Instructions to use QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF",
	filename="distilgpt2-finetuned-python_code_instructions_18k_alpaca.Q2_K.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 QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-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 QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-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 QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF:Q4_K_M

SGLang

How to use QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-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 QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-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 QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF

This is quantized version of Vishaltiwari2019/distilgpt2-finetuned-python_code_instructions_18k_alpaca created using llama.cpp

Original Model Card

distilgpt2-finetuned-python_code_instructions_18k_alpaca

This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.5063

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
1.7264	1.0	3861	1.5890
1.6046	2.0	7722	1.5214
1.5359	3.0	11583	1.5063

Framework versions

Transformers 4.39.3
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 207

GGUF

Model size

0.1B params

Architecture

gpt2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF

Base model

distilbert/distilgpt2

Quantized

(23)

this model

QuantFactory
/

distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF

QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF

Original Model Card

distilgpt2-finetuned-python_code_instructions_18k_alpaca

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF

Dataset used to train QuantFactory/distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF