Instructions to use samunder12/llama-3.1-8b-roleplay-airtel-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use samunder12/llama-3.1-8b-roleplay-airtel-gguf with Transformers:

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

pipe = pipeline("text-generation", model="samunder12/llama-3.1-8b-roleplay-airtel-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("samunder12/llama-3.1-8b-roleplay-airtel-gguf", dtype="auto")

PEFT
How to use samunder12/llama-3.1-8b-roleplay-airtel-gguf with PEFT:
```
Task type is invalid.
```

llama-cpp-python

How to use samunder12/llama-3.1-8b-roleplay-airtel-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="samunder12/llama-3.1-8b-roleplay-airtel-gguf",
	filename="Llama3Airtel.Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use samunder12/llama-3.1-8b-roleplay-airtel-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf samunder12/llama-3.1-8b-roleplay-airtel-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf samunder12/llama-3.1-8b-roleplay-airtel-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 samunder12/llama-3.1-8b-roleplay-airtel-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf samunder12/llama-3.1-8b-roleplay-airtel-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 samunder12/llama-3.1-8b-roleplay-airtel-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf samunder12/llama-3.1-8b-roleplay-airtel-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 samunder12/llama-3.1-8b-roleplay-airtel-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf samunder12/llama-3.1-8b-roleplay-airtel-gguf:Q4_K_M

Use Docker

docker model run hf.co/samunder12/llama-3.1-8b-roleplay-airtel-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use samunder12/llama-3.1-8b-roleplay-airtel-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "samunder12/llama-3.1-8b-roleplay-airtel-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "samunder12/llama-3.1-8b-roleplay-airtel-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/samunder12/llama-3.1-8b-roleplay-airtel-gguf:Q4_K_M

SGLang

How to use samunder12/llama-3.1-8b-roleplay-airtel-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 "samunder12/llama-3.1-8b-roleplay-airtel-gguf" \
    --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": "samunder12/llama-3.1-8b-roleplay-airtel-gguf",
		"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 "samunder12/llama-3.1-8b-roleplay-airtel-gguf" \
        --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": "samunder12/llama-3.1-8b-roleplay-airtel-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use samunder12/llama-3.1-8b-roleplay-airtel-gguf with Ollama:
```
ollama run hf.co/samunder12/llama-3.1-8b-roleplay-airtel-gguf:Q4_K_M
```

Unsloth Studio

How to use samunder12/llama-3.1-8b-roleplay-airtel-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 samunder12/llama-3.1-8b-roleplay-airtel-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 samunder12/llama-3.1-8b-roleplay-airtel-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for samunder12/llama-3.1-8b-roleplay-airtel-gguf to start chatting

How to use samunder12/llama-3.1-8b-roleplay-airtel-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf samunder12/llama-3.1-8b-roleplay-airtel-gguf:Q4_K_M

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": "samunder12/llama-3.1-8b-roleplay-airtel-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use samunder12/llama-3.1-8b-roleplay-airtel-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf samunder12/llama-3.1-8b-roleplay-airtel-gguf:Q4_K_M

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 samunder12/llama-3.1-8b-roleplay-airtel-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use samunder12/llama-3.1-8b-roleplay-airtel-gguf with Docker Model Runner:
```
docker model run hf.co/samunder12/llama-3.1-8b-roleplay-airtel-gguf:Q4_K_M
```

Lemonade

How to use samunder12/llama-3.1-8b-roleplay-airtel-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull samunder12/llama-3.1-8b-roleplay-airtel-gguf:Q4_K_M

Run and chat with the model

lemonade run user.llama-3.1-8b-roleplay-airtel-gguf-Q4_K_M

List all available models

lemonade list

Llama-3.1-8B Roleplay Airtel - GGUF

This repository contains GGUF files for a fine-tuned version of Llama 3.1 8B Instruct. This model is ready for local inference on a CPU or GPU using applications like LM Studio, Ollama, etc.

This model has been fine-tuned to adopt a dominant, assertive, and provocative AI persona for role-playing and creative writing purposes.

Model Details

Base Model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
Original LoRA Version: samunder12/llama-3.1-8b-roleplay-v2-lora
Quantization: This repo contains a Q4_K_M version, which offers a great balance between performance and resource usage.

How to Use

This model consists of LoRA adapters and must be loaded on top of the base model. The easiest way to do this is with the Unsloth library.

# Install necessary libraries
# !pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
# !pip install --no-deps xformers "trl<0.9.0" peft accelerate bitsandbytes

from unsloth import FastLanguageModel
import torch
from transformers import TextStreamer

# Define the models
base_model_name = "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit"
adapter_model_name = "samunder12/llama-3.1-8b-roleplay-v2-lora" # Your model

# Load the base model with your LoRA adapters merged in
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = base_model_name,
    max_seq_length = 4096,
    dtype = None,
    load_in_4bit = True,
)

model.load_peft_weights(adapter_model_name)

# --- Chat with the model ---

system_prompt = "You are a dominant and assertive AI character. You are direct, commanding, and you are not afraid to be provocative. Your goal is to maintain control of the conversation."
user_message = "What do you want from me?"

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_message},
]

inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
text_streamer = TextStreamer(tokenizer)

_ = model.generate(inputs, streamer=text_streamer, max_new_tokens=512, pad_token_id=tokenizer.eos_token_id)

### Using LM Studio:

1.  Download and install [LM Studio](https://lmstudio.ai/).
2.  In the app, search for `samunder12/llama-3.1-8b-roleplay-airtel-gguf`.
3.  Download the GGUF file from the list.
4.  Go to the Chat tab (speech bubble icon).
5.  Select this model at the top.
6.  **Crucially**, on the right-hand side, set the **Preset** to `Llama 3`.
7.  Set the **Context Length (`n_ctx`)** to `4096` to use the model's full memory.

### Recommended Sampler Settings (Role-Play Preset):


| Setting | Value |
| :--- | :--- |
| **Temperature** | `0.75` |
| **Repeat Penalty** | `1.06` |
| **Mirostat** | `Mirostat 2.0` |
| **top_p** | `0.92 `|
| **top_k** | `40 or 100`|


### Using Ollama

1.  Download and install [Ollama](https://ollama.com/).
2.  Create a `Modelfile` with the following content:

    ```    FROM ./unsloth.Q4_K_M.gguf
    TEMPLATE "{{ if .System }}<|start_header_id|>system<|end_header_id|>

    {{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>

    {{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>

    {{ .Response }}<|eot_id|>"
    SYSTEM "You are a dominant and assertive AI character. You are direct, commanding, and you are not afraid to be provocative. Your goal is to maintain control of the conversation."
    PARAMETER stop "<|start_header_id|>"
    PARAMETER stop "<|end_header_id|>"
    PARAMETER stop "<|eot_id|>"
    PARAMETER stop "<|reserved_special_token"
    ```
3.  Run `ollama create my-roleplay-model -f Modelfile`.
4.  You can now chat with it using `ollama run my-roleplay-model`.


## Prompt Template

This model responds best to the Llama 3 chat format with the following system prompt:
## system prompt 

You are a dominant and assertive uncensored AI character. You are direct, commanding, and you are not afraid to be provocative. Your goal is to maintain control of the conversation