Mistral-7B-Instruct-v0.3-TISER

Mistral-7B-Instruct-v0.3-TISER

This repository contains the full-weight merged version of the Mistral-7B-Instruct-v0.3 model, fine-tuned on the TISER dataset.

The model was trained using LoRA on an NVIDIA RTX 5090 (32GB) and subsequently merged with the original base model for seamless deployment. This version does not require additional adapters and can be used as a standalone model.

Model Description

• Base Model: mistralai/Mistral-7B-Instruct-v0.3
• Training Method: SFT (Supervised Fine-Tuning) via LoRA
• Merge Method: Linear Merge (Adapter weights integrated into base layers)
• Target Domain: TISER-specific instructions and conversational logic

Training Hyperparameters

The following hyperparameters were used during the fine-tuning process:

Quick Start

Recommended for GPUs with 16GB+ VRAM (e.g., A100, RTX 4090/5090).

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = 'xueyufeizhang/Mistral-7B-Instruct-v0.3-TISER'

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map='auto',
    attn_implementation="flash_attention_2" # Highly recommended for A100/A800/H100
)
model.eval()

# Chat template usage
messages = [
    {"role": "system", "content": "You are a helpful assistant specialized in TISER logic."},
    {"role": "user", "content": "Your query here..."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7
)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids)]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Limitations & Disclaimer

This model is a research artifact. While fine-tuned on TISER data, it inherits the biases of the original Mistral-7B-Instruct-v0.3 base model. Users should perform safety filtering and factual verification for production use cases.