README.md

metadata

language: en
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.2
datasets:
  - souvik18/mistral_tokenized_2048_fixed_v2
pipeline_tag: text-generation
library_name: transformers
tags:
  - mistral
  - lora
  - qlora
  - instruction-tuning
  - causal-lm
metrics:
  - accuracy

Roy

Model Overview

Roy is a fine-tuned large language model based on
mistralai/Mistral-7B-Instruct-v0.2.

The model was trained using QLoRA with a resumable streaming pipeline and later merged into the base model to produce a single standalone checkpoint (no LoRA adapter required at inference time).

This model is optimized for:

• Instruction following
• Conversational responses
• General reasoning and explanation tasks

---

Base Model

• Base: Mistral-7B-Instruct-v0.2
• Architecture: Decoder-only Transformer
• Parameters: ~7B
• Context Length: 2048 tokens

---

Training Dataset

The model was trained on a custom tokenized dataset:

• Dataset name: mistral_tokenized_2048_fixed_v2
• Dataset repository:
  https://huggingface.co/datasets/souvik18/mistral_tokenized_2048_fixed_v2
• Owner: souvik18
• Format: Pre-tokenized input_ids
• Sequence length: 2048
• Tokenizer: Mistral tokenizer
• Dataset size: ~10.7M tokens

Dataset Processing

• Fixed padding and truncation
• Removed malformed / corrupted samples
• Validated against NaN and overflow issues
• Optimized for streaming-based training

---

Training Method

• Fine-tuning method: QLoRA
• Quantization: 4-bit (NF4)
• Optimizer: AdamW
• Learning rate: 2e-4
• LoRA rank (r): 32
• Target modules:
  q_proj, k_proj, v_proj, o_proj,
  gate_proj, up_proj, down_proj
• Gradient checkpointing: Enabled
• Training style: Streaming + resumable
• Checkpointing: Hugging Face Hub (HF-only)

After training, the LoRA adapter was merged into the base model weights to create this final model.

---

Inference

This model can be used directly without any LoRA adapter.

Example (Transformers)

!pip uninstall -y transformers peft accelerate torch safetensors numpy
!pip install numpy==1.26.4
!pip install torch==2.2.2
!pip install transformers==4.41.2
!pip install peft==0.11.1
!pip install accelerate==0.30.1
!pip install safetensors==0.4.3

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# -----------------------------
# CONFIG
# -----------------------------
MODEL_ID = "souvik18/Roy"
DTYPE = torch.float16   # use float16 for GPU

# -----------------------------
# LOAD TOKENIZER & MODEL
# -----------------------------
print("🔹 Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
tokenizer.pad_token = tokenizer.eos_token

print("🔹 Loading model...")
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=DTYPE,
    device_map="auto"
)
model.eval()

print("\n✅ Model loaded successfully")
print("Type 'exit' or 'quit' to stop\n")

# -----------------------------
# CHAT LOOP
# -----------------------------
while True:
    user_input = input("🧑 You: ").strip()

    if user_input.lower() in ["exit", "quit"]:
        print("👋 Bye!")
        break

    prompt = f"[INST] {user_input} [/INST]"

    inputs = tokenizer(
        prompt,
        return_tensors="pt"
    ).to(model.device)

    with torch.no_grad():
        output = model.generate(
            **inputs,
            max_new_tokens=200,
            temperature=0.7,
            top_p=0.9,
            do_sample=True,
            repetition_penalty=1.1,
            eos_token_id=tokenizer.eos_token_id,
        )

    response = tokenizer.decode(output[0], skip_special_tokens=True)
    print(f"\n Roy: {response}\n")