Instructions to use QuantFactory/Tulu-MathLingo-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Tulu-MathLingo-8B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/Tulu-MathLingo-8B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Tulu-MathLingo-8B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Tulu-MathLingo-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Tulu-MathLingo-8B-GGUF",
	filename="Tulu-MathLingo-8B.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/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Tulu-MathLingo-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Tulu-MathLingo-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Tulu-MathLingo-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Tulu-MathLingo-8B-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Tulu-MathLingo-8B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Tulu-MathLingo-8B-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-GGUF to start chatting

How to use QuantFactory/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-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/Tulu-MathLingo-8B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use QuantFactory/Tulu-MathLingo-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Tulu-MathLingo-8B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Tulu-MathLingo-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Tulu-MathLingo-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Tulu-MathLingo-8B-GGUF-Q4_K_M

List all available models

lemonade list

aashish1904 commited on Dec 28, 2024

Commit

94d2b9e

verified ·

1 Parent(s): d55faf7

Upload README.md with huggingface_hub

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README.md +138 -0

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	@@ -0,0 +1,138 @@

+---
+license: creativeml-openrail-m
+datasets:
+- microsoft/orca-math-word-problems-200k
+language:
+- en
+base_model:
+- allenai/Llama-3.1-Tulu-3-8B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- safetensors
+- math
+- tulu
+- trl
+- llama
+- text-generation-inference
+- math_lingo
+---
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+# QuantFactory/Tulu-MathLingo-8B-GGUF
+This is quantized version of [prithivMLmods/Tulu-MathLingo-8B](https://huggingface.co/prithivMLmods/Tulu-MathLingo-8B) created using llama.cpp
+# Original Model Card
+# Tulu-MathLingo-8B Model Files
+The **Tulu-MathLingo-8B** model is a fine-tuned version of **meta-llama/Llama-3.1-8B**, optimized for solving mathematical word problems and reasoning tasks in English and the Tulu language. The model integrates advanced language understanding and reasoning capabilities with a focus on providing solutions to math-related queries.
+| **File Name**                     | **Size**     | **Description**                                 | **Upload Status** |
+|-----------------------------------|--------------|------------------------------------------------|-------------------|
+| `.gitattributes`                  | 1.57 kB     | Configures LFS tracking for large files.        | Updated           |
+| `README.md`                       | 292 Bytes   | Basic details about the uploaded model.         | Updated           |
+| `config.json`                     | 988 Bytes   | Contains model architecture and metadata.       | Uploaded          |
+| `generation_config.json`          | 241 Bytes   | Parameters for text generation (e.g., length, temperature). | Uploaded          |
+| `model-00001-of-00004.safetensors`| 4.98 GB     | Part 1 of model weights.                        | Uploaded (LFS)    |
+| `model-00002-of-00004.safetensors`| 5 GB        | Part 2 of model weights.                        | Uploaded (LFS)    |
+| `model-00003-of-00004.safetensors`| 4.92 GB     | Part 3 of model weights.                        | Uploaded (LFS)    |
+| `model-00004-of-00004.safetensors`| 1.17 GB     | Part 4 of model weights.                        | Uploaded (LFS)    |
+| `model.safetensors.index.json`    | 25.4 kB     | Index file for multi-part model weights.        | Uploaded          |
+| `special_tokens_map.json`         | 462 Bytes   | Maps special tokens (e.g., `<PAD>`, `<EOS>`).   | Uploaded          |
+| `tokenizer.json`                  | 17.2 MB     | Full tokenizer configuration.                   | Uploaded (LFS)    |
+| `tokenizer_config.json`           | 57.6 kB     | Metadata for tokenizer usage.                   | Uploaded          |
+### Sample Solve
+![xvxv.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vX8m-ltsacAztTF9SqDxB.png)
+### **Key Features**
+1. **Multilingual Math Reasoning:**
+   - Designed for solving complex math problems in **English** and **Tulu**.
+2. **Text Generation:**
+   - Generates detailed and contextually accurate text responses.
+3. **Fine-Tuned Specializations:**
+   - Trained on the **microsoft/orca-math-word-problems-200k** dataset for word problem-solving.
+4. **Special Token Mapping:**
+   - Configured to use tokens for specific functions such as `<PAD>` and `<EOS>` effectively.
+5. **Secure and Efficient Storage:**
+   - Model weights are stored in the **Safetensors** format for secure and faster inference.
+6. **Large Parameter Size:**
+   - 8.03 billion parameters enable handling complex queries and multi-turn conversations.
+---
+### **Training Details**
+- **Base Model:** [meta-llama/Llama-3.1-8B](#)
+- **Fine-Tuned:**
+   - Through multiple stages: **SFT (Supervised Fine-Tuning)** and **DPO (Direct Preference Optimization)**.
+- **Dataset:**
+   - Trained on **200k word problems** from the **Microsoft Orca Math Word Problems Dataset**.
+- **Model Size:**
+   - 8.03B parameters, optimized for **FP16** tensor type.
+---
+### **Applications**
+1. **Mathematical Word Problems:**
+   - Solve structured or unstructured math problems in natural language.
+2. **Conversational AI for Math:**
+   - Engage users in interactive dialogues focused on math and logic reasoning.
+3. **Multilingual Support:**
+   - Supports queries in **Tulu** and **English**, enhancing accessibility.
+4. **Education Tools:**
+   - Useful in tutoring systems for math, helping students with problem-solving.
+---
+### **Usage**
+#### **Loading the Model**
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "prithivMLmods/Tulu-MathLingo-8B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="fp16")
+```
+---
+##### **Math Word Problem**
+```python
+query = "If a train travels 60 miles in 2 hours, what is its average speed?"
+inputs = tokenizer(query, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print("Answer:", response)
+```
+### **Performance Requirements**
+- **Hardware:**
+   - Requires a GPU with at least **24GB VRAM** for optimal performance due to model size and FP16 usage.
+- **Optimization:**
+   - Use mixed precision (`fp16`) for reduced memory footprint.
+   - Split inference across multiple GPUs if necessary.
+---