import subprocess
import sys
import os
import urllib.request
import zipfile
import ssl

# Create an SSL context that doesn't verify certificates
ssl_context = ssl.create_default_context()
ssl_context.check_hostname = False
ssl_context.verify_mode = ssl.CERT_NONE

# Install packages first
print("🔧 Installing Python packages...")
packages = [
    "torch", "torchvision", "torchaudio", 
    "transformers", "datasets", "accelerate", "peft",
    "bitsandbytes>=0.43.0", "safetensors", "fastcore", "requests"
]

for package in packages:
    try:
        subprocess.check_call([sys.executable, "-m", "pip", "install", "--user", package], 
                            stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
        print(f"✅ Installed {package}")
    except:
        print(f"❌ Failed to install {package}")

# Download with SSL verification disabled
print("\n📥 Downloading repository...")
url = "https://github.com/AnswerDotAI/fsdp_qlora/archive/refs/heads/main.zip"

try:
    # Use urllib with disabled SSL verification
    urllib.request.urlretrieve(url, "fsdp_qlora.zip", context=ssl_context)
    print("✅ Downloaded using urllib")
except Exception as e:
    print(f"❌ urllib failed: {e}")
    
    # Fallback to requests
    try:
        import requests
        print("🔄 Trying with requests...")
        response = requests.get(url, verify=False)
        with open("fsdp_qlora.zip", "wb") as f:
            f.write(response.content)
        print("✅ Downloaded using requests")
    except Exception as e2:
        print(f"❌ requests also failed: {e2}")

# Extract if download succeeded
if os.path.exists("fsdp_qlora.zip"):
    print("📂 Extracting repository...")
    
    # Clean up existing directory
    if os.path.exists("fsdp_qlora"):
        import shutil
        shutil.rmtree("fsdp_qlora")
    
    with zipfile.ZipFile("fsdp_qlora.zip", 'r') as zip_ref:
        zip_ref.extractall(".")
    
    os.rename("fsdp_qlora-main", "fsdp_qlora")
    os.remove("fsdp_qlora.zip")
    print("✅ Setup complete! Repository is in ./fsdp_qlora")
    
    # Verify
    if os.path.exists("fsdp_qlora/train.py"):
        print("🚀 train.py found - ready to train!")
    else:
        print("❌ train.py not found")
else:
    print("❌ Download failed completely")

🔧 Installing Python packages...
✅ Installed torch
✅ Installed torchvision
✅ Installed torchaudio
✅ Installed transformers
✅ Installed datasets
✅ Installed accelerate
✅ Installed peft
✅ Installed bitsandbytes>=0.43.0
✅ Installed safetensors
✅ Installed fastcore
✅ Installed requests

📥 Downloading repository...
❌ urllib failed: urlretrieve() got an unexpected keyword argument 'context'
🔄 Trying with requests...
✅ Downloaded using requests
📂 Extracting repository...
✅ Setup complete! Repository is in ./fsdp_qlora
🚀 train.py found - ready to train!

/usr/local/lib/python3.11/site-packages/urllib3/connectionpool.py:1097: InsecureRequestWarning: Unverified HTTPS request is being made to host 'github.com'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#tls-warnings
  warnings.warn(
/usr/local/lib/python3.11/site-packages/urllib3/connectionpool.py:1097: InsecureRequestWarning: Unverified HTTPS request is being made to host 'codeload.github.com'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#tls-warnings
  warnings.warn(

%pip install huggingface_hub

Requirement already satisfied: huggingface_hub in /root/.local/lib/python3.11/site-packages (0.33.4)
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Note: you may need to restart the kernel to use updated packages.

%pip install llama-recipes fastcore "transformers!=4.38.*,!=4.39.*" --extra-index-url https://download.pytorch.org/whl/test/cu118

Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/test/cu118
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  Preparing metadata (setup.py) ... done
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  Downloading https://download.pytorch.org/whl/test/PyYAML-6.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (757 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 757.7/757.7 kB 3.6 MB/s eta 0:00:00
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Collecting rich~=13.5.2 (from semgrep>1.68->codeshield->llama-cookbook==0.0.5.post1->llama-recipes)
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Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.11/site-packages (from jsonschema~=4.6->semgrep>1.68->codeshield->llama-cookbook==0.0.5.post1->llama-recipes) (0.36.2)
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Collecting opentelemetry-exporter-otlp-proto-common==1.25.0 (from opentelemetry-exporter-otlp-proto-http~=1.25.0->semgrep>1.68->codeshield->llama-cookbook==0.0.5.post1->llama-recipes)
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INFO: pip is looking at multiple versions of opentelemetry-semantic-conventions to determine which version is compatible with other requirements. This could take a while.
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INFO: pip is still looking at multiple versions of opentelemetry-semantic-conventions to determine which version is compatible with other requirements. This could take a while.
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INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.
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Building wheels for collected packages: markupsafe, peewee, fire, rouge-score, antlr4-python3-runtime, langdetect
  DEPRECATION: Building 'markupsafe' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'markupsafe'. Discussion can be found at https://github.com/pypa/pip/issues/6334
  Building wheel for markupsafe (setup.py) ... done
  Created wheel for markupsafe: filename=MarkupSafe-2.0.1-py3-none-any.whl size=9745 sha256=7e2d66f9e7f03fb5c9650b1bb42b497988d9caf286498a83e97842c9bd37bfd8
  Stored in directory: /tmp/pip-ephem-wheel-cache-ysln68d0/wheels/ea/18/79/6266ea508b8164a77b95aa19534c77eb805f2878612c37efca
  Building wheel for peewee (pyproject.toml) ... done
  Created wheel for peewee: filename=peewee-3.18.2-py3-none-any.whl size=139106 sha256=74775c98fa5491eac6deed3b5a8d8c2e44da24939d4780973eaec27fdde604ca
  Stored in directory: /tmp/pip-ephem-wheel-cache-ysln68d0/wheels/28/84/61/758d1bd7b9c9d700158c8642a8aff2a9bf2e1ae69641c40784
  DEPRECATION: Building 'fire' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'fire'. Discussion can be found at https://github.com/pypa/pip/issues/6334
  Building wheel for fire (setup.py) ... done
  Created wheel for fire: filename=fire-0.7.0-py3-none-any.whl size=114249 sha256=604933afdfa2c129d2a0233ffaccf9ce24822ded3a417ab33a6f781ddc81d7af
  Stored in directory: /tmp/pip-ephem-wheel-cache-ysln68d0/wheels/46/54/24/1624fd5b8674eb1188623f7e8e17cdf7c0f6c24b609dfb8a89
  DEPRECATION: Building 'rouge-score' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'rouge-score'. Discussion can be found at https://github.com/pypa/pip/issues/6334
  Building wheel for rouge-score (setup.py) ... done
  Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24935 sha256=0831f9eb0a69648a284b2f8bc386bdee2e9c262f67eb5d95bd6bffb09eaec1fc
  Stored in directory: /tmp/pip-ephem-wheel-cache-ysln68d0/wheels/1e/19/43/8a442dc83660ca25e163e1bd1f89919284ab0d0c1475475148
  DEPRECATION: Building 'antlr4-python3-runtime' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'antlr4-python3-runtime'. Discussion can be found at https://github.com/pypa/pip/issues/6334
  Building wheel for antlr4-python3-runtime (setup.py) ... done
  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144555 sha256=187021b4cd7030f0ba2c29c20fdd1655628f2e6f082d8f5ae91f16dd343995bd
  Stored in directory: /tmp/pip-ephem-wheel-cache-ysln68d0/wheels/56/e9/6d/b5ab1c9ab438ad8897f796286bf23cd4ffc0f1ea8bc2200ecd
  DEPRECATION: Building 'langdetect' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'langdetect'. Discussion can be found at https://github.com/pypa/pip/issues/6334
  Building wheel for langdetect (setup.py) ... done
  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993225 sha256=a906ce17a25a949ae03647d998c1541f9c12b603f2c439fdef6983b2076421fc
  Stored in directory: /tmp/pip-ephem-wheel-cache-ysln68d0/wheels/0a/f2/b2/e5ca405801e05eb7c8ed5b3b4bcf1fcabcd6272c167640072e
Successfully built markupsafe peewee fire rouge-score antlr4-python3-runtime langdetect
Installing collected packages: texttable, sentencepiece, pydub, peewee, flatbuffers, filetype, brotli, boltons, appdirs, antlr4-python3-runtime, zipp, wrapt, websockets, unstructured.pytesseract, typing-extensions, tomlkit, tomli, tokenize-rt, threadpoolctl, termcolor, tabulate, shellingham, semantic-version, ruff, ruamel.yaml.clib, rapidfuzz, pyyaml, python-multipart, python-magic, python-iso639, pyppmd, pypdfium2, pypdf, pyparsing, pycryptodomex, pybcj, pyasn1, protobuf, pi-heif, pdf2image, pathspec, orjson, opentelemetry-util-http, olefile, numpy, mypy-extensions, multivolumefile, mdurl, marshmallow, markupsafe, lxml, loralib, langdetect, kiwisolver, joblib, jiter, inflate64, humanfriendly, html5lib, grpcio, groovy, fonttools, ffmpy, face, exceptiongroup, emoji, distro, cycler, colorama, click, chardet, cachetools, bracex, backoff, annotated-types, aiofiles, absl-py, wcmatch, uvicorn, typing-inspection, typing-inspect, scipy, ruamel.yaml, rsa, requests-toolbelt, pyzstd, python-oxmsg, pydantic-core, pycocotools, pyasn1-modules, proto-plus, opentelemetry-proto, opencv-python, onnx, omegaconf, nltk, markdown-it-py, importlib-metadata, googleapis-common-protos, glom, fire, deprecated, cryptography, contourpy, coloredlogs, click-option-group, black, starlette, scikit-learn, rouge-score, rich, pydantic, py7zr, pikepdf, pdfminer.six, opentelemetry-exporter-otlp-proto-common, opentelemetry-api, onnxruntime, matplotlib, grpcio-status, google-auth, dataclasses-json, unstructured-client, typer, safehttpx, opentelemetry-semantic-conventions, opentelemetry-instrumentation, openai, gradio-client, google-api-core, fastapi, unstructured, timm, sentence-transformers, optimum, opentelemetry-sdk, opentelemetry-instrumentation-requests, gradio, evaluate, unstructured-inference, opentelemetry-exporter-otlp-proto-http, google-cloud-vision, effdet, semgrep, codeshield, llama-cookbook, llama-recipes
  Attempting uninstall: typing-extensions━━━━━━━━━━━━━━━━━━━━━━━━━  12/147 [websockets]on3-runtime]
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Successfully installed absl-py-2.3.1 aiofiles-24.1.0 annotated-types-0.7.0 antlr4-python3-runtime-4.9.3 appdirs-1.4.4 backoff-2.2.1 black-25.1.0 boltons-21.0.0 bracex-2.6 brotli-1.1.0 cachetools-5.5.2 chardet-5.2.0 click-8.1.8 click-option-group-0.5.7 codeshield-1.0.1 colorama-0.4.6 coloredlogs-15.0.1 contourpy-1.3.2 cryptography-45.0.5 cycler-0.12.1 dataclasses-json-0.6.7 deprecated-1.2.18 distro-1.9.0 effdet-0.4.1 emoji-2.14.1 evaluate-0.4.5 exceptiongroup-1.2.2 face-24.0.0 fastapi-0.116.1 ffmpy-0.6.0 filetype-1.2.0 fire-0.7.0 flatbuffers-25.2.10 fonttools-4.59.0 glom-22.1.0 google-api-core-2.25.1 google-auth-2.40.3 google-cloud-vision-3.10.2 googleapis-common-protos-1.70.0 gradio-5.38.0 gradio-client-1.11.0 groovy-0.1.2 grpcio-1.73.1 grpcio-status-1.62.3 html5lib-1.1 humanfriendly-10.0 importlib-metadata-7.1.0 inflate64-1.0.3 jiter-0.10.0 joblib-1.5.1 kiwisolver-1.4.8 langdetect-1.0.9 llama-cookbook-0.0.5.post1 llama-recipes-0.0.5.post2 loralib-0.1.2 lxml-6.0.0 markdown-it-py-3.0.0 markupsafe-2.0.1 marshmallow-3.26.1 matplotlib-3.10.3 mdurl-0.1.2 multivolumefile-0.2.3 mypy-extensions-1.1.0 nltk-3.9.1 numpy-2.2.6 olefile-0.47 omegaconf-2.3.0 onnx-1.18.0 onnxruntime-1.22.1 openai-1.97.0 opencv-python-4.12.0.88 opentelemetry-api-1.25.0 opentelemetry-exporter-otlp-proto-common-1.25.0 opentelemetry-exporter-otlp-proto-http-1.25.0 opentelemetry-instrumentation-0.46b0 opentelemetry-instrumentation-requests-0.46b0 opentelemetry-proto-1.25.0 opentelemetry-sdk-1.25.0 opentelemetry-semantic-conventions-0.46b0 opentelemetry-util-http-0.46b0 optimum-1.26.1 orjson-3.11.0 pathspec-0.12.1 pdf2image-1.17.0 pdfminer.six-20250506 peewee-3.18.2 pi-heif-1.0.0 pikepdf-9.10.2 proto-plus-1.26.1 protobuf-4.25.8 py7zr-1.0.0 pyasn1-0.6.1 pyasn1-modules-0.4.2 pybcj-1.0.6 pycocotools-2.0.10 pycryptodomex-3.23.0 pydantic-2.11.7 pydantic-core-2.33.2 pydub-0.25.1 pyparsing-3.2.3 pypdf-5.8.0 pypdfium2-4.30.1 pyppmd-1.2.0 python-iso639-2025.2.18 python-magic-0.4.27 python-multipart-0.0.20 python-oxmsg-0.0.2 pyyaml-6.0.1 pyzstd-0.17.0 rapidfuzz-3.13.0 requests-toolbelt-1.0.0 rich-13.5.3 rouge-score-0.1.2 rsa-4.9.1 ruamel.yaml-0.18.14 ruamel.yaml.clib-0.2.12 ruff-0.12.4 safehttpx-0.1.6 scikit-learn-1.7.1 scipy-1.16.0 semantic-version-2.10.0 semgrep-1.128.1 sentence-transformers-5.0.0 sentencepiece-0.2.0 shellingham-1.5.4 starlette-0.47.1 tabulate-0.9.0 termcolor-3.1.0 texttable-1.7.0 threadpoolctl-3.6.0 timm-1.0.17 tokenize-rt-6.2.0 tomli-2.0.2 tomlkit-0.13.3 typer-0.16.0 typing-extensions-4.14.1 typing-inspect-0.9.0 typing-inspection-0.4.1 unstructured-0.18.9 unstructured-client-0.39.1 unstructured-inference-1.0.5 unstructured.pytesseract-0.3.15 uvicorn-0.35.0 wcmatch-8.5.2 websockets-15.0.1 wrapt-1.17.2 zipp-3.23.0
Note: you may need to restart the kernel to use updated packages.

%pip install bitsandbytes>=0.43.0

Note: you may need to restart the kernel to use updated packages.

from huggingface_hub import login
import getpass

# Get your token securely
hf_token = getpass.getpass("Enter your Hugging Face token: ")

# Login programmatically
login(token=hf_token)

print("✅ Successfully logged in to Hugging Face!")

