Instructions to use HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf", filename="mistral-7b-biology-mcq-finetuned.Q4_K_M.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 HangTuahMalayWarrior/mistral-biology-mcq-qlora_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 HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf HangTuahMalayWarrior/mistral-biology-mcq-qlora_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 HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf HangTuahMalayWarrior/mistral-biology-mcq-qlora_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 HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf HangTuahMalayWarrior/mistral-biology-mcq-qlora_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 HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf:Q4_K_M
Use Docker
docker model run hf.co/HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf with Ollama:
ollama run hf.co/HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf:Q4_K_M
- Unsloth Studio
How to use HangTuahMalayWarrior/mistral-biology-mcq-qlora_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 HangTuahMalayWarrior/mistral-biology-mcq-qlora_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 HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf to start chatting
- Atomic Chat new
- Docker Model Runner
How to use HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf with Docker Model Runner:
docker model run hf.co/HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf:Q4_K_M
- Lemonade
How to use HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf:Q4_K_M
Run and chat with the model
lemonade run user.mistral-biology-mcq-qlora_gguf-Q4_K_M
List all available models
lemonade list
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.2
tags:
- biology
- medical
- mcq
- gguf
- ollama
- mistral
- education
language:
- en
metrics:
- accuracy
Mistral-Biology-MCQ-Strict
This model is a high-precision, zero-hallucination configuration of a Mistral-7B fine-tune, specifically engineered for Biology Multiple Choice Questions (MCQs). It is optimized to provide deterministic, single-letter outputs (A, B, C, D) for automated grading and assessment.
Model Details
Model Description
- Developed by: HangTuahMalayWarrior (Fine-tuning)
- Model type: GGUF (Quantized for Ollama)
- Language(s) (NLP): English
- License: Apache-2.0
- Finetuned from model: Mistral-7B-v0.1 (via QLoRA)
Uses
Direct Use
This model is intended for strictly formatted MCQ processing. It is designed to be used with a specific Ollama Modelfile that sets the temperature to 0 and utilizes stop sequences to prevent "model bleed" or conversational rambling.
Out-of-Scope Use
- General purpose chatting or creative writing.
- Non-biological scientific domains (performance may vary).
- Medical advice (This is an educational tool, not a clinical diagnostic tool).
Bias, Risks, and Limitations
The model has been fine-tuned on a specific subset of Biology MCQs. While it is highly accurate for that domain, it may exhibit "catastrophic forgetting" regarding general knowledge topics not related to biology.
Recommendations
To maintain a 100% accuracy target, users must use the provided Modelfile settings. High temperature settings will cause the model to leak training tags (e.g., <|start_of_role|>).
How to Get Started with the Model
To run this model on Windows using Ollama without hallucinations, create a file named Modelfile with the following content:
FROM hf.co/HangTuahMalayWarrior/mistral-biology-mcq-qlora_gguf:Q4_K_M
# Strict Precision Parameters
PARAMETER temperature 0.0
PARAMETER top_k 1
PARAMETER top_p 0.01
# Stop Sequences to prevent training-tag leaks
PARAMETER stop "<|"
PARAMETER stop "\n"
PARAMETER stop "user"
SYSTEM """You are a strict MCQ answering bot.
Your output must ONLY be the letter of the correct option (e.g., A, B, C, or D).
Do not provide explanations. Do not repeat the question."""