Instructions to use RichardErkhov/Pinchao_-_ChatBot_NFR-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/Pinchao_-_ChatBot_NFR-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RichardErkhov/Pinchao_-_ChatBot_NFR-gguf",
	filename="ChatBot_NFR.IQ3_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use RichardErkhov/Pinchao_-_ChatBot_NFR-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 RichardErkhov/Pinchao_-_ChatBot_NFR-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/Pinchao_-_ChatBot_NFR-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RichardErkhov/Pinchao_-_ChatBot_NFR-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/Pinchao_-_ChatBot_NFR-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 RichardErkhov/Pinchao_-_ChatBot_NFR-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/Pinchao_-_ChatBot_NFR-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 RichardErkhov/Pinchao_-_ChatBot_NFR-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/Pinchao_-_ChatBot_NFR-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/Pinchao_-_ChatBot_NFR-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/Pinchao_-_ChatBot_NFR-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/Pinchao_-_ChatBot_NFR-gguf:Q4_K_M
```

Unsloth Studio

How to use RichardErkhov/Pinchao_-_ChatBot_NFR-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 RichardErkhov/Pinchao_-_ChatBot_NFR-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 RichardErkhov/Pinchao_-_ChatBot_NFR-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RichardErkhov/Pinchao_-_ChatBot_NFR-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use RichardErkhov/Pinchao_-_ChatBot_NFR-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/Pinchao_-_ChatBot_NFR-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/Pinchao_-_ChatBot_NFR-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/Pinchao_-_ChatBot_NFR-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Pinchao_-_ChatBot_NFR-gguf-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

ChatBot_NFR - GGUF

Model creator: https://huggingface.co/Pinchao/
Original model: https://huggingface.co/Pinchao/ChatBot_NFR/

Name	Quant method	Size
ChatBot_NFR.Q2_K.gguf	Q2_K	1.32GB
ChatBot_NFR.IQ3_XS.gguf	IQ3_XS	1.51GB
ChatBot_NFR.IQ3_S.gguf	IQ3_S	1.57GB
ChatBot_NFR.Q3_K_S.gguf	Q3_K_S	1.57GB
ChatBot_NFR.IQ3_M.gguf	IQ3_M	1.73GB
ChatBot_NFR.Q3_K.gguf	Q3_K	1.82GB
ChatBot_NFR.Q3_K_M.gguf	Q3_K_M	1.82GB
ChatBot_NFR.Q3_K_L.gguf	Q3_K_L	1.94GB
ChatBot_NFR.IQ4_XS.gguf	IQ4_XS	1.93GB
ChatBot_NFR.Q4_0.gguf	Q4_0	2.03GB
ChatBot_NFR.IQ4_NL.gguf	IQ4_NL	2.04GB
ChatBot_NFR.Q4_K_S.gguf	Q4_K_S	2.04GB
ChatBot_NFR.Q4_K.gguf	Q4_K	2.23GB
ChatBot_NFR.Q4_K_M.gguf	Q4_K_M	2.23GB
ChatBot_NFR.Q4_1.gguf	Q4_1	2.24GB
ChatBot_NFR.Q5_0.gguf	Q5_0	2.46GB
ChatBot_NFR.Q5_K_S.gguf	Q5_K_S	2.46GB
ChatBot_NFR.Q5_K.gguf	Q5_K	2.62GB
ChatBot_NFR.Q5_K_M.gguf	Q5_K_M	2.62GB
ChatBot_NFR.Q5_1.gguf	Q5_1	2.68GB
ChatBot_NFR.Q6_K.gguf	Q6_K	2.92GB
ChatBot_NFR.Q8_0.gguf	Q8_0	3.78GB

Original model description:

tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: microsoft/Phi-3-mini-4k-instruct widget: - messages: - role: user content: What is your favorite condiment? license: apache-2.0 language: - es - en datasets: - Pinchao/ChatBot_NFR

Model Trained Using AutoTrain

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

Usage


from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "Pinchao/ChatBot_NFR"

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: 39

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

2-bit

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4-bit

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6-bit

8-bit

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