/root/.local/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Enter your Hugging Face token:  ········

✅ Successfully logged in to Hugging Face!

import os
import subprocess

# Set environment variable
os.environ['BNB_CUDA_VERSION'] = '125'

# Install ONLY the essential fixes
commands = [
    ["pip", "install", "transformers==4.47.1", "--upgrade"],
    ["pip", "install", "bitsandbytes>=0.43.0", "--upgrade", "--force-reinstall"]
]

for cmd in commands:
    print(f"Running: {' '.join(cmd)}")
    try:
        result = subprocess.run(cmd, capture_output=True, text=True, timeout=300)
        if result.returncode != 0:
            print(f"Error: {result.stderr}")
        else:
            print("✅ Success")
    except subprocess.TimeoutExpired:
        print("⚠️ Command timed out")

print("✅ Essential dependencies updated")

Running: pip install transformers==4.47.1 --upgrade
✅ Success
Running: pip install bitsandbytes>=0.43.0 --upgrade --force-reinstall
✅ Success
✅ Essential dependencies updated

import os
os.chdir("fsdp_qlora")

# Apply fixes to train.py
with open("train.py", "r") as f:
    content = f.read()

# Apply transformers fix
if "LLAMA_ATTENTION_CLASSES" in content:
    print("🔧 Applying transformers fix...")
    
    # Simple replacement approach
    content = content.replace(
        "LLAMA_ATTENTION_CLASSES,", 
        "LlamaAttention,"
    )
    content = content.replace(
        "MISTRAL_ATTENTION_CLASSES,", 
        "MistralAttention,"
    )
    content = content.replace(
        "(*LLAMA_ATTENTION_CLASSES.values(), *MISTRAL_ATTENTION_CLASSES.values())",
        "(LlamaAttention, MistralAttention)"
    )
    
    # Add dataset choice
    if "uganda_clinical_guidelines" not in content:
        content = content.replace(
            '"orca_math"]) = "alpaca_sample",',
            '"orca_math", "uganda_clinical_guidelines"]) = "alpaca_sample",'
        )
    
    with open("train.py", "w") as f:
        f.write(content)
    
    print("✅ train.py fixed")
    

🔧 Applying transformers fix...
✅ train.py fixed

# Test if fixes work
try:
    import bitsandbytes
    print("✅ Bitsandbytes works")
except Exception as e:
    print(f"❌ Bitsandbytes issue: {e}")

print("Ready for training!")

WARNING: BNB_CUDA_VERSION=125 environment variable detected; loading libbitsandbytes_cuda125.so.
This can be used to load a bitsandbytes version built with a CUDA version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=

❌ Bitsandbytes issue: Failed to find C compiler. Please specify via CC environment variable.
Ready for training!

import subprocess
import os

def setup_environment():
    """Setup the environment to avoid compiler issues"""
    
    print("🔧 Setting up environment for training...")
    
    # Step 1: Set environment variables
    os.environ['BNB_CUDA_VERSION'] = '125'
    os.environ['CC'] = '/usr/bin/gcc'
    os.environ['CXX'] = '/usr/bin/g++'
    os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
    
    # Step 2: Install build tools if possible
    try:
        print("📦 Installing build tools...")
        subprocess.run(["apt", "update"], capture_output=True, timeout=60)
        result = subprocess.run(["apt", "install", "-y", "build-essential", "gcc", "g++"], 
                              capture_output=True, timeout=120)
        if result.returncode == 0:
            print("✅ Build tools installed")
        else:
            print("⚠️ Build tools installation failed, proceeding anyway...")
    except Exception as e:
        print(f"⚠️ Could not install build tools: {e}")
    
    # Step 3: Test if bitsandbytes works now
    try:
        import bitsandbytes
        print("✅ Bitsandbytes imports successfully")
        return True
    except Exception as e:
        print(f"❌ Bitsandbytes still has issues: {e}")
        
        # Step 4: Try installing older version
        print("🔄 Trying older bitsandbytes version...")
        try:
            subprocess.run(["pip", "uninstall", "bitsandbytes", "-y"], capture_output=True)
            subprocess.run(["pip", "install", "bitsandbytes==0.41.3"], capture_output=True)
            
            import bitsandbytes
            print("✅ Older bitsandbytes version works")
            return True
        except Exception as e2:
            print(f"❌ Even older version failed: {e2}")
            return False

# Run the setup
if setup_environment():
    print("🚀 Environment ready! Running training...")
    
    # Your training command
    cmd = [
        "python", "train.py",
        "--train_type", "bnb_dora",
        "--model_name", "meta-llama/Llama-2-7b-hf", 
        "--dataset", "ug_clinical_guidelines",  # Fixed dataset name
        "--dataset_samples", "10",
        "--batch_size", "1",
        "--context_length", "256",
        "--num_epochs", "1",
        "--save_model", "false",
        "--log_to", "stdout"
    ]
    
    print("🧪 Running test training...")
    process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
    
    try:
        for line in iter(process.stdout.readline, ''):
            if line:
                print(line.rstrip())
        process.wait()
        print(f"Test completed: {process.returncode}")
    except KeyboardInterrupt:
        print("Interrupted")
        process.terminate()
        
else:
    print("❌ Could not setup environment properly")

🔧 Setting up environment for training...
📦 Installing build tools...
✅ Build tools installed
✅ Bitsandbytes imports successfully
🚀 Environment ready! Running training...
🧪 Running test training...
WARNING: BNB_CUDA_VERSION=125 environment variable detected; loading libbitsandbytes_cuda125.so.
This can be used to load a bitsandbytes version built with a CUDA version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=

World size: 2
WARNING: BNB_CUDA_VERSION=125 environment variable detected; loading libbitsandbytes_cuda125.so.
This can be used to load a bitsandbytes version built with a CUDA version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=

WARNING: BNB_CUDA_VERSION=125 environment variable detected; loading libbitsandbytes_cuda125.so.
This can be used to load a bitsandbytes version built with a CUDA version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=


Generating train split:   0%|          | 0/130 [00:00<?, ? examples/s]
Generating train split: 100%|██████████| 130/130 [00:00<00:00, 23549.26 examples/s]
Creating model 0

Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]
Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]
Downloading shards:  50%|█████     | 1/2 [00:48<00:48, 48.18s/it]
Downloading shards:  50%|█████     | 1/2 [00:48<00:48, 48.21s/it]
Downloading shards: 100%|██████████| 2/2 [01:04<00:00, 29.56s/it]
Downloading shards: 100%|██████████| 2/2 [01:04<00:00, 32.35s/it]
Loading model 0

Loading & Quantizing Model Shards:   0%|          | 0/2 [00:00<?, ?it/s]
Downloading shards: 100%|██████████| 2/2 [01:04<00:00, 29.57s/it]
Downloading shards: 100%|██████████| 2/2 [01:04<00:00, 32.36s/it]
WARNING: BNB_CUDA_VERSION=125 environment variable detected; loading libbitsandbytes_cuda125.so.
This can be used to load a bitsandbytes version built with a CUDA version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=

WARNING: BNB_CUDA_VERSION=125 environment variable detected; loading libbitsandbytes_cuda125.so.
This can be used to load a bitsandbytes version built with a CUDA version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=


Loading & Quantizing Model Shards:  50%|█████     | 1/2 [00:15<00:15, 15.38s/it]WARNING: BNB_CUDA_VERSION=125 environment variable detected; loading libbitsandbytes_cuda125.so.
This can be used to load a bitsandbytes version built with a CUDA version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=

WARNING: BNB_CUDA_VERSION=125 environment variable detected; loading libbitsandbytes_cuda125.so.
This can be used to load a bitsandbytes version built with a CUDA version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=


Loading & Quantizing Model Shards: 100%|██████████| 2/2 [00:25<00:00, 12.20s/it]
Loading & Quantizing Model Shards: 100%|██████████| 2/2 [00:25<00:00, 12.68s/it]
/usr/local/lib/python3.11/site-packages/torch/distributed/distributed_c10d.py:4631: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
  warnings.warn(  # warn only once
/usr/local/lib/python3.11/site-packages/torch/distributed/distributed_c10d.py:4631: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
  warnings.warn(  # warn only once
Rank 0: Model created: 0.107 GiB
Using BNB DORA 0
Rank 0: LoRA layers added: 0.107 GiB
Wrapping model w/ FSDP 0
Rank 0: Wrapped model: 1.625 GiB
Applying activation checkpointing 0
Total Training Steps: 5

