# Llama-3-70B-Synthia-v3.5 Llama-3-70B-Synthia-v3.5 (Synthetic Intelligent Agent) is a general purpose Large Language Model (LLM). It was trained on the Synthia-v3.5 dataset that contains the varied system contexts, plus some other publicly available datasets. It has been fine-tuned for instruction following as well as having long-form conversations. Compute for Llama-3-70B-Synthia-v3.5 was sponsored by [KindoAI](https://kindo.ai/).
![Synthia](https://huggingface.co/migtissera/Llama-3-8B-Synthia-v3.5/resolve/main/Synthia-3.5.jpg)
## Evaluation We evaluated Llama-3-70B-Synthia-v3.5 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**|-|||
# Sample code to run inference ```python import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "/home/migel/Llama-3-70B-Synthia-v3.5" output_file_path = "/home/migel/conversations.jsonl" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_4bit=False, trust_remote_code=False, ) 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, pad_token_id=tokenizer.eos_token_id, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) return f"{string}" conversation = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are Synthia, a helful, female AI assitant. You always provide detailed answers without hesitation.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n""" while True: user_input = input("You: ") llm_prompt = f"{conversation}{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" answer = generate_text(llm_prompt) print(answer) conversation = f"{llm_prompt}{answer}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n" json_data = {"prompt": user_input, "answer": answer} with open(output_file_path, "a") as output_file: output_file.write(json.dumps(json_data) + "\n") ``` # Join My General AI Discord (NeuroLattice): https://discord.gg/Hz6GrwGFKD # 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. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Llama-3-70B-Synthia-v3.5) | Metric |Value| |-------------------|----:| |Avg. |35.20| |IFEval (0-Shot) |60.76| |BBH (3-Shot) |49.12| |MATH Lvl 5 (4-Shot)|18.96| |GPQA (0-shot) |18.34| |MuSR (0-shot) |23.39| |MMLU-PRO (5-shot) |40.65|