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---
license: other
license_name: qwen2
license_link: https://huggingface.co/Qwen/Qwen2-72B/blob/main/LICENSE
model-index:
- name: Tess-v2.5.2-Qwen2-72B
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: 44.94
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=migtissera/Tess-v2.5.2-Qwen2-72B
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: 52.31
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=migtissera/Tess-v2.5.2-Qwen2-72B
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: 27.42
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=migtissera/Tess-v2.5.2-Qwen2-72B
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: 13.42
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=migtissera/Tess-v2.5.2-Qwen2-72B
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: 10.89
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=migtissera/Tess-v2.5.2-Qwen2-72B
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: 50.68
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=migtissera/Tess-v2.5.2-Qwen2-72B
name: Open LLM Leaderboard
---
# Tess-v2.5.2 (Qwen2-72B)
# Update:
This is a fine-tune over the Tess-v2.5, with a changed learning rate and a subset of Tess-v2.5 dataset. The model is completely different to Tess-v2.5.
I was testing a new feature with the Tess-v2.5 dataset. If you had used the model, you might have noticed that the model generations sometimes would end up with a follow-up question. This is intentional, and was created to provide more of a "natural" conversation.
What had happened earlier was that the stop token wasn't getting properly generated, so the model would go on to answer its own question.
This is fixed in Tess-v2.5.2. The model would still ask you follow-up questions, but the stop tokens are getting properly generated. If you'd like to not have the follow-up questions feature, just add the following to your system prompt: "No follow-up questions necessary".
# Tess-v2.5.2 (Qwen2-72B)
We've created Tess-v2.5.2, the latest state-of-the-art model in the Tess series of Large Language Models (LLMs). Tess, short for Tesoro (<em>Treasure</em> in Italian), is the flagship LLM series created by Migel Tissera. Tess-v2.5.2 brings significant improvements in reasoning capabilities, coding capabilities and mathematics. It is currently the #1 ranked open weight model when evaluated on MMLU (Massive Multitask Language Understanding). It scores higher than all other open weight models including Qwen2-72B-Instruct, Llama3-70B-Instruct, Mixtral-8x22B-Instruct and DBRX-Instruct. Further, when evaluated on MMLU, Tess-v2.5.2 (Qwen2-72B) model outperforms even the frontier closed models Gemini-1.0-Ultra, Gemini-1.5-Pro, Mistral-Large and Claude-3-Sonnet.
Tess-v2.5.2 (Qwen2-72B) was fine-tuned over the newly released Qwen2-72B base, using the Tess-v2.5 dataset that contain 300K samples spanning multiple topics, including business and management, marketing, history, social sciences, arts, STEM subjects and computer programming. This dataset was synthetically generated using the [Sensei](https://github.com/migtissera/Sensei) framework, using multiple frontier models such as GPT-4-Turbo, Claude-Opus and Mistral-Large.
The compute for this model was generously sponsored by [KindoAI](https://kindo.ai).
When evaluated on a subset of AGIEval (Nous), this model compares very well with the godfather GPT-4-0314 model as well.
# Training Process
Tess-v2.5.2 model was initiated with the base weights of Qwen2-72B. It was then fine-tuned with the Tess-v2.5 dataset, using Axolotl as the training framework. Most of Tess models follow a common fine-tuning methodology: low learning rates, low number of epochs, and uses very high quality and diverse data. This model was fine-tuned on a 4xA100 VM on Microsoft Azure for 4 days. The model has not been aligned with RLHF or DPO.
The author believes that model's capabilities seem to come primariliy from the pre-training process. This is the foundation for every fine-tune of Tess models, and preserving the entropy of the base models is of paramount to the author.
# Sample code to run inference
Note that this model uses ChatML prompt format.
```python
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria
model_path = "migtissera/Tess-v2.5.2-Qwen2-72B"
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=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
terminators = [
tokenizer.convert_tokens_to_ids("<|im_end|>")
]
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,
eos_token_id=terminators,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
return f"{string}"
conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""
while True:
user_input = input("You: ")
llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}\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__Tess-v2.5.2-Qwen2-72B)
| Metric |Value|
|-------------------|----:|
|Avg. |33.28|
|IFEval (0-Shot) |44.94|
|BBH (3-Shot) |52.31|
|MATH Lvl 5 (4-Shot)|27.42|
|GPQA (0-shot) |13.42|
|MuSR (0-shot) |10.89|
|MMLU-PRO (5-shot) |50.68|
|