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README.md

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 license: apache-2.0
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+
+# HelixNet-LMoE
+
+HelixNet-LMoE is a simple LoRA based Mixture of Experts version of the [HelixNet](https://huggingface.co/migtissera/HelixNet) 3-model system by [Migel Tissera](https://huggingface.co/migtissera).
+
+For each HelixNet model, a separate LoRA adapter was extracted :
+ * [HelixNet-LMoE-Actor](rhysjones/HelixNet-LMoE-Actor)
+ * [HelixNet-LMoE-Critic](rhysjones/HelixNet-LMoE-Critic)
+ * [HelixNet-LMoE-Regenerator](rhysjones/HelixNet-LMoE-Regenerator)
+
+These are then loaded togeter with the base [Mistral 7b](https://huggingface.co/mistralai/Mistral-7B-v0.1) model to give the combined LMoE model.
+
+As HelixNet processes its inputs using the actor, critic and regenerator actions, the corresponding LoRA adapter is dynamically enabled as required.
+
+It is similar in approach to [Airoboro's MoE implementation](https://github.com/jondurbin/airoboros/tree/main#lmoe) allowing GPU memory requirements in this (unquantized) instance to be reduced from 3 x 14GB to 1 x 14GB + 3 x 320MB.
+The LoRAs were extracted based on the process given in [https://github.com/uukuguy/multi_loras](https://github.com/uukuguy/multi_loras), with a Rank of 64 and an Alpha of 128.
+
+# Prompt format:
+
+```
+SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
+USER: What is the relationship between Earth's atmosphere, magnetic field and gravity?
+ASSISTANT:
+```
+# Example Usage
+
+The following is a code example on how to use HelixNet-LMoE. No special system-context messages are needed for the `critic` and the `regenerator`. \
+At the **You:** prompt, enter a questions such as _What is the relationship between Earth's atmosphere, magnetic field and gravity?_
+
+```python
+import torch, json
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+def load_model(model_path):
+    model = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        torch_dtype=torch.float16,
+        device_map="cuda",
+        load_in_4bit=False,
+        trust_remote_code=True,
+    )
+    return model
+
+def load_tokenizer(model_path):
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    return tokenizer
+
+def generate_text(instruction, adapter):
+    # Select our required LoRA adapter
+    adapter_model.set_adapter(adapter)
+    
+    tokens = base_tokenizer.encode(instruction)
+    tokens = torch.LongTensor(tokens).unsqueeze(0)
+    tokens = tokens.to("cuda")
+
+    instance = {
+        "input_ids": tokens,
+        "top_p": 1.0,
+        "temperature": 0.75,
+        "generate_len": 1024,
+        "top_k": 50,
+    }
+
+    length = len(tokens[0])
+    with torch.no_grad():
+        rest = adapter_model.generate(
+            input_ids=tokens,
+            max_length=length + instance["generate_len"],
+            use_cache=True,
+            do_sample=True,
+            top_p=instance["top_p"],
+            temperature=instance["temperature"],
+            top_k=instance["top_k"],
+            num_return_sequences=1,
+            pad_token_id=base_tokenizer.eos_token_id,
+        )
+    output = rest[0][length:]
+    string = base_tokenizer.decode(output, skip_special_tokens=True)
+    return f"{string}"
+
+# Load our base Mistral 7B model and tokenizer
+base_model = load_model("mistralai/Mistral-7B-v0.1")
+base_tokenizer = load_tokenizer("mistralai/Mistral-7B-v0.1")
+
+# Load in our three different LoRA adapters for the actor, critic and regenerator
+adapter_model = PeftModel.from_pretrained(base_model, "rhysjones/HelixNet-LMoE-Actor", "actor")
+adapter_model.load_adapter("rhysjones/HelixNet-LMoE-Critic", adapter_name="critic")
+adapter_model.load_adapter("rhysjones/HelixNet-LMoE-Regenerator", adapter_name="regenerator")
+
+system_prompt = "You are HelixNet. Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
+
+while True:
+    user_input = input("You: ")
+
+    prompt_actor = f"SYSTEM: {system_prompt} \nUSER: {user_input} \nASSISTANT: "
+    actor_response = generate_text(prompt_actor, "actor")
+    print(f"ACTOR: {actor_response}\n\n")
+
+    prompt_critic = f"SYSTEM: {system_prompt} \nUSER: {user_input} \nRESPONSE: {actor_response} \nCRITIQUE:"
+    critic_response = generate_text(prompt_critic, "critic")
+    print(f"CRITIQUE: {critic_response}\n\n")
+    
+    prompt_regenerator = f"SYSTEM: {system_prompt} \nUSER: {user_input} \nRESPONSE: {actor_response} \nCRITIQUE: {critic_response} \nREGENERATOR:"
+    regenerator_response = generate_text(prompt_regenerator, "regenerator")
+    print(f"REGENERATION: {regenerator_response}")
+
+```
+
+# LLM Evaluation
+
+Evaluation on a merged version of each base+lora models has yet to be done on the [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) to see how it compares to the equivalent full HelixNet model.
+
+# HelixNet Details
+
+HelixNet is a Deep Learning architecture consisting of 3 x Mistral-7B LLMs. It has an `actor`, a `critic`, and a `regenerator`. The `actor` LLM produces an initial response to a given system-context and a question. The `critic` then takes in as input, a tuple of (system-context, question, response) and provides a critique based on the provided answer to the given system-context and the question. Its job is not to criticize, but to provide an intelligent critique so that the answer can be modified/regenerated to address the question better. Finally, the `regenerator` takes in a tuple of (system-context, question, response, critique) and regenerates the answer.
+
+HelixNet is insprired from an actor-critic architecture most prominent in Reinforcement Learning algorithms. The name derives from Helix, referring to the spiral structure of a DNA molecule. It symbolizes the intertwined nature of the three networks, working in tandem, much like the strands of a DNA molecule.
+
+HelixNet regenerates very pleasing and accurate responses, due to the entropy preservation of the regenerator. The regenerator was only trained on a dataset of 1000 samples, similar to Meta's LIMA. The actor network here was trained on about 250K very high-quality samples, and the critic network was trained on further 10K samples.
+
+Full details on how HelixNet was trained and evaluated is located at [https://huggingface.co/migtissera/HelixNet](https://huggingface.co/migtissera/HelixNet)