Instructions to use Kiren261204/interview-reviewer-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Kiren261204/interview-reviewer-v1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Kiren261204/interview-reviewer-v1", filename="unsloth.Q4_K_M.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 Kiren261204/interview-reviewer-v1 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Kiren261204/interview-reviewer-v1:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Kiren261204/interview-reviewer-v1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Kiren261204/interview-reviewer-v1:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Kiren261204/interview-reviewer-v1: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 Kiren261204/interview-reviewer-v1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Kiren261204/interview-reviewer-v1: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 Kiren261204/interview-reviewer-v1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Kiren261204/interview-reviewer-v1:Q4_K_M
Use Docker
docker model run hf.co/Kiren261204/interview-reviewer-v1:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Kiren261204/interview-reviewer-v1 with Ollama:
ollama run hf.co/Kiren261204/interview-reviewer-v1:Q4_K_M
- Unsloth Studio new
How to use Kiren261204/interview-reviewer-v1 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 Kiren261204/interview-reviewer-v1 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 Kiren261204/interview-reviewer-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Kiren261204/interview-reviewer-v1 to start chatting
- Docker Model Runner
How to use Kiren261204/interview-reviewer-v1 with Docker Model Runner:
docker model run hf.co/Kiren261204/interview-reviewer-v1:Q4_K_M
- Lemonade
How to use Kiren261204/interview-reviewer-v1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Kiren261204/interview-reviewer-v1:Q4_K_M
Run and chat with the model
lemonade run user.interview-reviewer-v1-Q4_K_M
List all available models
lemonade list
π Interview Coach AI (GGUF)
This is a fine-tuned Llama-3 model trained to act as a strict and helpful technical interviewer. It evaluates interview answers and provides feedback.
Model File: unsloth.Q4_K_M.gguf
Base Model: Llama-3 (8B)
π» How to Use (LM Studio / Ollama)
System Prompt (Required):
You are an expert interviewer. Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
Chat Format: Use this format when chatting with the model:
Instruction:
Evaluate this interview answer.
Input:
[Paste the candidate's answer here]
Output:
- Downloads last month
- 3
4-bit