Instructions to use nevril/C2_CounselorChat_llama3.1_8B_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nevril/C2_CounselorChat_llama3.1_8B_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nevril/C2_CounselorChat_llama3.1_8B_GGUF",
	filename="C2_llama31_8B_Q8_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use nevril/C2_CounselorChat_llama3.1_8B_GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nevril/C2_CounselorChat_llama3.1_8B_GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf nevril/C2_CounselorChat_llama3.1_8B_GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nevril/C2_CounselorChat_llama3.1_8B_GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf nevril/C2_CounselorChat_llama3.1_8B_GGUF: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 nevril/C2_CounselorChat_llama3.1_8B_GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf nevril/C2_CounselorChat_llama3.1_8B_GGUF: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 nevril/C2_CounselorChat_llama3.1_8B_GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nevril/C2_CounselorChat_llama3.1_8B_GGUF:Q8_0

Use Docker

docker model run hf.co/nevril/C2_CounselorChat_llama3.1_8B_GGUF:Q8_0

LM Studio
Jan
Ollama
How to use nevril/C2_CounselorChat_llama3.1_8B_GGUF with Ollama:
```
ollama run hf.co/nevril/C2_CounselorChat_llama3.1_8B_GGUF:Q8_0
```

Unsloth Studio new

How to use nevril/C2_CounselorChat_llama3.1_8B_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 nevril/C2_CounselorChat_llama3.1_8B_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 nevril/C2_CounselorChat_llama3.1_8B_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nevril/C2_CounselorChat_llama3.1_8B_GGUF to start chatting

Pi new

How to use nevril/C2_CounselorChat_llama3.1_8B_GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nevril/C2_CounselorChat_llama3.1_8B_GGUF: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": "nevril/C2_CounselorChat_llama3.1_8B_GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nevril/C2_CounselorChat_llama3.1_8B_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 nevril/C2_CounselorChat_llama3.1_8B_GGUF: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 nevril/C2_CounselorChat_llama3.1_8B_GGUF:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use nevril/C2_CounselorChat_llama3.1_8B_GGUF with Docker Model Runner:
```
docker model run hf.co/nevril/C2_CounselorChat_llama3.1_8B_GGUF:Q8_0
```

Lemonade

How to use nevril/C2_CounselorChat_llama3.1_8B_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nevril/C2_CounselorChat_llama3.1_8B_GGUF:Q8_0

Run and chat with the model

lemonade run user.C2_CounselorChat_llama3.1_8B_GGUF-Q8_0

List all available models

lemonade list

CounselorChat (C2) — A Mental Health Conversational Assistant

Overview

CounselorChat (C2) is a fine-tuned version of the Llama 3.1 8B model, designed as an experimental chatbot to offer quick mental health tips and support. This model is not a substitute for professional mental health care and should not replace consultation with a licensed specialist.

The name CounselorChat is not the best but it was suggested by the model itself during testing so... you know...

Features

Base Model: Llama 3.1 8B.
Training Process:
- Datasets: Fine-tuned on a combination of:
  - CalebE/new_mental_health_conversations_all1
  - Amod/mental_health_counseling_conversations
- Method: One epoch of training using Unsloth at 16-bit precision.
- Data Cleanup: Deduplication and cleaning were performed to enhance the quality of training data.

Deployment Options

GGUF Quantized Model: Published as an 8-bit GGUF quantized model. Other quantization levels are available upon request.
Modelfile for Ollama: A pre-configured Modelfile for deployment on Ollama is also included, with a default context length of 8192 tokens.
- To deploy, update the PATH_TO_MODEL at the beginning of the Modelfile to point to the GGUF file location.
- Then, run the following CLI command from the same directory as the Modelfile:
```
ollama create C2_llama3.1_8B_q8x8K --file OllamaModelfile_C2_llama31_8B_Q8x8K
```
Other Deployment Options: For alternative deployment setups or platforms, it is recommended to set the SYSTEM prompt to:

You are an AI counselor designed to provide compassionate, evidence-based mental health support. Offer helpful, non-judgmental guidance focusing on validated techniques. Avoid diagnosing conditions, or speculative or unsafe advice.

Intended Use

CounselorChat is designed to provide quick, supportive mental health tips. It can be used for:

Offering general advice for stress management, mindfulness, and coping strategies.
Engaging in non-clinical conversations.

Disclaimer: This model is an experimental tool. It is not licensed or intended to provide clinical counseling or medical advice. For serious mental health concerns, please seek the assistance of a trained mental health professional.

Limitations

The model is not equipped to handle emergencies or complex mental health cases.
Responses are generated based on the training data and may lack the nuance of human judgment.
While efforts have been made to clean and deduplicate the datasets, unintended biases or inaccuracies may persist.

Downloads last month: 3

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for nevril/C2_CounselorChat_llama3.1_8B_GGUF

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

unsloth/Meta-Llama-3.1-8B-Instruct

Quantized

(79)

this model

nevril
/

C2_CounselorChat_llama3.1_8B_GGUF