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Model: shahzebnaveed/NeuralHermes-2.5-Mistral-7B
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: apache-2.0
+---
+
+# Model Card for NeuralHermes 2.5 - Mistral 7B
+
+
+NeuralHermes is based on the teknium/OpenHermes-2.5-Mistral-7B model that has been further fine-tuned with Direct Preference Optimization (DPO) using the Intel/orca_dpo_pairs dataset, reformatted with the ChatML template.
+
+It is directly inspired by the RLHF process described by Intel/neural-chat-7b-v3-1's authors to improve performance.
+
+
+**IMPORTANT**
+
+- This model was only run for 2 steps before GPU went out of memory. Hence, this is not completely fine-tuned with DPO.
+- Secondly, to make it run over a small GPU, I purposefully reduced the parameters (# of LORA adapters, alpha, etc.). The values are therefore not the ideal.
+
+
+
+## Uses
+
+You can use the following code to use this model:
+
+
+import transformers
+from transformers import AutoTokenizer
+
+# Format prompt
+message = [
+    {"role": "system", "content": "You are a helpful assistant chatbot."},
+    {"role": "user", "content": "What is a Large Language Model?"}
+]
+tokenizer = AutoTokenizer.from_pretrained(new_model)
+prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
+
+# Create pipeline
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=new_model,
+    tokenizer=tokenizer
+)
+
+# Generate text
+sequences = pipeline(
+    prompt,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+    num_return_sequences=1,
+    max_length=200,
+)
+print(sequences[0]['generated_text'])