  0%|          | 0/5 [00:00<?, ?it/s]
Epoch 0, Loss 0.000:   0%|          | 0/5 [00:00<?, ?it/s]
Epoch 0, Loss 0.000:  20%|██        | 1/5 [00:07<00:28,  7.01s/it]
Epoch 0, Loss 1.388, LR 1.00e-05:  20%|██        | 1/5 [00:07<00:28,  7.01s/it]
Epoch 0, Loss 1.388, LR 1.00e-05:  40%|████      | 2/5 [00:09<00:12,  4.30s/it]
Epoch 0, Loss 1.477, LR 1.00e-05:  40%|████      | 2/5 [00:09<00:12,  4.30s/it]
Epoch 0, Loss 1.477, LR 1.00e-05:  60%|██████    | 3/5 [00:11<00:06,  3.23s/it]
Epoch 0, Loss 1.187, LR 1.00e-05:  60%|██████    | 3/5 [00:11<00:06,  3.23s/it]
Epoch 0, Loss 1.187, LR 1.00e-05:  80%|████████  | 4/5 [00:13<00:02,  2.71s/it]
Epoch 0, Loss 1.041, LR 1.00e-05:  80%|████████  | 4/5 [00:13<00:02,  2.71s/it]
Epoch 0, Loss 1.041, LR 1.00e-05: 100%|██████████| 5/5 [00:15<00:00,  2.43s/it]
Epoch 0, Loss 1.475, LR 1.00e-05: 100%|██████████| 5/5 [00:15<00:00,  2.43s/it]
Epoch 0, Loss 1.475, LR 1.00e-05: 100%|██████████| 5/5 [00:15<00:00,  3.12s/it]
Finished training 0
CUDA event elapsed time: 15.2380859375 sec
time_taken: 15.2380859375
Rank 0: Before forward: 1.62 GiB
Rank 0: After forward: 2.46 GiB
Rank 0: After backward: 2.64 GiB
Rank 0: Peak allocated memory: 1.35 GiB
Rank 0: Peak reserved memory:  2.65 GiB
Using BNB DORA 1
Test completed: 0

!ls

'Converting the State Dict.ipynb'   fsdp_multi_node.sh   tests
 LICENSE                hf_train.py      train.py
 PROFILING.md               nbs          train.sh
 README.md              profile.sh       train_hqq_bench.sh
 __pycache__                profiling_utils.py   train_sql.sh
 benchmarking               scripts
 benchmarks_03_2024.md          table1.sh

import subprocess
import os

# Set environment
os.environ['BNB_CUDA_VERSION'] = '125'
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'

# FULL TRAINING with model saving
cmd = [
    "python", "train.py",
    "--train_type", "bnb_dora",
    "--model_name", "meta-llama/Llama-2-7b-hf", 
    "--dataset", "ug_clinical_guidelines",
    "--dataset_samples", "130",  # Use all your data
    "--batch_size", "2",
    "--context_length", "512",   # Longer context for medical text
    "--precision", "bf16",
    "--num_epochs", "3",         # More epochs for better training
    "--save_model", "true",      # 🔥 SAVE THE MODEL
    "--output_dir", "./uganda_clinical_model",  # Where to save
    "--log_to", "stdout"
]

print("🏥 Training Uganda Clinical Model (FULL)...")
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)

try:
    for line in iter(process.stdout.readline, ''):
        if line:
            print(line.rstrip())
    process.wait()
    print(f"Training completed: {process.returncode}")
    
    # Check if model was saved
    if os.path.exists("uganda_clinical_model"):
        print("🎉 Model saved successfully!")
        print("📁 Saved files:")
        for f in os.listdir("uganda_clinical_model"):
            print(f"  📄 {f}")
    
except KeyboardInterrupt:
    print("Interrupted")
    process.terminate()

🏥 Training Uganda Clinical Model (FULL)...
WARNING: BNB_CUDA_VERSION=125 environment variable detected; loading libbitsandbytes_cuda125.so.
This can be used to load a bitsandbytes version built with a CUDA version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=

World size: 2
WARNING: BNB_CUDA_VERSION=125 environment variable detected; loading libbitsandbytes_cuda125.so.
This can be used to load a bitsandbytes version built with a CUDA version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=

WARNING: BNB_CUDA_VERSION=125 environment variable detected; loading libbitsandbytes_cuda125.so.
This can be used to load a bitsandbytes version built with a CUDA version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=

Creating model 0
Loading model 0

Loading & Quantizing Model Shards:   0%|          | 0/2 [00:00<?, ?it/s]WARNING: BNB_CUDA_VERSION=125 environment variable detected; loading libbitsandbytes_cuda125.so.
This can be used to load a bitsandbytes version built with a CUDA version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=

WARNING: BNB_CUDA_VERSION=125 environment variable detected; loading libbitsandbytes_cuda125.so.
This can be used to load a bitsandbytes version built with a CUDA version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=

WARNING: BNB_CUDA_VERSION=125 environment variable detected; loading libbitsandbytes_cuda125.so.
This can be used to load a bitsandbytes version built with a CUDA version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=


Loading & Quantizing Model Shards:  50%|█████     | 1/2 [00:15<00:15, 15.16s/it]WARNING: BNB_CUDA_VERSION=125 environment variable detected; loading libbitsandbytes_cuda125.so.
This can be used to load a bitsandbytes version built with a CUDA version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=


Loading & Quantizing Model Shards: 100%|██████████| 2/2 [00:26<00:00, 12.62s/it]
Loading & Quantizing Model Shards: 100%|██████████| 2/2 [00:26<00:00, 13.00s/it]
/usr/local/lib/python3.11/site-packages/torch/distributed/distributed_c10d.py:4631: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
  warnings.warn(  # warn only once
/usr/local/lib/python3.11/site-packages/torch/distributed/distributed_c10d.py:4631: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
  warnings.warn(  # warn only once
Rank 0: Model created: 0.107 GiB
Using BNB DORA 0
Rank 0: LoRA layers added: 0.107 GiB
Wrapping model w/ FSDP 0
Rank 0: Wrapped model: 1.625 GiB
Applying activation checkpointing 0
Total Training Steps: 99

