You can deploy this model and start earning money today!

RichardErkhov/
migtissera_-_Tess-7B-v2.0-gguf

Name: migtissera_-_Tess-7B-v2.0-gguf
Availability: InStock

Views

No views yet

Quantization made by Richard Erkhov.

Github

Discord

Request more models

Tess-7B-v2.0 - GGUF

Model creator: https://huggingface.co/migtissera/
Original model: https://huggingface.co/migtissera/Tess-7B-v2.0/

Name	Quant method	Size
Tess-7B-v2.0.Q2_K.gguf	Q2_K	2.53GB
Tess-7B-v2.0.IQ3_XS.gguf	IQ3_XS	2.81GB
Tess-7B-v2.0.IQ3_S.gguf	IQ3_S	2.96GB
Tess-7B-v2.0.Q3_K_S.gguf	Q3_K_S	2.95GB
Tess-7B-v2.0.IQ3_M.gguf	IQ3_M	3.06GB
Tess-7B-v2.0.Q3_K.gguf	Q3_K	3.28GB
Tess-7B-v2.0.Q3_K_M.gguf	Q3_K_M	3.28GB
Tess-7B-v2.0.Q3_K_L.gguf	Q3_K_L	3.56GB
Tess-7B-v2.0.IQ4_XS.gguf	IQ4_XS	3.67GB
Tess-7B-v2.0.Q4_0.gguf	Q4_0	3.83GB
Tess-7B-v2.0.IQ4_NL.gguf	IQ4_NL	3.87GB
Tess-7B-v2.0.Q4_K_S.gguf	Q4_K_S	3.86GB
Tess-7B-v2.0.Q4_K.gguf	Q4_K	4.07GB
Tess-7B-v2.0.Q4_K_M.gguf	Q4_K_M	4.07GB
Tess-7B-v2.0.Q4_1.gguf	Q4_1	4.24GB
Tess-7B-v2.0.Q5_0.gguf	Q5_0	4.65GB
Tess-7B-v2.0.Q5_K_S.gguf	Q5_K_S	4.65GB
Tess-7B-v2.0.Q5_K.gguf	Q5_K	4.78GB
Tess-7B-v2.0.Q5_K_M.gguf	Q5_K_M	4.78GB
Tess-7B-v2.0.Q5_1.gguf	Q5_1	5.07GB
Tess-7B-v2.0.Q6_K.gguf	Q6_K	5.53GB
Tess-7B-v2.0.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0

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.

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: <ANY SYSTEM CONTEXT>
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.

RichardErkhov/migtissera_-_Tess-7B-v2.0-gguf