Instructions to use tharun2909/disligpt_FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tharun2909/disligpt_FT with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tharun2909/disligpt_FT",
	filename="finetuned-distilgptGGUF.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 tharun2909/disligpt_FT with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tharun2909/disligpt_FT
# Run inference directly in the terminal:
llama-cli -hf tharun2909/disligpt_FT

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tharun2909/disligpt_FT
# Run inference directly in the terminal:
llama-cli -hf tharun2909/disligpt_FT

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 tharun2909/disligpt_FT
# Run inference directly in the terminal:
./llama-cli -hf tharun2909/disligpt_FT

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 tharun2909/disligpt_FT
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tharun2909/disligpt_FT

Use Docker

docker model run hf.co/tharun2909/disligpt_FT

LM Studio
Jan
Ollama
How to use tharun2909/disligpt_FT with Ollama:
```
ollama run hf.co/tharun2909/disligpt_FT
```

Unsloth Studio new

How to use tharun2909/disligpt_FT 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 tharun2909/disligpt_FT 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 tharun2909/disligpt_FT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tharun2909/disligpt_FT to start chatting

Docker Model Runner
How to use tharun2909/disligpt_FT with Docker Model Runner:
```
docker model run hf.co/tharun2909/disligpt_FT
```

Lemonade

How to use tharun2909/disligpt_FT with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tharun2909/disligpt_FT

Run and chat with the model

lemonade run user.disligpt_FT-{{QUANT_TAG}}

List all available models

lemonade list

disligpt_FT

File size: 236 Bytes

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{
  "model_name": "finetuned-distilgptGGUF",
  "quantization_type": "q8_0",
  "source_model": "/tmp/inputs/merged_model_dir/data",
  "gguf_file": "finetuned-distilgptGGUF.gguf",
  "file_size_mb": 1.89,
  "conversion_tool": "llama.cpp"
}