  0%|          | 0/99 [00:00<?, ?it/s]
Epoch 0, Loss 0.000:   0%|          | 0/99 [00:00<?, ?it/s]
Epoch 0, Loss 0.000:   1%|          | 1/99 [00:08<13:29,  8.26s/it]
Epoch 0, Loss 1.426, LR 1.00e-05:   1%|          | 1/99 [00:08<13:29,  8.26s/it]
Epoch 0, Loss 1.426, LR 1.00e-05:   2%|▏         | 2/99 [00:10<07:31,  4.66s/it]
Epoch 0, Loss 1.409, LR 1.00e-05:   2%|▏         | 2/99 [00:10<07:31,  4.66s/it]
Epoch 0, Loss 1.409, LR 1.00e-05:   3%|▎         | 3/99 [00:12<05:27,  3.41s/it]
Epoch 0, Loss 1.369, LR 1.00e-05:   3%|▎         | 3/99 [00:12<05:27,  3.41s/it]
Epoch 0, Loss 1.369, LR 1.00e-05:   4%|▍         | 4/99 [00:14<04:27,  2.82s/it]
Epoch 0, Loss 1.200, LR 1.00e-05:   4%|▍         | 4/99 [00:14<04:27,  2.82s/it]
Epoch 0, Loss 1.200, LR 1.00e-05:   5%|▌         | 5/99 [00:16<03:55,  2.51s/it]
Epoch 0, Loss 1.123, LR 1.00e-05:   5%|▌         | 5/99 [00:16<03:55,  2.51s/it]
Epoch 0, Loss 1.123, LR 1.00e-05:   6%|▌         | 6/99 [00:18<03:37,  2.34s/it]
Epoch 0, Loss 1.126, LR 1.00e-05:   6%|▌         | 6/99 [00:18<03:37,  2.34s/it]
Epoch 0, Loss 1.126, LR 1.00e-05:   7%|▋         | 7/99 [00:20<03:23,  2.21s/it]
Epoch 0, Loss 0.955, LR 1.00e-05:   7%|▋         | 7/99 [00:20<03:23,  2.21s/it]
Epoch 0, Loss 0.955, LR 1.00e-05:   8%|▊         | 8/99 [00:22<03:20,  2.21s/it]
Epoch 0, Loss 0.910, LR 1.00e-05:   8%|▊         | 8/99 [00:22<03:20,  2.21s/it]
Epoch 0, Loss 0.910, LR 1.00e-05:   9%|▉         | 9/99 [00:24<03:12,  2.14s/it]
Epoch 0, Loss 0.948, LR 1.00e-05:   9%|▉         | 9/99 [00:24<03:12,  2.14s/it]
Epoch 0, Loss 0.948, LR 1.00e-05:  10%|█         | 10/99 [00:26<03:05,  2.08s/it]
Epoch 0, Loss 0.674, LR 1.00e-05:  10%|█         | 10/99 [00:26<03:05,  2.08s/it]
Epoch 0, Loss 0.674, LR 1.00e-05:  11%|█         | 11/99 [00:28<02:58,  2.03s/it]
Epoch 0, Loss 0.956, LR 1.00e-05:  11%|█         | 11/99 [00:28<02:58,  2.03s/it]
Epoch 0, Loss 0.956, LR 1.00e-05:  12%|█▏        | 12/99 [00:30<02:54,  2.01s/it]
Epoch 0, Loss 0.994, LR 1.00e-05:  12%|█▏        | 12/99 [00:30<02:54,  2.01s/it]
Epoch 0, Loss 0.994, LR 1.00e-05:  13%|█▎        | 13/99 [00:32<02:52,  2.00s/it]
Epoch 0, Loss 0.803, LR 1.00e-05:  13%|█▎        | 13/99 [00:32<02:52,  2.00s/it]
Epoch 0, Loss 0.803, LR 1.00e-05:  14%|█▍        | 14/99 [00:34<02:49,  2.00s/it]
Epoch 0, Loss 0.902, LR 1.00e-05:  14%|█▍        | 14/99 [00:34<02:49,  2.00s/it]
Epoch 0, Loss 0.902, LR 1.00e-05:  15%|█▌        | 15/99 [00:36<02:53,  2.07s/it]
Epoch 0, Loss 1.091, LR 1.00e-05:  15%|█▌        | 15/99 [00:36<02:53,  2.07s/it]
Epoch 0, Loss 1.091, LR 1.00e-05:  16%|█▌        | 16/99 [00:38<02:49,  2.04s/it]
Epoch 0, Loss 0.834, LR 1.00e-05:  16%|█▌        | 16/99 [00:38<02:49,  2.04s/it]
Epoch 0, Loss 0.834, LR 1.00e-05:  17%|█▋        | 17/99 [00:40<02:44,  2.00s/it]
Epoch 0, Loss 1.042, LR 1.00e-05:  17%|█▋        | 17/99 [00:40<02:44,  2.00s/it]
Epoch 0, Loss 1.042, LR 1.00e-05:  18%|█▊        | 18/99 [00:42<02:39,  1.97s/it]
Epoch 0, Loss 0.731, LR 1.00e-05:  18%|█▊        | 18/99 [00:42<02:39,  1.97s/it]
Epoch 0, Loss 0.731, LR 1.00e-05:  19%|█▉        | 19/99 [00:44<02:35,  1.95s/it]
Epoch 0, Loss 1.042, LR 1.00e-05:  19%|█▉        | 19/99 [00:44<02:35,  1.95s/it]
Epoch 0, Loss 1.042, LR 1.00e-05:  20%|██        | 20/99 [00:45<02:33,  1.94s/it]
Epoch 0, Loss 1.062, LR 1.00e-05:  20%|██        | 20/99 [00:46<02:33,  1.94s/it]
Epoch 0, Loss 1.062, LR 1.00e-05:  21%|██        | 21/99 [00:47<02:31,  1.95s/it]
Epoch 0, Loss 0.817, LR 1.00e-05:  21%|██        | 21/99 [00:47<02:31,  1.95s/it]
Epoch 0, Loss 0.817, LR 1.00e-05:  22%|██▏       | 22/99 [00:50<02:36,  2.04s/it]
Epoch 0, Loss 0.928, LR 1.00e-05:  22%|██▏       | 22/99 [00:50<02:36,  2.04s/it]
Epoch 0, Loss 0.928, LR 1.00e-05:  23%|██▎       | 23/99 [00:52<02:32,  2.01s/it]
Epoch 0, Loss 1.187, LR 1.00e-05:  23%|██▎       | 23/99 [00:52<02:32,  2.01s/it]
Epoch 0, Loss 1.187, LR 1.00e-05:  24%|██▍       | 24/99 [00:54<02:29,  1.99s/it]
Epoch 0, Loss 1.039, LR 1.00e-05:  24%|██▍       | 24/99 [00:54<02:29,  1.99s/it]
Epoch 0, Loss 1.039, LR 1.00e-05:  25%|██▌       | 25/99 [00:56<02:27,  1.99s/it]
Epoch 0, Loss 0.800, LR 1.00e-05:  25%|██▌       | 25/99 [00:56<02:27,  1.99s/it]
Epoch 0, Loss 0.800, LR 1.00e-05:  26%|██▋       | 26/99 [00:58<02:24,  1.98s/it]
Epoch 0, Loss 0.946, LR 1.00e-05:  26%|██▋       | 26/99 [00:58<02:24,  1.98s/it]
Epoch 0, Loss 0.946, LR 1.00e-05:  27%|██▋       | 27/99 [01:00<02:23,  1.99s/it]
Epoch 0, Loss 1.006, LR 1.00e-05:  27%|██▋       | 27/99 [01:00<02:23,  1.99s/it]
Epoch 0, Loss 1.006, LR 1.00e-05:  28%|██▊       | 28/99 [01:01<02:20,  1.98s/it]
Epoch 0, Loss 0.677, LR 1.00e-05:  28%|██▊       | 28/99 [01:01<02:20,  1.98s/it]
Epoch 0, Loss 0.677, LR 1.00e-05:  29%|██▉       | 29/99 [01:04<02:22,  2.04s/it]
Epoch 0, Loss 1.013, LR 1.00e-05:  29%|██▉       | 29/99 [01:04<02:22,  2.04s/it]
Epoch 0, Loss 1.013, LR 1.00e-05:  30%|███       | 30/99 [01:06<02:18,  2.01s/it]
Epoch 0, Loss 0.918, LR 1.00e-05:  30%|███       | 30/99 [01:06<02:18,  2.01s/it]
Epoch 0, Loss 0.918, LR 1.00e-05:  31%|███▏      | 31/99 [01:08<02:16,  2.01s/it]
Epoch 0, Loss 0.839, LR 1.00e-05:  31%|███▏      | 31/99 [01:08<02:16,  2.01s/it]
Epoch 0, Loss 0.839, LR 1.00e-05:  32%|███▏      | 32/99 [01:10<02:15,  2.02s/it]
Epoch 0, Loss 1.119, LR 1.00e-05:  32%|███▏      | 32/99 [01:10<02:15,  2.02s/it]
Epoch 0, Loss 1.119, LR 1.00e-05:  33%|███▎      | 33/99 [01:12<02:13,  2.02s/it]
Epoch 0, Loss 0.769, LR 1.00e-05:  33%|███▎      | 33/99 [01:12<02:13,  2.02s/it]
Epoch 1, Loss 0.769, LR 1.00e-05:  33%|███▎      | 33/99 [01:12<02:13,  2.02s/it]
