Instructions to use vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF with Transformers:

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

pipe = pipeline("question-answering", model="vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF", device_map="auto")

llama-cpp-python

How to use vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF",
	filename="finetuned-16b.gguf",
)

llm.create_chat_completion(
	messages = "{\n    \"question\": \"What is my name?\",\n    \"context\": \"My name is Clara and I live in Berkeley.\"\n}"
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF
# Run inference directly in the terminal:
llama cli -hf vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF
# Run inference directly in the terminal:
llama cli -hf vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF

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 vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF
# Run inference directly in the terminal:
./llama-cli -hf vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF

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 vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF

Use Docker

docker model run hf.co/vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF

LM Studio
Jan
Ollama
How to use vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF with Ollama:
```
ollama run hf.co/vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF
```

Unsloth Studio

How to use vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-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 vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-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 vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF with Docker Model Runner:
```
docker model run hf.co/vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF
```

Lemonade

How to use vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF

Run and chat with the model

lemonade run user.Llama-2-7b-chat-hf-finedtuned-to-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Model Card for vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF

This model is a fine-tuned version of Llama-2-Chat-7b on company-specific question-answers data. It is designed for efficient performance while maintaining high-quality output, suitable for conversational AI applications.

Full Tutorial on Cheap Finetuning

https://github.com/VishanOberoi/FineTuningForTheGPUPoor?tab=readme-ov-file

Model Details

It was finetuned using QLORA and PEFT. After fine-tuning, the adapters were merged with the base model and then quantized to GGUF.

Developed by: Vishan Oberoi and Dev Chandan.
Model type: Transformer-based Large Language Model
Language(s) (NLP): English
License: MIT
Finetuned from model: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf

Model Sources

Repository: vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF
Links:
- LLaMA: LLaMA Paper
- QLORA: QLORA Paper
- llama.cpp: llama.cpp Paper/Documentation

Uses

This model is optimized for direct use in conversational AI, particularly for generating responses based on company-specific data. It can be utilized effectively in customer service bots, FAQ bots, and other applications where accurate and contextually relevant answers are required.

Example with `ctransformers`:

from ctransformers import AutoModelForCausalLM, AutoTokenizer

llm = AutoModelForCausalLM.from_pretrained("vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF", model_file="finetuned.gguf", model_type="llama", gpu_layers = 50, max_new_tokens = 2000, temperature = 0.2, top_k = 40, top_p = 0.6, context_length = 6000)

system_prompt = "<<SYS>>You are a useful bot... <</SYS>>"

user_prompt = "Tell me about your company"

Combine system prompt with user prompt

full_prompt = f"{system_prompt}\n[INST]{user_prompt}[/INST]"

Generate the response

response = llm(full_prompt)

Print the response

print(response)

Downloads last month: 60

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Papers for vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF

QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 62

LLaMA: Open and Efficient Foundation Language Models

Paper • 2302.13971 • Published Feb 27, 2023 • 25