chempile-instruct-base-ckpt-checkpoint-100

LoRA Adapter - Checkpoint 100

LoRA adapter fine-tuned from Qwen/Qwen2.5-7B-Instruct (checkpoint 100)

Quick Start with Quantization

pip install torch transformers peft accelerate bitsandbytes

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel, prepare_model_for_kbit_training
from accelerate import PartialState

# Configure 4-bit quantization for memory efficiency
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=False,
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct", use_fast=False)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

# Load base model with quantization
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-7B-Instruct",
    quantization_config=bnb_config,
    device_map={"": PartialState().process_index},
    torch_dtype=torch.float16,
)

# Prepare for LoRA
base_model = prepare_model_for_kbit_training(base_model)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "YOUR_USERNAME/YOUR_REPO_NAME")

print("✅ Model loaded with 4-bit quantization!")

Interactive Chat

def generate_text(model, tokenizer, prompt, max_new_tokens=500, temperature=0.7):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
        )
    
    return tokenizer.decode(
        outputs[0][inputs["input_ids"].shape[1]:], 
        skip_special_tokens=True
    )

# Interactive mode
print("🤖 Interactive Chat (type 'quit' to exit)")
while True:
    prompt = input("\nPrompt: ").strip()
    if prompt.lower() in ['quit', 'exit', 'q']:
        break
    if prompt:
        response = generate_text(model, tokenizer, prompt)
        print(f"Response: {response}")

Memory Requirements

• 4-bit quantization: ~4GB VRAM (7B model)
• 8-bit quantization: ~7GB VRAM (7B model)
• No quantization: ~14GB VRAM (7B model)

Training Details

• Base model: Qwen/Qwen2.5-7B-Instruct
• Training framework: LoRA (Low-Rank Adaptation)
• Checkpoint: 100