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brandon57/
mistral-fastgpt-7b

Name: mistral-fastgpt-7b
Availability: InStock

transformers

safetensors

text-generation-inference

unsloth

mistral

trl

unsloth/mistral-7b-instruct-v0.3-bnb-4bit

finetune

apache-2.0

endpoints_compatible

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Mistral-FastGPT-7B: AI Assistant

Developed by: brandon57
License: apache-2.0
Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit

Installing the Model and Dependencies

To get started, install the unsloth library and get the latest version from the GitHub repository:

bash

pip install unsloth
# Also get the latest nightly Unsloth!
pip uninstall unsloth -y && pip install --upgrade --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git

Loading the Model

You can then load the fine-tuned model optimized for your language learning tasks. The model is configured for optimal performance, even with limited resources:

python

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "brandon57/mistral-fastgpt-7b",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

Interacting with the Model

Once the model is ready, you can use it for real-time conversation generation. For example, to ask the chatbot to complete a Fibonacci sequence and answer general knowledge questions:

python

FastLanguageModel.for_inference(model) # Enable native 2x faster inference
messages = [                         # Change below!
    {"role": "user",      "content": "Continue the fibonacci sequence! Your input is 1, 1, 2, 3, 5, 8"},
    {"role": "assistant", "content": "The fibonacci sequence continues as 13, 21, 34, 55 and 89."},
    {"role": "user",      "content": "What is France's tallest tower called?"},
]
input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt = True,
    return_tensors = "pt",
).to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
_ = model.generate(input_ids, streamer = text_streamer, max_new_tokens = 128, pad_token_id = tokenizer.eos_token_id)