interactions.
+## 🚀 Merged Models
+This model merge incorporates the following:
+- [**bunnycore/Qandora-2.5-7B-Creative**](https://huggingface.co/bunnycore/Qandora-2.5-7B-Creative): Specializes in creative text generation, enhancing the model's ability to produce imaginative and diverse content.
+- [**allknowingroger/HomerSlerp1-7B**](https://huggingface.co/allknowingroger/HomerSlerp1-7B): Utilizes spherical linear interpolation (SLERP) to blend model weights smoothly, ensuring a harmonious integration of different model attributes.
+- [**sethuiyer/Qwen2.5-7B-Anvita**](https://huggingface.co/sethuiyer/Qwen2.5-7B-Anvita): Focuses on instruction-following capabilities, improving the model's performance in understanding and executing user commands.
+- [**fblgit/cybertron-v4-qw7B-MGS**](https://huggingface.co/fblgit/cybertron-v4-qw7B-MGS): Enhances mathematical reasoning and precision, enabling the model to handle complex computational tasks effectively.
+- [**jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0**](https://huggingface.co/jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0): Provides uncensored expertise and robust technical knowledge, making the model suitable for specialized technical support and information retrieval.
+- [**newsbang/Homer-v0.5-Qwen2.5-7B**](https://huggingface.co/newsbang/Homer-v0.5-Qwen2.5-7B): Acts as the foundational conversational model, providing robust language comprehension and generation capabilities.
+## 🧩 Merge Configuration
+The configuration below outlines how the models are merged using the **Model Stock** method. This approach ensures a balanced and effective integration of the unique strengths from each source model.
+```yaml
+# Merge configuration for ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix using Model Stock
+models:
+  - model: bunnycore/Qandora-2.5-7B-Creative
+  - model: allknowingroger/HomerSlerp1-7B
+  - model: sethuiyer/Qwen2.5-7B-Anvita
+  - model: fblgit/cybertron-v4-qw7B-MGS
+  - model: jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0
+merge_method: model_stock
+base_model: newsbang/Homer-v0.5-Qwen2.5-7B
+normalize: false
+int8_mask: true
+dtype: bfloat16
+```
+### Key Parameters
+- **Merge Method (`merge_method`):** Utilizes the **Model Stock** method, as described in [Model Stock](https://arxiv.org/abs/2403.19522), to effectively combine multiple models by leveraging their strengths.
+- **Models (`models`):** Specifies the list of models to be merged:
+  - **bunnycore/Qandora-2.5-7B-Creative:** Enhances creative text generation.
+  - **allknowingroger/HomerSlerp1-7B:** Facilitates smooth blending of model weights using SLERP.
+  - **sethuiyer/Qwen2.5-7B-Anvita:** Improves instruction-following capabilities.
+  - **fblgit/cybertron-v4-qw7B-MGS:** Enhances mathematical reasoning and precision.
+  - **jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0:** Provides uncensored technical expertise.
+- **Base Model (`base_model`):** Defines the foundational model for the merge, which is **newsbang/Homer-v0.5-Qwen2.5-7B** in this case.
+- **Normalization (`normalize`):** Set to `false` to retain the original scaling of the model weights during the merge.
+- **INT8 Mask (`int8_mask`):** Enabled (`true`) to apply INT8 quantization masking, optimizing the model for efficient inference without significant loss in precision.
+- **Data Type (`dtype`):** Uses `bfloat16` to maintain computational efficiency while ensuring high precision.
+## 🏆 Performance Highlights
+- **Creative Text Generation:** Enhanced ability to produce imaginative and diverse content suitable for creative writing, storytelling, and content creation.
+- **Instruction Following:** Improved performance in understanding and executing user instructions, making the model more responsive and accurate in task execution.
+- **Mathematical Reasoning:** Enhanced capability to handle complex computational tasks with high precision, suitable for technical and analytical applications.
+- **Uncensored Technical Expertise:** Provides robust technical knowledge without content restrictions, making it ideal for specialized technical support and information retrieval.
+- **Optimized Inference:** INT8 masking and `bfloat16` data type contribute to efficient computation, enabling faster response times without compromising quality.
+## 🎯 Use Case & Applications
+**ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix** is designed to excel in environments that demand a combination of creative generation, precise instruction following, mathematical reasoning, and technical expertise. Ideal applications include:
+- **Creative Writing Assistance:** Aiding authors and content creators in generating imaginative narratives, dialogues, and descriptive text.
+- **Interactive Storytelling and Role-Playing:** Enhancing dynamic and engaging interactions in role-playing games and interactive storytelling platforms.
+- **Educational Tools and Tutoring Systems:** Providing detailed explanations, answering questions, and assisting in educational content creation with contextual understanding.
+- **Technical Support and Customer Service:** Offering accurate and contextually relevant responses in technical support scenarios, improving user satisfaction.
+- **Content Generation for Marketing:** Creating compelling and diverse marketing copy, social media posts, and promotional material with creative flair.
+- **Mathematical Problem Solving:** Assisting in solving complex mathematical problems and providing step-by-step explanations for educational purposes.
+- **Technical Documentation and Analysis:** Generating detailed technical documents, reports, and analyses with high precision and clarity.
+## 📝 Usage
+To utilize **ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix**, follow the steps below:
+### Installation
+First, install the necessary libraries:
+```bash
+pip install -qU transformers accelerate
+```
+### Example Code
+Below is an example of how to load and use the model for text generation:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+# Define the model name
+model_name = "ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix"
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Load the model
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+# Initialize the pipeline
+text_generator = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+# Define the input prompt
+prompt = "Explain the significance of artificial intelligence in modern healthcare."
+# Generate the output
+outputs = text_generator(
+    prompt,
+    max_new_tokens=150,
+    do_sample=True,
+    temperature=0.7,
+    top_k=50,
+    top_p=0.95
+)
+# Print the generated text
+print(outputs[0]["generated_text"])
+```
+### Notes
+- **Fine-Tuning:** This merged model may require fine-tuning to optimize performance for specific applications or domains.
+- **Resource Requirements:** Ensure that your environment has sufficient computational resources, especially GPU-enabled hardware, to handle the model efficiently during inference.
+- **Customization:** Users can adjust parameters such as `temperature`, `top_k`, and `top_p` to control the creativity and diversity of the generated text.
+## 📜 License
+This model is open-sourced under the **Apache-2.0 License**.
+## 💡 Tags
+- `merge`
+- `mergekit`
+- `model_stock`
+- `Qwen`
+- `Homer`
+- `Anvita`
+- `Nerd`
+- `ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix`
+- `bunnycore/Qandora-2.5-7B-Creative`
+- `allknowingroger/HomerSlerp1-7B`
+- `sethuiyer/Qwen2.5-7B-Anvita`
+- `fblgit/cybertron-v4-qw7B-MGS`
+- `jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0`
+- `newsbang/Homer-v0.5-Qwen2.5-7B`
+---
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ZeroXClem__Qwen2.5-7B-HomerAnvita-NerdMix)
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |34.17|
+|IFEval (0-Shot)    |77.08|
+|BBH (3-Shot)       |36.58|
+|MATH Lvl 5 (4-Shot)|29.53|
+|GPQA (0-shot)      | 9.28|
+|MuSR (0-shot)      |14.41|
+|MMLU-PRO (5-shot)  |38.13|