Update README.md

README.md

@@ -4,117 +4,20 @@ license_name: qwen2
 license_link: https://huggingface.co/Qwen/Qwen2-72B/blob/main/LICENSE
 ---
 
-# Tess-v2.5.2 (Qwen2-72B)
+Quantized model => https://huggingface.co/migtissera/Tess-v2.5.2-Qwen2-72B
 
-![Tess-v2.5](https://huggingface.co/migtissera/Tess-v2.5-Qwen2-72B/resolve/main/Tess-v2.5.png)
+**Quantization Details:**  
+Quantization is done using turboderp's ExLlamaV2 v0.1.8. 
 
-# Update: 
+I use the default calibration datasets and arguments. The repo also includes a "measurement.json" file, which was used during the quantization process.
 
-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.
+For models with bits per weight (BPW) over 6.0, I default to quantizing the `lm_head` layer at 8 bits instead of the standard 6 bits.
 
-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. 
+**Who are you? What's with these weird BPWs on [insert model here]?**  
+I specialize in optimized EXL2 quantization for models in the 70B to 100B+ range, specifically tailored for 48GB VRAM setups. My rig is built using 2 x 3090s with a Ryzen APU (APU used solely for desktop output—no VRAM wasted on the 3090s). I use TabbyAPI for inference, targeting context sizes between 32K and 64K.
 
-Exercise caution and cross-check information when necessary. This is an uncensored model.
+Every model I upload includes a `config.yml` file with my ideal TabbyAPI settings. If you're using my config, don’t forget to set `PYTORCH_CUDA_ALLOC_CONF=backend:cudaMallocAsync` to save some VRAM.