Instructions to use Zkare/Chatbot_Ielts_Assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zkare/Chatbot_Ielts_Assistant with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Zkare/Chatbot_Ielts_Assistant",
	filename="Qwen2.5-7B-Instruct.F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Zkare/Chatbot_Ielts_Assistant with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Zkare/Chatbot_Ielts_Assistant:F16
# Run inference directly in the terminal:
llama-cli -hf Zkare/Chatbot_Ielts_Assistant:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Zkare/Chatbot_Ielts_Assistant:F16
# Run inference directly in the terminal:
llama-cli -hf Zkare/Chatbot_Ielts_Assistant: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 Zkare/Chatbot_Ielts_Assistant:F16
# Run inference directly in the terminal:
./llama-cli -hf Zkare/Chatbot_Ielts_Assistant: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 Zkare/Chatbot_Ielts_Assistant:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Zkare/Chatbot_Ielts_Assistant:F16

Use Docker

docker model run hf.co/Zkare/Chatbot_Ielts_Assistant:F16

LM Studio
Jan
Ollama
How to use Zkare/Chatbot_Ielts_Assistant with Ollama:
```
ollama run hf.co/Zkare/Chatbot_Ielts_Assistant:F16
```

Unsloth Studio new

How to use Zkare/Chatbot_Ielts_Assistant 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 Zkare/Chatbot_Ielts_Assistant 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 Zkare/Chatbot_Ielts_Assistant to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Zkare/Chatbot_Ielts_Assistant to start chatting

Pi new

How to use Zkare/Chatbot_Ielts_Assistant with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Zkare/Chatbot_Ielts_Assistant: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": "Zkare/Chatbot_Ielts_Assistant:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Zkare/Chatbot_Ielts_Assistant with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Zkare/Chatbot_Ielts_Assistant: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 Zkare/Chatbot_Ielts_Assistant:F16

Run Hermes

hermes

Docker Model Runner
How to use Zkare/Chatbot_Ielts_Assistant with Docker Model Runner:
```
docker model run hf.co/Zkare/Chatbot_Ielts_Assistant:F16
```

Lemonade

How to use Zkare/Chatbot_Ielts_Assistant with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Zkare/Chatbot_Ielts_Assistant:F16

Run and chat with the model

lemonade run user.Chatbot_Ielts_Assistant-F16

List all available models

lemonade list

Chatbot IELTS Assistant

Model Description

Chatbot IELTS Assistant is a conversational model designed to support learners in practicing English for the IELTS exam. It provides interactive speaking practice, writing guidance, and explanations in both English and Vietnamese when necessary. The goal of this model is to help learners improve fluency, vocabulary, and coherence in IELTS-style tasks.

Author: Zkare
Languages: English (primary), Vietnamese (support explanations)
License: MIT

Intended Uses

Practice IELTS Speaking with simulated examiner-style questions.
Receive feedback and suggestions for Writing Task 1 and Task 2.
Get vocabulary, grammar, and structural recommendations for academic English.
Support bilingual learners with English explanations and optional Vietnamese assistance.

Limitations

The model does not replace an official IELTS instructor or examiner.
Feedback on writing and speaking should be taken as guidance only, not an official band score.
Some responses may contain inaccuracies in advanced grammar or idiomatic usage.

Training Data

The model is trained and fine-tuned on publicly available resources, including:

IELTS sample answers from open sources.
Conversational English datasets for academic and casual contexts.
Speaking and writing practice materials curated for language learning.

How to Use

from transformers import pipeline

chatbot = pipeline("text-generation", model="hf.co/Zkare/Chatbot_Ielts_Assistant:Q4_K_M") # You can change :Q4_K_M to :F16 for better one

prompt = "Can you ask me a Part 2 IELTS Speaking question?"
response = chatbot(prompt, max_length=256, do_sample=True, top_p=0.9, temperature=0.7)

print(response[0]["generated_text"])

Downloads last month: 47

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

16-bit

Inference Providers NEW

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Model tree for Zkare/Chatbot_Ielts_Assistant

Base model

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Finetuned

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Finetuned

unsloth/Qwen2.5-7B-Instruct

Quantized

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