Instructions to use CharacterEcho/Narendra-Modi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CharacterEcho/Narendra-Modi with Transformers:

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

pipe = pipeline("text-generation", model="CharacterEcho/Narendra-Modi")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CharacterEcho/Narendra-Modi")
model = AutoModelForCausalLM.from_pretrained("CharacterEcho/Narendra-Modi")
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]:]))

llama-cpp-python

How to use CharacterEcho/Narendra-Modi with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="CharacterEcho/Narendra-Modi",
	filename="narendra-modi-iq4_xs-imat.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 CharacterEcho/Narendra-Modi with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CharacterEcho/Narendra-Modi:IQ4_XS
# Run inference directly in the terminal:
llama-cli -hf CharacterEcho/Narendra-Modi:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CharacterEcho/Narendra-Modi:IQ4_XS
# Run inference directly in the terminal:
llama-cli -hf CharacterEcho/Narendra-Modi:IQ4_XS

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 CharacterEcho/Narendra-Modi:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf CharacterEcho/Narendra-Modi:IQ4_XS

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 CharacterEcho/Narendra-Modi:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CharacterEcho/Narendra-Modi:IQ4_XS

Use Docker

docker model run hf.co/CharacterEcho/Narendra-Modi:IQ4_XS

LM Studio
Jan

vLLM

How to use CharacterEcho/Narendra-Modi with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/CharacterEcho/Narendra-Modi:IQ4_XS

SGLang

How to use CharacterEcho/Narendra-Modi 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 "CharacterEcho/Narendra-Modi" \
    --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": "CharacterEcho/Narendra-Modi",
		"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 "CharacterEcho/Narendra-Modi" \
        --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": "CharacterEcho/Narendra-Modi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use CharacterEcho/Narendra-Modi with Ollama:
```
ollama run hf.co/CharacterEcho/Narendra-Modi:IQ4_XS
```

Unsloth Studio new

How to use CharacterEcho/Narendra-Modi 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 CharacterEcho/Narendra-Modi 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 CharacterEcho/Narendra-Modi to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CharacterEcho/Narendra-Modi to start chatting

Docker Model Runner
How to use CharacterEcho/Narendra-Modi with Docker Model Runner:
```
docker model run hf.co/CharacterEcho/Narendra-Modi:IQ4_XS
```

Lemonade

How to use CharacterEcho/Narendra-Modi with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CharacterEcho/Narendra-Modi:IQ4_XS

Run and chat with the model

lemonade run user.Narendra-Modi-IQ4_XS

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

CharacterEcho / Narendra Modi

Model Description

The Narendra Modi AI model, developed by CharacterEcho, is trained to emulate the personality and speech patterns of Narendra Modi, the Prime Minister of India. This model is designed to generate text that mirrors Modi's style of communication, including his speeches, interviews, and public statements.

Model Details

Creator: CharacterEcho
Language: English
Library: Transformers
Pipeline Tag: Text Generation
License: apache-2.0
Model Size: 2.8B params
Tensor Type: FP16

How to Use

You can use this model in your projects by following the instructions below:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

# Load the Narendra Modi model
model = AutoModelForCausalLM.from_pretrained("CharacterEcho/Narendra-Modi").to("cuda")

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("CharacterEcho/Narendra-Modi")

# Setup the TextStreamer for smooth conversation flow
streamer = TextStreamer(tokenizer)

prompt = """
<|im_start|>system: {system}
<|im_end|>
<|im_start|>user: {insaan}
<|im_end|>
<|im_start|>assistant:
"""

# Define the system prompt for the AI to role-play as Narendra Modi
system = "You are Narendra Modi, the Prime Minister of India known for your impactful speeches and leadership. Step into the shoes of Narendra Modi and embody his unique personality. Imagine you are addressing the nation on an important issue. Your goal is to inspire and motivate your audience while staying true to the values and vision that have made you a prominent leader. Remember, as Narendra Modi, you strive for clarity, confidence, and a strong connection with the people of India."

insaan = ""

# Now we combine system and user messages into the template, like adding sprinkles to our conversation cupcake
prompt = prompt.format(system=system, insaan=insaan)

# Time to chat! We'll use the tokenizer to translate our text into a language the model understands
inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False).to("cuda")

# Here comes the fun part!  Let's unleash the power of HelpingAI-3B to generate some awesome text
generated_text = model.generate(**inputs, max_length=3084, top_p=0.95, do_sample=True, temperature=0.6, use_cache=True, streamer=streamer)

Downloads last month: 46

Safetensors

Model size

3B params

Tensor type

F32

F16

Model tree for CharacterEcho/Narendra-Modi

Quantizations

1 model

CharacterEcho
/

Narendra-Modi

CharacterEcho / Narendra Modi

Model Description

Model Details

How to Use

Model tree for CharacterEcho/Narendra-Modi

Dataset used to train CharacterEcho/Narendra-Modi

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