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Model: migtissera/Synthia-7B-v3.0
Source: Original Platform

README.md

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+---
+license: apache-2.0
+---
+
+# Synthia-7B-v3.0
+SynthIA-7B-v3.0 (Synthetic Intelligent Agent) is a Mistral-7B model trained with guidance on Orca-2 paper. It has been fine-tuned for instruction following as well as having long-form conversations. SynthIA-3.0 dataset contains the Generarized Tree-of-Thought prompt plus 10 more new long-form system contexts. However, in the training phase the system context was removed as suggested in Orca-2 paper.
+
+<br>
+
+![Synthia](https://huggingface.co/migtissera/Synthia-7B-v3.0/resolve/main/Synthia-v3.jpg)
+
+<br>
+
+
+To evoke generalized Tree of Thought + Chain of Thought reasoning, you may use the following system message:
+```
+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.
+```
+
+
+## Evaluation
+
+We evaluated Synthia-7B-v3.0 on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. 
+
+Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). Section to follow.
+
+||||
+|:------:|:--------:|:-------:|
+|**Task**|**Metric**|**Value**|
+|*arc_challenge*|acc_norm||
+|*hellaswag*|acc_norm||
+|*mmlu*|acc_norm||
+|*truthfulqa_mc*|mc2||
+|**Total Average**|-|||
+
+<br>
+
+## Example Usage
+
+### Here is 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 difference between an Orca, Dolphin and a Seal?
+ASSISTANT:
+```
+
+### Below shows a code example on how to use this model:
+
+```python
+import torch, json
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_path = "migtissera/Synthia-7B-v3.0"
+output_file_path = "./Synthia-7B-conversations.jsonl"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    load_in_8bit=False,
+    trust_remote_code=True,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+
+def generate_text(instruction):
+    tokens = 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 = 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,
+        )
+    output = rest[0][length:]
+    string = tokenizer.decode(output, skip_special_tokens=True)
+    answer = string.split("USER:")[0].strip()
+    return f"{answer}"
+
+
+conversation = f"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."
+
+
+while True:
+    user_input = input("You: ")
+    llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
+    answer = generate_text(llm_prompt)
+    print(answer)
+    conversation = f"{llm_prompt}{answer}"
+    json_data = {"prompt": user_input, "answer": answer}
+
+    ## Save your conversation
+    with open(output_file_path, "a") as output_file:
+        output_file.write(json.dumps(json_data) + "\n")
+
+```
+
+<br>
+
+#### Limitations & Biases:
+
+While this model aims for accuracy, it can occasionally produce inaccurate or misleading results. 
+
+Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content. 
+
+Exercise caution and cross-check information when necessary. This is an uncensored model.
+
+
+<br>
+