Llama-3-8B-Synthia-v3.5

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.

It has been fine-tuned for instruction following as well as having long-form conversations.

Compute for Llama-3-8B-Synthia-v3.5 was sponsored by KindoAI.

👁 Synthia

Evaluation

We evaluated Llama-3-8B-Synthia-v3.5 on a wide range of tasks using Language Model Evaluation Harness from EleutherAI.

Here are the results on metrics used by HuggingFaceH4 Open LLM Leaderboard. Section to follow.

Sample code to run inference

import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "/home/migel/Tess-2.0-Llama-3-8B"
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")

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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.