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README.md

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+
+---
+
+library_name: transformers
+license: apache-2.0
+tags:
+- autotrain
+- text-generation-inference
+- text-generation
+- peft
+- generated_from_trainer
+- mistral
+- transformers
+- Inference Endpoints
+- pytorch
+base_model: mistralai/Mistral-7B-Instruct-v0.2
+model-index:
+- name: Mental-Health_ML
+  results: []
+datasets:
+- Amod/mental_health_counseling_conversations
+inference: true
+widget:
+- messages:
+  - role: user
+    content: What is your favorite condiment?
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF
+This is quantized version of [prabureddy/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2](https://huggingface.co/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](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the [mental_health_counseling_conversations](https://huggingface.co/datasets/Amod/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)
+```