Instructions to use roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-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 roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16 # Run inference directly in the terminal: llama cli -hf roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16 # Run inference directly in the terminal: llama cli -hf roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-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 roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-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 roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16
Use Docker
docker model run hf.co/roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16
- LM Studio
- Jan
- Ollama
How to use roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf with Ollama:
ollama run hf.co/roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16
- Unsloth Studio
How to use roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-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 roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-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 roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf to start chatting
- Pi
How to use roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16
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": "roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16
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 roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf with Docker Model Runner:
docker model run hf.co/roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16
- Lemonade
How to use roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull roger33303/Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf:F16
Run and chat with the model
lemonade run user.Best_Model-llama3.2-3b-16bit-Instruct-Finetune-website-QnA-gguf-F16
List all available models
lemonade list
8c9e39b 80ef866 0073e4a c6cd55f 80ef866 c6cd55f 0073e4a c6cd55f 0073e4a 80ef866 8c9e39b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | ---
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Llama-3.2B Finetuned Model
## 1. Introduction
This model is a finetuned version of the Llama-3.2B large language model. It has been specifically trained to provide detailed and accurate responses for university course-related queries. This model offers insights on course details, fee structures, duration, and campus options, along with links to corresponding course pages. The finetuning process ensured domain-specific accuracy by utilizing a tailored dataset.
---
## GGUF Model:
This is a GGUF model made for running offline with Ollama. A Modelfile is also created to locally host and run this model with Ollama
## 2. Dataset Used for Finetuning
The finetuning of the Llama-3.2B model was performed using a private dataset obtained through web scraping. Data was collected from the University of Westminster website and included:
- Course titles
- Campus details
- Duration options (full-time, part-time, distance learning)
- Fee structures (for UK and international students)
- Course descriptions
- Direct links to course pages
This dataset was carefully cleaned and formatted to enhance the model's ability to provide precise responses to user queries.
---
## 3. How to Use This Model
To use the Llama-3.2B finetuned model, follow the steps below:
```python
from transformers import TextStreamer
def chatml(question, model):
messages = [{"role": "user", "content": question},]
inputs = tokenizer.apply_chat_template(messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",).to("cuda")
print(tokenizer.decode(inputs[0]))
text_streamer = TextStreamer(tokenizer, skip_special_tokens=True,
skip_prompt=True)
return model.generate(input_ids=inputs,
streamer=text_streamer,
max_new_tokens=512)
#Use the following example to test the model:
question = "Does the University of Westminster offer a course on AI, Data and Communication MA?"
x = chatml(question, model)
```
This setup ensures you can effectively query the Llama-3.2B finetuned model and receive detailed, relevant responses.
---
# Uploaded model
- **Developed by:** roger33303
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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