Instructions to use ayeshaNoor1/Llama_finetunedModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ayeshaNoor1/Llama_finetunedModel with Transformers:

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

pipe = pipeline("text-generation", model="ayeshaNoor1/Llama_finetunedModel")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ayeshaNoor1/Llama_finetunedModel", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ayeshaNoor1/Llama_finetunedModel with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ayeshaNoor1/Llama_finetunedModel"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ayeshaNoor1/Llama_finetunedModel",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ayeshaNoor1/Llama_finetunedModel

SGLang

How to use ayeshaNoor1/Llama_finetunedModel 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 "ayeshaNoor1/Llama_finetunedModel" \
    --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": "ayeshaNoor1/Llama_finetunedModel",
		"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 "ayeshaNoor1/Llama_finetunedModel" \
        --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": "ayeshaNoor1/Llama_finetunedModel",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use ayeshaNoor1/Llama_finetunedModel 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 ayeshaNoor1/Llama_finetunedModel 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 ayeshaNoor1/Llama_finetunedModel to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ayeshaNoor1/Llama_finetunedModel to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="ayeshaNoor1/Llama_finetunedModel",
    max_seq_length=2048,
)

Docker Model Runner
How to use ayeshaNoor1/Llama_finetunedModel with Docker Model Runner:
```
docker model run hf.co/ayeshaNoor1/Llama_finetunedModel
```

Model Card for Model ID

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

This model has been fine-tuned for use in a chatbot aimed at mental well-being support. It is designed to offer empathetic, supportive responses to users' mental health inquiries. A combined dataset was created by merging three relevant datasets for training to enhance the model’s ability to understand and respond appropriately in counseling scenarios.

Model Name: Llama_finetunedModel
Developed by: Ayesha Noor
Model type: Language model for conversational AI
Language(s) (NLP): English
Finetuned model: https://huggingface.co/ayeshaNoor1/Llama_finetunedModel

Model Sources

Repository: https://huggingface.co/ayeshaNoor1

Uses

Direct Use

Intended for mental health chatbot applications, particularly for providing initial support, resources, and empathetic responses in mental well-being conversations.

Downstream Use

May be used as part of broader mental health support applications, integrated into platforms aimed at user well-being.

Out-of-Scope Use

Not recommended for critical mental health assessments, as it is not a replacement for professional help. Avoid using for high-stakes decision-making without appropriate oversight.

Recommendations

Users should be aware of the limitations in handling diverse mental health needs and sensitive conversations. Professional oversight is advised when using in serious or emergency mental health contexts.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ayeshaNoor1/Llama_finetunedModel")
model = AutoModelForCausalLM.from_pretrained("ayeshaNoor1/Llama_finetunedModel")

# Sample input text
input_text = "I'm feeling really down lately. Can you help me?"

# Tokenize and generate response
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)

# Decode and print the response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Details

Training Data

A single dataset was created by merging:

First Dataset: - https://huggingface.co/datasets/marmikpandya/mental-health
Second Dataset: - https://huggingface.co/datasets/Amod/mental_health_counseling_conversations
Third Dataset: - https://huggingface.co/datasets/AdithyaSK/CompanionLLama_instruction_30k

Training Procedure

Preprocessing

Data was preprocessed to ensure consistency in format, relevance to mental health support, and removal of any sensitive or personal identifiers.

Summary

The model demonstrated proficiency in providing supportive responses in well-being conversations.

Technical Specifications

Compute Infrastructure

Software

Libraries: transformers, datasets, torch, pandas, trl, unsloth
Framework: PyTorch

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for ayeshaNoor1/Llama_finetunedModel

Base model

meta-llama/Llama-3.2-3B-Instruct

Finetuned

unsloth/Llama-3.2-3B-Instruct