Epoch 1, Loss 0.769, LR 1.00e-05:  34%|███▍      | 34/99 [01:14<02:12,  2.03s/it]
Epoch 1, Loss 0.613, LR 1.00e-05:  34%|███▍      | 34/99 [01:14<02:12,  2.03s/it]
Epoch 1, Loss 0.613, LR 1.00e-05:  35%|███▌      | 35/99 [01:16<02:09,  2.02s/it]
Epoch 1, Loss 0.661, LR 1.00e-05:  35%|███▌      | 35/99 [01:16<02:09,  2.02s/it]
Epoch 1, Loss 0.661, LR 1.00e-05:  36%|███▋      | 36/99 [01:18<02:13,  2.13s/it]
Epoch 1, Loss 0.629, LR 1.00e-05:  36%|███▋      | 36/99 [01:18<02:13,  2.13s/it]
Epoch 1, Loss 0.629, LR 1.00e-05:  37%|███▋      | 37/99 [01:20<02:10,  2.10s/it]
Epoch 1, Loss 0.638, LR 1.00e-05:  37%|███▋      | 37/99 [01:20<02:10,  2.10s/it]
Epoch 1, Loss 0.638, LR 1.00e-05:  38%|███▊      | 38/99 [01:22<02:05,  2.05s/it]
Epoch 1, Loss 0.596, LR 1.00e-05:  38%|███▊      | 38/99 [01:22<02:05,  2.05s/it]
Epoch 1, Loss 0.596, LR 1.00e-05:  39%|███▉      | 39/99 [01:24<02:01,  2.02s/it]
Epoch 1, Loss 0.618, LR 1.00e-05:  39%|███▉      | 39/99 [01:24<02:01,  2.02s/it]
Epoch 1, Loss 0.618, LR 1.00e-05:  40%|████      | 40/99 [01:26<01:58,  2.01s/it]
Epoch 1, Loss 0.539, LR 1.00e-05:  40%|████      | 40/99 [01:26<01:58,  2.01s/it]
Epoch 1, Loss 0.539, LR 1.00e-05:  41%|████▏     | 41/99 [01:28<01:56,  2.02s/it]
Epoch 1, Loss 0.418, LR 1.00e-05:  41%|████▏     | 41/99 [01:28<01:56,  2.02s/it]
Epoch 1, Loss 0.418, LR 1.00e-05:  42%|████▏     | 42/99 [01:30<01:54,  2.01s/it]
Epoch 1, Loss 0.477, LR 1.00e-05:  42%|████▏     | 42/99 [01:30<01:54,  2.01s/it]
Epoch 1, Loss 0.477, LR 1.00e-05:  43%|████▎     | 43/99 [01:32<01:56,  2.09s/it]
Epoch 1, Loss 0.350, LR 1.00e-05:  43%|████▎     | 43/99 [01:32<01:56,  2.09s/it]
Epoch 1, Loss 0.350, LR 1.00e-05:  44%|████▍     | 44/99 [01:34<01:52,  2.04s/it]
Epoch 1, Loss 0.469, LR 1.00e-05:  44%|████▍     | 44/99 [01:34<01:52,  2.04s/it]
Epoch 1, Loss 0.469, LR 1.00e-05:  45%|████▌     | 45/99 [01:36<01:49,  2.02s/it]
Epoch 1, Loss 0.488, LR 1.00e-05:  45%|████▌     | 45/99 [01:36<01:49,  2.02s/it]
Epoch 1, Loss 0.488, LR 1.00e-05:  46%|████▋     | 46/99 [01:38<01:45,  1.99s/it]
Epoch 1, Loss 0.434, LR 1.00e-05:  46%|████▋     | 46/99 [01:38<01:45,  1.99s/it]
Epoch 1, Loss 0.434, LR 1.00e-05:  47%|████▋     | 47/99 [01:40<01:42,  1.98s/it]
Epoch 1, Loss 0.382, LR 1.00e-05:  47%|████▋     | 47/99 [01:40<01:42,  1.98s/it]
Epoch 1, Loss 0.382, LR 1.00e-05:  48%|████▊     | 48/99 [01:42<01:39,  1.95s/it]
Epoch 1, Loss 0.577, LR 1.00e-05:  48%|████▊     | 48/99 [01:42<01:39,  1.95s/it]
Epoch 1, Loss 0.577, LR 1.00e-05:  49%|████▉     | 49/99 [01:44<01:36,  1.93s/it]
Epoch 1, Loss 0.414, LR 1.00e-05:  49%|████▉     | 49/99 [01:44<01:36,  1.93s/it]
Epoch 1, Loss 0.414, LR 1.00e-05:  51%|█████     | 50/99 [01:46<01:38,  2.01s/it]
Epoch 1, Loss 0.575, LR 1.00e-05:  51%|█████     | 50/99 [01:46<01:38,  2.01s/it]
Epoch 1, Loss 0.575, LR 1.00e-05:  52%|█████▏    | 51/99 [01:48<01:34,  1.97s/it]
Epoch 1, Loss 0.422, LR 1.00e-05:  52%|█████▏    | 51/99 [01:48<01:34,  1.97s/it]
Epoch 1, Loss 0.422, LR 1.00e-05:  53%|█████▎    | 52/99 [01:50<01:31,  1.94s/it]
Epoch 1, Loss 0.690, LR 1.00e-05:  53%|█████▎    | 52/99 [01:50<01:31,  1.94s/it]
Epoch 1, Loss 0.690, LR 1.00e-05:  54%|█████▎    | 53/99 [01:52<01:28,  1.91s/it]
Epoch 1, Loss 0.683, LR 1.00e-05:  54%|█████▎    | 53/99 [01:52<01:28,  1.91s/it]
Epoch 1, Loss 0.683, LR 1.00e-05:  55%|█████▍    | 54/99 [01:54<01:25,  1.91s/it]
Epoch 1, Loss 0.494, LR 1.00e-05:  55%|█████▍    | 54/99 [01:54<01:25,  1.91s/it]
Epoch 1, Loss 0.494, LR 1.00e-05:  56%|█████▌    | 55/99 [01:55<01:23,  1.90s/it]
Epoch 1, Loss 0.613, LR 1.00e-05:  56%|█████▌    | 55/99 [01:55<01:23,  1.90s/it]
Epoch 1, Loss 0.613, LR 1.00e-05:  57%|█████▋    | 56/99 [01:57<01:21,  1.90s/it]
Epoch 1, Loss 0.723, LR 1.00e-05:  57%|█████▋    | 56/99 [01:57<01:21,  1.90s/it]
Epoch 1, Loss 0.723, LR 1.00e-05:  58%|█████▊    | 57/99 [01:59<01:22,  1.97s/it]
Epoch 1, Loss 0.645, LR 1.00e-05:  58%|█████▊    | 57/99 [01:59<01:22,  1.97s/it]
Epoch 1, Loss 0.645, LR 1.00e-05:  59%|█████▊    | 58/99 [02:01<01:20,  1.96s/it]
Epoch 1, Loss 0.494, LR 1.00e-05:  59%|█████▊    | 58/99 [02:01<01:20,  1.96s/it]
Epoch 1, Loss 0.494, LR 1.00e-05:  60%|█████▉    | 59/99 [02:03<01:17,  1.94s/it]
Epoch 1, Loss 0.573, LR 1.00e-05:  60%|█████▉    | 59/99 [02:03<01:17,  1.94s/it]
Epoch 1, Loss 0.573, LR 1.00e-05:  61%|██████    | 60/99 [02:05<01:15,  1.94s/it]
Epoch 1, Loss 0.595, LR 1.00e-05:  61%|██████    | 60/99 [02:05<01:15,  1.94s/it]
Epoch 1, Loss 0.595, LR 1.00e-05:  62%|██████▏   | 61/99 [02:07<01:13,  1.94s/it]
Epoch 1, Loss 0.381, LR 1.00e-05:  62%|██████▏   | 61/99 [02:07<01:13,  1.94s/it]
Epoch 1, Loss 0.381, LR 1.00e-05:  63%|██████▎   | 62/99 [02:09<01:11,  1.93s/it]
Epoch 1, Loss 0.641, LR 1.00e-05:  63%|██████▎   | 62/99 [02:09<01:11,  1.93s/it]
Epoch 1, Loss 0.641, LR 1.00e-05:  64%|██████▎   | 63/99 [02:11<01:10,  1.95s/it]
Epoch 1, Loss 0.548, LR 1.00e-05:  64%|██████▎   | 63/99 [02:11<01:10,  1.95s/it]
Epoch 1, Loss 0.548, LR 1.00e-05:  65%|██████▍   | 64/99 [02:13<01:11,  2.04s/it]
Epoch 1, Loss 0.494, LR 1.00e-05:  65%|██████▍   | 64/99 [02:13<01:11,  2.04s/it]
Epoch 1, Loss 0.494, LR 1.00e-05:  66%|██████▌   | 65/99 [02:15<01:07,  1.98s/it]
Epoch 1, Loss 0.712, LR 1.00e-05:  66%|██████▌   | 65/99 [02:15<01:07,  1.98s/it]
Epoch 1, Loss 0.712, LR 1.00e-05:  67%|██████▋   | 66/99 [02:17<01:04,  1.95s/it]
Epoch 1, Loss 0.335, LR 1.00e-05:  67%|██████▋   | 66/99 [02:17<01:04,  1.95s/it]
Epoch 2, Loss 0.335, LR 1.00e-05:  67%|██████▋   | 66/99 [02:17<01:04,  1.95s/it]
Epoch 2, Loss 0.335, LR 1.00e-05:  68%|██████▊   | 67/99 [02:19<01:01,  1.93s/it]
Epoch 2, Loss 0.411, LR 1.00e-05:  68%|██████▊   | 67/99 [02:19<01:01,  1.93s/it]
