thesven/Aether-Code-Mistral-7B-0.3-v1-bnb-4bit

Uploaded model

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

This model is an iteration of the Mistral 7B model, fine-tuned using Supervised Fine-Tuning (SFT) on the AetherCode-v1 dataset specifically for code-related tasks. It combines the advanced capabilities of the base Mistral 7B model with specialized training to enhance its performance in software development contexts.

Usage

from unsloth import FastLanguageModel

max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "thesven/Aether-Code-Mistral-7B-0.3-v1", # YOUR MODEL YOU USED FOR TRAINING
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "You are an expert python developer, help me with my questions.", # instruction
        "How can I use puppeteer to get a mobile screen shot of a website?", # input
        "", # output - leave this blank for generation!
    ),
], return_tensors = "pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens = 4000, use_cache = True)
print(tokenizer.batch_decode(outputs))

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.