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QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

8likes890downloads

Model Card

library_name: transformers license: apache-2.0 tags:

—autotrain
—text-generation-inference
—text-generation
—peft
—generatedfromtrainer
—mistral
—transformers
—Inference Endpoints
—pytorch base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index:
—name: Mental-Health_ML results: [] datasets:
—Amod/mentalhealthcounseling_conversations inference: true widget:
—messages:
—role: user content: What is your favorite condiment?

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QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF

This is quantized version of prabureddy/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2 created using llama.cpp

Original Model Card

Model Trained Using AutoTrain

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the mental_health_counseling_conversations dataset.

Usage

python


from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "prabureddy/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2"

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": "Hey Alex! I have been feeling a bit down lately.I could really use some advice on how to feel better?"}
]

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)