Instructions to use prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF",
	filename="Qwen-UMLS-7B-Instruct.F16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Qwen-UMLS-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-server -hf prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Qwen-UMLS-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 prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Qwen-UMLS-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 prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Qwen-UMLS-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 "prithivMLmods/Qwen-UMLS-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": "prithivMLmods/Qwen-UMLS-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 "prithivMLmods/Qwen-UMLS-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": "prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use prithivMLmods/Qwen-UMLS-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 prithivMLmods/Qwen-UMLS-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 prithivMLmods/Qwen-UMLS-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 prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF to start chatting

Pi new

How to use prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Qwen-UMLS-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": "prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/Qwen-UMLS-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-server -hf prithivMLmods/Qwen-UMLS-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 prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen-UMLS-7B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Qwen-UMLS-7B-Instruct-GGUF `[ Unified Medical Language System ]`

The Qwen-UMLS-7B-Instruct model is a specialized, instruction-tuned language model designed for medical and healthcare-related tasks. It is fine-tuned on the Qwen2.5-7B-Instruct base model using the UMLS (Unified Medical Language System) dataset, making it an invaluable tool for medical professionals, researchers, and developers building healthcare applications.

File Name	Size	Description	Upload Status
`.gitattributes`	1.79 kB	Specifies LFS tracking for large model files.	Uploaded
`Qwen-UMLS-7B-Instruct.F16.gguf`	15.2 GB	Full-precision GGUF format of the model.	Uploaded (LFS)
`Qwen-UMLS-7B-Instruct.Q4_K_M.gguf`	4.68 GB	Quantized GGUF format (Q4_K_M) for smaller size and faster inference.	Uploaded (LFS)
`Qwen-UMLS-7B-Instruct.Q5_K_M.gguf`	5.44 GB	Quantized GGUF format (Q5_K_M) for balanced performance and accuracy.	Uploaded (LFS)
`Qwen-UMLS-7B-Instruct.Q8_0.gguf`	8.1 GB	Higher precision quantized GGUF format (Q8_0).	Uploaded (LFS)
`README.md`	315 Bytes	Basic project information file.	Updated
`config.json`	29 Bytes	Minimal configuration file for the model.	Uploaded

Key Features:

Medical Expertise:
- Trained on the UMLS dataset, ensuring deep domain knowledge in medical terminology, diagnostics, and treatment plans.
Instruction-Following:
- Designed to handle complex queries with clarity and precision, suitable for diagnostic support, patient education, and research.
High-Parameter Model:
- Leverages 7 billion parameters to deliver detailed, contextually accurate responses.

Training Details:

Base Model: Qwen2.5-7B-Instruct
Dataset: avaliev/UMLS
- Comprehensive dataset of medical terminologies, relationships, and use cases with 99.1k samples.

Capabilities:

Clinical Text Analysis:
- Interpret medical notes, prescriptions, and research articles.
Question-Answering:
- Answer medical queries, provide explanations for symptoms, and suggest treatments based on user prompts.
Educational Support:
- Assist in learning medical terminologies and understanding complex concepts.
Healthcare Applications:
- Integrate into clinical decision-support systems or patient care applications.

Usage Instructions:

Setup: Download all files and ensure compatibility with the Hugging Face Transformers library.

Loading the Model:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Qwen-UMLS-7B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

Generate Medical Text:

input_text = "What are the symptoms and treatments for diabetes?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Customizing Outputs: Modify generation_config.json to optimize output style:
- temperature for creativity vs. determinism.
- max_length for concise or extended responses.

Applications:

Clinical Support:
- Assist healthcare providers with quick, accurate information retrieval.
Patient Education:
- Provide patients with understandable explanations of medical conditions.
Medical Research:
- Summarize or analyze complex medical research papers.
AI-Driven Diagnostics:
- Integrate with diagnostic systems for preliminary assessments.

Run with Ollama [ Ollama Run ]

Overview

Ollama is a powerful tool that allows you to run machine learning models effortlessly. This guide will help you download, install, and run your own GGUF models in just a few minutes.

Download and Install Ollama
Steps to Run GGUF Models
Running the Model
Sample Usage

Download and Install Ollama🦙

To get started, download Ollama from https://ollama.com/download and install it on your Windows or Mac system.

Steps to Run GGUF Models

1. Create the Model File

First, create a model file and name it appropriately. For example, you can name your model file metallama.

2. Add the Template Command

In your model file, include a FROM line that specifies the base model file you want to use. For instance:

FROM Llama-3.2-1B.F16.gguf

Ensure that the model file is in the same directory as your script.

3. Create and Patch the Model

Open your terminal and run the following command to create and patch your model:

ollama create metallama -f ./metallama

Once the process is successful, you will see a confirmation message.

To verify that the model was created successfully, you can list all models with:

ollama list

Make sure that metallama appears in the list of models.

Running the Model

To run your newly created model, use the following command in your terminal:

ollama run metallama

Sample Usage / Test

In the command prompt, you can execute:

D:\>ollama run metallama

You can interact with the model like this:

>>> write a mini passage about space x
Space X, the private aerospace company founded by Elon Musk, is revolutionizing the field of space exploration.
With its ambitious goals to make humanity a multi-planetary species and establish a sustainable human presence in
the cosmos, Space X has become a leading player in the industry. The company's spacecraft, like the Falcon 9, have
demonstrated remarkable capabilities, allowing for the transport of crews and cargo into space with unprecedented
efficiency. As technology continues to advance, the possibility of establishing permanent colonies on Mars becomes
increasingly feasible, thanks in part to the success of reusable rockets that can launch multiple times without
sustaining significant damage. The journey towards becoming a multi-planetary species is underway, and Space X
plays a pivotal role in pushing the boundaries of human exploration and settlement.

Conclusion

With these simple steps, you can easily download, install, and run your own models using Ollama. Whether you're exploring the capabilities of Llama or building your own custom models, Ollama makes it accessible and efficient.

This README provides clear instructions and structured information to help users navigate the process of using Ollama effectively. Adjust any sections as needed based on your specific requirements or additional details you may want to include.

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GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF

Base model

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Quantized

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prithivMLmods
/

Qwen-UMLS-7B-Instruct-GGUF

Qwen-UMLS-7B-Instruct-GGUF `[ Unified Medical Language System ]`

Key Features:

Training Details:

Capabilities:

Usage Instructions:

Applications:

Run with Ollama [ Ollama Run ]

Overview

Table of Contents

Download and Install Ollama🦙

Steps to Run GGUF Models

1. Create the Model File

2. Add the Template Command

3. Create and Patch the Model

Running the Model

Sample Usage / Test

Conclusion

Model tree for prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF

Dataset used to train prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF

Collections including prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF

GPT-Generated Unified Format (GGUF)

Custom Build Models

Qwen-UMLS-7B-Instruct-GGUF [ Unified Medical Language System ]

Key Features:

Training Details:

Capabilities:

Usage Instructions:

Applications:

Run with Ollama [ Ollama Run ]

Overview

Table of Contents

Download and Install Ollama🦙

Steps to Run GGUF Models

1. Create the Model File

2. Add the Template Command

3. Create and Patch the Model

Running the Model

Sample Usage / Test

Conclusion

Model tree for prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF

Dataset used to train prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF

Collections including prithivMLmods/Qwen-UMLS-7B-Instruct-GGUF

Qwen-UMLS-7B-Instruct-GGUF `[ Unified Medical Language System ]`