115 lines
3.6 KiB
Markdown
115 lines
3.6 KiB
Markdown
---
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license: llama3
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---
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# Llama-3-8B-Synthia-v3.5
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Llama-3-8B-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.
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It has been fine-tuned for instruction following as well as having long-form conversations.
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Compute for Llama-3-8B-Synthia-v3.5 was sponsored by [KindoAI](https://kindo.ai/).
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<br>
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<br>
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## Evaluation
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We evaluated Llama-3-8B-Synthia-v3.5 on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI.
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Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). Section to follow.
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|:------:|:--------:|:-------:|
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|**Task**|**Metric**|**Value**|
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|*arc_challenge*|acc_norm||
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|*hellaswag*|acc_norm||
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|*mmlu*|acc_norm||
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|*truthfulqa_mc*|mc2||
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|**Total Average**|-|||
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<br>
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# Sample code to run inference
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```python
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import torch, json
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_path = "/home/migel/Tess-2.0-Llama-3-8B"
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output_file_path = "/home/migel/conversations.jsonl"
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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torch_dtype=torch.float16,
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device_map="auto",
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load_in_4bit=False,
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trust_remote_code=False,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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def generate_text(instruction):
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tokens = tokenizer.encode(instruction)
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tokens = torch.LongTensor(tokens).unsqueeze(0)
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tokens = tokens.to("cuda")
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instance = {
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"input_ids": tokens,
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"top_p": 1.0,
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"temperature": 0.75,
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"generate_len": 1024,
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"top_k": 50,
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}
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length = len(tokens[0])
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with torch.no_grad():
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rest = model.generate(
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input_ids=tokens,
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max_length=length + instance["generate_len"],
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use_cache=True,
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do_sample=True,
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top_p=instance["top_p"],
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temperature=instance["temperature"],
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top_k=instance["top_k"],
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num_return_sequences=1,
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pad_token_id=tokenizer.eos_token_id,
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)
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output = rest[0][length:]
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string = tokenizer.decode(output, skip_special_tokens=True)
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return f"{string}"
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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"""
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while True:
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user_input = input("You: ")
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llm_prompt = f"{conversation}{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
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answer = generate_text(llm_prompt)
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print(answer)
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conversation = f"{llm_prompt}{answer}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n"
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json_data = {"prompt": user_input, "answer": answer}
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with open(output_file_path, "a") as output_file:
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output_file.write(json.dumps(json_data) + "\n")
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```
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# Join My General AI Discord (NeuroLattice):
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https://discord.gg/Hz6GrwGFKD
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# Limitations & Biases:
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While this model aims for accuracy, it can occasionally produce inaccurate or misleading results.
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Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
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Exercise caution and cross-check information when necessary. This is an uncensored model.
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