Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Tess-10.7B-v2.0 - GGUF - Model creator: https://huggingface.co/Joseph717171/ - Original model: https://huggingface.co/Joseph717171/Tess-10.7B-v2.0/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Tess-10.7B-v2.0.Q2_K.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q2_K.gguf) | Q2_K | 3.73GB | | [Tess-10.7B-v2.0.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.IQ3_XS.gguf) | IQ3_XS | 4.14GB | | [Tess-10.7B-v2.0.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.IQ3_S.gguf) | IQ3_S | 4.37GB | | [Tess-10.7B-v2.0.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q3_K_S.gguf) | Q3_K_S | 4.34GB | | [Tess-10.7B-v2.0.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.IQ3_M.gguf) | IQ3_M | 4.51GB | | [Tess-10.7B-v2.0.Q3_K.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q3_K.gguf) | Q3_K | 4.84GB | | [Tess-10.7B-v2.0.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q3_K_M.gguf) | Q3_K_M | 4.84GB | | [Tess-10.7B-v2.0.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q3_K_L.gguf) | Q3_K_L | 5.26GB | | [Tess-10.7B-v2.0.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.IQ4_XS.gguf) | IQ4_XS | 5.43GB | | [Tess-10.7B-v2.0.Q4_0.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q4_0.gguf) | Q4_0 | 5.66GB | | [Tess-10.7B-v2.0.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.IQ4_NL.gguf) | IQ4_NL | 5.72GB | | [Tess-10.7B-v2.0.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q4_K_S.gguf) | Q4_K_S | 5.7GB | | [Tess-10.7B-v2.0.Q4_K.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q4_K.gguf) | Q4_K | 6.02GB | | [Tess-10.7B-v2.0.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q4_K_M.gguf) | Q4_K_M | 6.02GB | | [Tess-10.7B-v2.0.Q4_1.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q4_1.gguf) | Q4_1 | 6.27GB | | [Tess-10.7B-v2.0.Q5_0.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q5_0.gguf) | Q5_0 | 6.89GB | | [Tess-10.7B-v2.0.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q5_K_S.gguf) | Q5_K_S | 6.89GB | | [Tess-10.7B-v2.0.Q5_K.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q5_K.gguf) | Q5_K | 7.08GB | | [Tess-10.7B-v2.0.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q5_K_M.gguf) | Q5_K_M | 7.08GB | | [Tess-10.7B-v2.0.Q5_1.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q5_1.gguf) | Q5_1 | 7.51GB | | [Tess-10.7B-v2.0.Q6_K.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q6_K.gguf) | Q6_K | 8.2GB | | [Tess-10.7B-v2.0.Q8_0.gguf](https://huggingface.co/RichardErkhov/Joseph717171_-_Tess-10.7B-v2.0-gguf/blob/main/Tess-10.7B-v2.0.Q8_0.gguf) | Q8_0 | 10.62GB | Original model description: --- license: apache-2.0 base_model: [] library_name: transformers tags: - mergekit - merge pipeline_tag: text-generation --- # Credit for the model card's description goes to ddh0, mergekit, and, migtissera # Inspired by ddh0/Starling-LM-10.7B-beta and ddh0/Mistral-10.7B-Instruct-v0.2 # Tess-10.7B-v0.2 # Deprecated "This model is deprecated due to the use of wrong sliding window parameter while training. Will update with the new model link in a couple of days." - migtissera This is Tess-10.7B-v0.2, a depth-upscaled version of [migtissera/Tess-7B-v2.0](https://huggingface.co/migtissera/Tess-7B-v2.0). This model is intended to be used as a basis for further fine-tuning, or as a drop-in upgrade from the original 7 billion parameter model. Paper detailing how Depth-Up Scaling works: [SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling](https://arxiv.org/abs/2312.15166) This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). # Prompt format same as [migtissera/Tess-7B-v2.0](https://huggingface.co/migtissera/Tess-7B-v2.0) # Prompt Format: ``` SYSTEM: USER: ASSISTANT: ``` ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * /Users/jsarnecki/opt/migtissera/Tess-7B-v2.0 ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: bfloat16 merge_method: passthrough slices: - sources: - layer_range: [0, 24] model: /Users/jsarnecki/opt/migtissera/Tess-7B-v2.0 - sources: - layer_range: [8, 32] model: /Users/jsarnecki/opt/migtissera/Tess-7B-v2.0 ``` # GGUFs (Thanks to [bartowski](https://huggingface.co/bartowski)) https://huggingface.co/bartowski/Tess-10.7B-v2.0-GGUF # exl2s (Thanks to [bartowski](https://huggingface.co/bartowski)) https://huggingface.co/bartowski/Tess-10.7B-v2.0-exl2 ![Tesoro](https://huggingface.co/migtissera/Tess-7B-v2.0/resolve/main/Tesoro.png) --- license: apache-2.0 --- # Tess-7B-v2.0 Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-7B-v2.0 was trained on the Mistral-7B-v0.2 base. # Prompt Format: ``` SYSTEM: USER: ASSISTANT: ``` ### Below shows a code example on how to use this model: ```python import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "migtissera/Tess-7B-v2.0" output_file_path = "./conversations.jsonl" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_8bit=False, trust_remote_code=True, ) 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": 0.5, "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, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) answer = string.split("USER:")[0].strip() return f"{answer}" conversation = f"SYSTEM: Answer the question thoughtfully and intelligently. Always answer without hesitation." while True: user_input = input("You: ") llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: " answer = generate_text(llm_prompt) print(answer) conversation = f"{llm_prompt}{answer}" json_data = {"prompt": user_input, "answer": answer} ## Save your conversation with open(output_file_path, "a") as output_file: output_file.write(json.dumps(json_data) + "\n") ```
#### 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.