Instructions to use MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF", dtype="auto") - llama-cpp-python
How to use MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF", filename="unsloth.F16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF:F16 # Run inference directly in the terminal: llama-cli -hf MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF:F16 # Run inference directly in the terminal: llama-cli -hf MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF:F16
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 MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF:F16
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 MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF:F16
Use Docker
docker model run hf.co/MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF:F16
- LM Studio
- Jan
- Ollama
How to use MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF with Ollama:
ollama run hf.co/MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF:F16
- Unsloth Studio new
How to use MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-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 MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-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 MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF to start chatting
- Docker Model Runner
How to use MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF with Docker Model Runner:
docker model run hf.co/MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF:F16
- Lemonade
How to use MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MasterControlAIML/Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF:F16
Run and chat with the model
lemonade run user.Qwen2.5-7b-Answer-Distractor-MCQ-Generation-GGUF-F16
List all available models
lemonade list
Uploaded model
- Model Usage: Given a context paragraph and a question, generate answer option and three distractor options (total 4 mcqs)
- Training Data: MedMCQA with enhanced context (https://huggingface.co/datasets/MasterControlAIML/Medmcqa-For-FinetuningQwen)
- Developed by: MasterControl
- License: apache-2.0
- Finetuned from model : unsloth/qwen2.5-7b-bnb-4bit
- Eval Dataset : https://huggingface.co/datasets/MasterControlAIML/Eval_Answer_Distractors_MCQs
Template to prompt
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
Instruction: {}
Input: {}
Response: {}"""
Example for answer-distractors generation use case:
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
Instruction:
Based on the context paragraph and question in input, generate a correct answer and three distractor options from the context paragraph:
Input:
Context Paragraph: In pediatric cases of liver failure, the prognosis and risk factors for mortality are varied and can depend greatly on several critical components. While children generally have a somewhat more favorable outcome compared to adults, certain factors can significantly increase the risk of mortality. Notably, sepsis, particularly caused by Gram-negative bacteria, is seen as the most important prognostic factor for death in these cases. This type of infection can lead to severe complications, increasing the risk of mortality despite medical interventions.On the other hand, factors such as increasing transaminases and bilirubin levels are often monitored but are not considered as pivotal in predicting mortality outcomes. Similarly, while an increasing prothrombin time, which reflects coagulation status, may raise important clinical concerns, it does not have a direct correlation to transplant or post-transplant survival according to current pediatric assessments. Additionally, other poor prognostic indicators include liver failure due to specific causes like Wilson disease or idiopathic origins, the occurrence of Stage IV hepatic encephalopathy where brain stem herniation is a major threat, age being less than one year, severe hemorrhage, renal failure, and the need for dialysis before transplantation. Understanding these risk factors aids in managing expectations and planning for comprehensive care in cases of pediatric liver failure.
Question: In a child with active liver failure, the most important prognosis factor for death is –
Response:
Correct Answer: Sepsis Distractors: Increasing transaminases,Increasing bilirubin,Increasing prothrombin time<|endoftext|>
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
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