Epoch 2, Loss 0.411, LR 1.00e-05:  69%|██████▊   | 68/99 [02:21<01:00,  1.96s/it]
Epoch 2, Loss 0.455, LR 1.00e-05:  69%|██████▊   | 68/99 [02:21<01:00,  1.96s/it]
Epoch 2, Loss 0.455, LR 1.00e-05:  70%|██████▉   | 69/99 [02:23<00:57,  1.93s/it]
Epoch 2, Loss 0.369, LR 1.00e-05:  70%|██████▉   | 69/99 [02:23<00:57,  1.93s/it]
Epoch 2, Loss 0.369, LR 1.00e-05:  71%|███████   | 70/99 [02:25<00:55,  1.91s/it]
Epoch 2, Loss 0.384, LR 1.00e-05:  71%|███████   | 70/99 [02:25<00:55,  1.91s/it]
Epoch 2, Loss 0.384, LR 1.00e-05:  72%|███████▏  | 71/99 [02:27<00:55,  1.99s/it]
Epoch 2, Loss 0.370, LR 1.00e-05:  72%|███████▏  | 71/99 [02:27<00:55,  1.99s/it]
Epoch 2, Loss 0.370, LR 1.00e-05:  73%|███████▎  | 72/99 [02:29<00:53,  1.97s/it]
Epoch 2, Loss 0.396, LR 1.00e-05:  73%|███████▎  | 72/99 [02:29<00:53,  1.97s/it]
Epoch 2, Loss 0.396, LR 1.00e-05:  74%|███████▎  | 73/99 [02:31<00:50,  1.94s/it]
Epoch 2, Loss 0.337, LR 1.00e-05:  74%|███████▎  | 73/99 [02:31<00:50,  1.94s/it]
Epoch 2, Loss 0.337, LR 1.00e-05:  75%|███████▍  | 74/99 [02:33<00:48,  1.92s/it]
Epoch 2, Loss 0.206, LR 1.00e-05:  75%|███████▍  | 74/99 [02:33<00:48,  1.92s/it]
Epoch 2, Loss 0.206, LR 1.00e-05:  76%|███████▌  | 75/99 [02:34<00:45,  1.91s/it]
Epoch 2, Loss 0.247, LR 1.00e-05:  76%|███████▌  | 75/99 [02:34<00:45,  1.91s/it]
Epoch 2, Loss 0.247, LR 1.00e-05:  77%|███████▋  | 76/99 [02:36<00:43,  1.91s/it]
Epoch 2, Loss 0.183, LR 1.00e-05:  77%|███████▋  | 76/99 [02:36<00:43,  1.91s/it]
Epoch 2, Loss 0.183, LR 1.00e-05:  78%|███████▊  | 77/99 [02:38<00:41,  1.90s/it]
Epoch 2, Loss 0.236, LR 1.00e-05:  78%|███████▊  | 77/99 [02:38<00:41,  1.90s/it]
Epoch 2, Loss 0.236, LR 1.00e-05:  79%|███████▉  | 78/99 [02:40<00:42,  2.01s/it]
Epoch 2, Loss 0.220, LR 1.00e-05:  79%|███████▉  | 78/99 [02:40<00:42,  2.01s/it]
Epoch 2, Loss 0.220, LR 1.00e-05:  80%|███████▉  | 79/99 [02:42<00:40,  2.01s/it]
Epoch 2, Loss 0.186, LR 1.00e-05:  80%|███████▉  | 79/99 [02:42<00:40,  2.01s/it]
Epoch 2, Loss 0.186, LR 1.00e-05:  81%|████████  | 80/99 [02:44<00:37,  1.99s/it]
Epoch 2, Loss 0.251, LR 1.00e-05:  81%|████████  | 80/99 [02:44<00:37,  1.99s/it]
Epoch 2, Loss 0.251, LR 1.00e-05:  82%|████████▏ | 81/99 [02:46<00:35,  1.95s/it]
Epoch 2, Loss 0.315, LR 1.00e-05:  82%|████████▏ | 81/99 [02:46<00:35,  1.95s/it]
Epoch 2, Loss 0.315, LR 1.00e-05:  83%|████████▎ | 82/99 [02:48<00:33,  1.94s/it]
Epoch 2, Loss 0.147, LR 1.00e-05:  83%|████████▎ | 82/99 [02:48<00:33,  1.94s/it]
Epoch 2, Loss 0.147, LR 1.00e-05:  84%|████████▍ | 83/99 [02:50<00:31,  1.94s/it]
Epoch 2, Loss 0.208, LR 1.00e-05:  84%|████████▍ | 83/99 [02:50<00:31,  1.94s/it]
Epoch 2, Loss 0.208, LR 1.00e-05:  85%|████████▍ | 84/99 [02:52<00:29,  1.94s/it]
Epoch 2, Loss 0.187, LR 1.00e-05:  85%|████████▍ | 84/99 [02:52<00:29,  1.94s/it]
Epoch 2, Loss 0.187, LR 1.00e-05:  86%|████████▌ | 85/99 [02:54<00:28,  2.03s/it]
Epoch 2, Loss 0.394, LR 1.00e-05:  86%|████████▌ | 85/99 [02:54<00:28,  2.03s/it]
Epoch 2, Loss 0.394, LR 1.00e-05:  87%|████████▋ | 86/99 [02:56<00:25,  1.99s/it]
Epoch 2, Loss 0.326, LR 1.00e-05:  87%|████████▋ | 86/99 [02:56<00:25,  1.99s/it]
Epoch 2, Loss 0.326, LR 1.00e-05:  88%|████████▊ | 87/99 [02:58<00:23,  1.97s/it]
Epoch 2, Loss 0.227, LR 1.00e-05:  88%|████████▊ | 87/99 [02:58<00:23,  1.97s/it]
Epoch 2, Loss 0.227, LR 1.00e-05:  89%|████████▉ | 88/99 [03:00<00:21,  1.96s/it]
Epoch 2, Loss 0.256, LR 1.00e-05:  89%|████████▉ | 88/99 [03:00<00:21,  1.96s/it]
Epoch 2, Loss 0.256, LR 1.00e-05:  90%|████████▉ | 89/99 [03:02<00:19,  1.96s/it]
Epoch 2, Loss 0.334, LR 1.00e-05:  90%|████████▉ | 89/99 [03:02<00:19,  1.96s/it]
Epoch 2, Loss 0.334, LR 1.00e-05:  91%|█████████ | 90/99 [03:04<00:17,  1.94s/it]
Epoch 2, Loss 0.330, LR 1.00e-05:  91%|█████████ | 90/99 [03:04<00:17,  1.94s/it]
Epoch 2, Loss 0.330, LR 1.00e-05:  92%|█████████▏| 91/99 [03:06<00:15,  1.94s/it]
Epoch 2, Loss 0.259, LR 1.00e-05:  92%|█████████▏| 91/99 [03:06<00:15,  1.94s/it]
Epoch 2, Loss 0.259, LR 1.00e-05:  93%|█████████▎| 92/99 [03:08<00:14,  2.02s/it]
Epoch 2, Loss 0.283, LR 1.00e-05:  93%|█████████▎| 92/99 [03:08<00:14,  2.02s/it]
Epoch 2, Loss 0.283, LR 1.00e-05:  94%|█████████▍| 93/99 [03:11<00:13,  2.17s/it]
Epoch 2, Loss 0.271, LR 1.00e-05:  94%|█████████▍| 93/99 [03:11<00:13,  2.17s/it]
Epoch 2, Loss 0.271, LR 1.00e-05:  95%|█████████▍| 94/99 [03:21<00:23,  4.62s/it]
Epoch 2, Loss 0.180, LR 1.00e-05:  95%|█████████▍| 94/99 [03:21<00:23,  4.62s/it]
Epoch 2, Loss 0.180, LR 1.00e-05:  96%|█████████▌| 95/99 [03:23<00:15,  3.83s/it]
Epoch 2, Loss 0.325, LR 1.00e-05:  96%|█████████▌| 95/99 [03:23<00:15,  3.83s/it]
Epoch 2, Loss 0.325, LR 1.00e-05:  97%|█████████▋| 96/99 [03:25<00:09,  3.27s/it]
Epoch 2, Loss 0.280, LR 1.00e-05:  97%|█████████▋| 96/99 [03:25<00:09,  3.27s/it]
Epoch 2, Loss 0.280, LR 1.00e-05:  98%|█████████▊| 97/99 [03:27<00:05,  2.90s/it]
Epoch 2, Loss 0.228, LR 1.00e-05:  98%|█████████▊| 97/99 [03:27<00:05,  2.90s/it]
Epoch 2, Loss 0.228, LR 1.00e-05:  99%|█████████▉| 98/99 [03:29<00:02,  2.65s/it]
Epoch 2, Loss 0.380, LR 1.00e-05:  99%|█████████▉| 98/99 [03:29<00:02,  2.65s/it]
Epoch 2, Loss 0.380, LR 1.00e-05: 100%|██████████| 99/99 [03:31<00:00,  2.56s/it]
Epoch 2, Loss 0.106, LR 1.00e-05: 100%|██████████| 99/99 [03:31<00:00,  2.56s/it]/usr/local/lib/python3.11/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:680: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  warnings.warn(
/usr/local/lib/python3.11/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:680: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
  warnings.warn(
Using BNB DORA 1

