metadata
license: llama3
model-index:
- name: Llama-3-8B-Synthia-v3.5
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 50.7
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=migtissera/Llama-3-8B-Synthia-v3.5
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 27.54
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=migtissera/Llama-3-8B-Synthia-v3.5
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 5.06
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=migtissera/Llama-3-8B-Synthia-v3.5
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 2.91
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=migtissera/Llama-3-8B-Synthia-v3.5
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 9.41
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=migtissera/Llama-3-8B-Synthia-v3.5
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 22.56
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=migtissera/Llama-3-8B-Synthia-v3.5
name: Open LLM Leaderboard
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.
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.
| Task | Metric | Value |
| arc_challenge | acc_norm | |
| hellaswag | acc_norm | |
| mmlu | acc_norm | |
| truthfulqa_mc | mc2 | |
| Total Average | - |
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")
Join My General AI Discord (NeuroLattice):
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
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 19.70 |
| IFEval (0-Shot) | 50.70 |
| BBH (3-Shot) | 27.54 |
| MATH Lvl 5 (4-Shot) | 5.06 |
| GPQA (0-shot) | 2.91 |
| MuSR (0-shot) | 9.41 |
| MMLU-PRO (5-shot) | 22.56 |