Instructions to use devparagiri/Testing-20250820-100957 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use devparagiri/Testing-20250820-100957 with Transformers:

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

pipe = pipeline("text-generation", model="devparagiri/Testing-20250820-100957")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("devparagiri/Testing-20250820-100957")
model = AutoModelForCausalLM.from_pretrained("devparagiri/Testing-20250820-100957")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use devparagiri/Testing-20250820-100957 with PEFT:
```
Task type is invalid.
```

llama-cpp-python

How to use devparagiri/Testing-20250820-100957 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="devparagiri/Testing-20250820-100957",
	filename="model-q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use devparagiri/Testing-20250820-100957 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf devparagiri/Testing-20250820-100957:Q8_0
# Run inference directly in the terminal:
llama-cli -hf devparagiri/Testing-20250820-100957:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf devparagiri/Testing-20250820-100957:Q8_0
# Run inference directly in the terminal:
llama-cli -hf devparagiri/Testing-20250820-100957:Q8_0

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 devparagiri/Testing-20250820-100957:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf devparagiri/Testing-20250820-100957:Q8_0

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 devparagiri/Testing-20250820-100957:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf devparagiri/Testing-20250820-100957:Q8_0

Use Docker

docker model run hf.co/devparagiri/Testing-20250820-100957:Q8_0

LM Studio
Jan

vLLM

How to use devparagiri/Testing-20250820-100957 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "devparagiri/Testing-20250820-100957"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "devparagiri/Testing-20250820-100957",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/devparagiri/Testing-20250820-100957:Q8_0

SGLang

How to use devparagiri/Testing-20250820-100957 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 "devparagiri/Testing-20250820-100957" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "devparagiri/Testing-20250820-100957",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "devparagiri/Testing-20250820-100957" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "devparagiri/Testing-20250820-100957",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use devparagiri/Testing-20250820-100957 with Ollama:
```
ollama run hf.co/devparagiri/Testing-20250820-100957:Q8_0
```

Unsloth Studio new

How to use devparagiri/Testing-20250820-100957 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 devparagiri/Testing-20250820-100957 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 devparagiri/Testing-20250820-100957 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for devparagiri/Testing-20250820-100957 to start chatting

Pi new

How to use devparagiri/Testing-20250820-100957 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf devparagiri/Testing-20250820-100957:Q8_0

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": "devparagiri/Testing-20250820-100957:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use devparagiri/Testing-20250820-100957 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf devparagiri/Testing-20250820-100957:Q8_0

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 devparagiri/Testing-20250820-100957:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use devparagiri/Testing-20250820-100957 with Docker Model Runner:
```
docker model run hf.co/devparagiri/Testing-20250820-100957:Q8_0
```

Lemonade

How to use devparagiri/Testing-20250820-100957 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull devparagiri/Testing-20250820-100957:Q8_0

Run and chat with the model

lemonade run user.Testing-20250820-100957-Q8_0

List all available models

lemonade list

Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit AutoTrain.

Usage


from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "PATH_TO_THIS_REPO"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()

# Prompt content: "hi"
messages = [
    {"role": "user", "content": "hi"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

# Model response: "Hello! How can I assist you today?"
print(response)

Downloads last month: 11

Safetensors

Model size

1B params

Tensor type

F16

Model tree for devparagiri/Testing-20250820-100957

Base model

meta-llama/Llama-3.2-1B-Instruct

Quantized

(375)

this model

devparagiri
/

Testing-20250820-100957

Model Trained Using AutoTrain

Usage

Model tree for devparagiri/Testing-20250820-100957

Dataset used to train devparagiri/Testing-20250820-100957