Epoch 2, Loss 0.106, LR 1.00e-05: 100%|██████████| 99/99 [03:33<00:00,  2.16s/it]
Finished training 0
CUDA event elapsed time: 211.839921875 sec
time_taken: 211.839921875
Rank 0: Before forward: 1.62 GiB
Rank 0: After forward: 2.46 GiB
Rank 0: After backward: 2.64 GiB
Rank 0: Peak allocated memory: 1.41 GiB
Rank 0: Peak reserved memory:  2.65 GiB
Saving trained LoRA weights.
Done 0
Training completed: 0
🎉 Model saved successfully!
📁 Saved files:
  📄 model_state_dict.safetensors

import os
import zipfile

def download_model():
    """Package the model for download"""
    if os.path.exists("uganda_clinical_model"):
        print("📦 Packaging model for download...")
        
        # Create a zip file
        with zipfile.ZipFile("uganda_clinical_qdora_model.zip", "w", zipfile.ZIP_DEFLATED) as zipf:
            for root, dirs, files in os.walk("uganda_clinical_model"):
                for file in files:
                    file_path = os.path.join(root, file)
                    zipf.write(file_path, file_path)
        
        print("✅ Model packaged as uganda_clinical_qdora_model.zip")
        print(f"📊 File size: {os.path.getsize('uganda_clinical_qdora_model.zip') / 1024 / 1024:.1f} MB")
        
        # In Jupyter, this will be available for download
        print("💾 You can download this file from the Jupyter file browser")
        
    else:
        print("❌ No model directory found. Run training with --save_model true first")

download_model()

📦 Packaging model for download...
✅ Model packaged as uganda_clinical_qdora_model.zip
📊 File size: 217.9 MB
💾 You can download this file from the Jupyter file browser

def test_model():
    """Test the trained model"""
    
    # Load the model for inference
    from transformers import AutoTokenizer, AutoModelForCausalLM
    import torch
    
    model_path = "./uganda_clinical_model"
    
    if not os.path.exists(model_path):
        print("❌ Model not found. Train with --save_model true first")
        return
    
    print("🔄 Loading trained model...")
    
    try:
        # Load tokenizer and model
        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            torch_dtype=torch.float16,
            device_map="auto"
        )
        
        print("✅ Model loaded successfully!")
        
        # Test with a medical question
        test_prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
I have fever, general body weakness, joint paints and have been getting by mosquitoes often. what could be the cause ?

### Response:"""
        
        print("\n🧪 Testing model...")
        print("Question: What are the symptoms of malaria?")
        print("\nModel Response:")
        
        # Generate response
        inputs = tokenizer(test_prompt, return_tensors="pt")
        
        with torch.no_grad():
            outputs = model.generate(
                inputs.input_ids,
                max_length=inputs.input_ids.shape[1] + 150,
                temperature=0.7,
                do_sample=True,
                pad_token_id=tokenizer.eos_token_id
            )
        
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        # Extract just the response part
        response_only = response.split("### Response:")[-1].strip()
        print(response_only)
        
    except Exception as e:
        print(f"❌ Error loading model: {e}")

# Run after you've saved the model
test_model()

🔄 Loading trained model...
❌ Error loading model: Unrecognized model in ./uganda_clinical_model. Should have a `model_type` key in its config.json, or contain one of the following strings in its name: albert, align, altclip, audio-spectrogram-transformer, autoformer, bark, bart, beit, bert, bert-generation, big_bird, bigbird_pegasus, biogpt, bit, blenderbot, blenderbot-small, blip, blip-2, bloom, bridgetower, bros, camembert, canine, chameleon, chinese_clip, chinese_clip_vision_model, clap, clip, clip_text_model, clip_vision_model, clipseg, clvp, code_llama, codegen, cohere, conditional_detr, convbert, convnext, convnextv2, cpmant, ctrl, cvt, dac, data2vec-audio, data2vec-text, data2vec-vision, dbrx, deberta, deberta-v2, decision_transformer, deformable_detr, deit, depth_anything, deta, detr, dinat, dinov2, distilbert, donut-swin, dpr, dpt, efficientformer, efficientnet, electra, encodec, encoder-decoder, ernie, ernie_m, esm, falcon, falcon_mamba, fastspeech2_conformer, flaubert, flava, fnet, focalnet, fsmt, funnel, fuyu, gemma, gemma2, git, glm, glpn, gpt-sw3, gpt2, gpt_bigcode, gpt_neo, gpt_neox, gpt_neox_japanese, gptj, gptsan-japanese, granite, granitemoe, graphormer, grounding-dino, groupvit, hiera, hubert, ibert, idefics, idefics2, idefics3, ijepa, imagegpt, informer, instructblip, instructblipvideo, jamba, jetmoe, jukebox, kosmos-2, layoutlm, layoutlmv2, layoutlmv3, led, levit, lilt, llama, llava, llava_next, llava_next_video, llava_onevision, longformer, longt5, luke, lxmert, m2m_100, mamba, mamba2, marian, markuplm, mask2former, maskformer, maskformer-swin, mbart, mctct, mega, megatron-bert, mgp-str, mimi, mistral, mixtral, mllama, mobilebert, mobilenet_v1, mobilenet_v2, mobilevit, mobilevitv2, moshi, mpnet, mpt, mra, mt5, musicgen, musicgen_melody, mvp, nat, nemotron, nezha, nllb-moe, nougat, nystromformer, olmo, olmo2, olmoe, omdet-turbo, oneformer, open-llama, openai-gpt, opt, owlv2, owlvit, paligemma, patchtsmixer, patchtst, pegasus, pegasus_x, perceiver, persimmon, phi, phi3, phimoe, pix2struct, pixtral, plbart, poolformer, pop2piano, prophetnet, pvt, pvt_v2, qdqbert, qwen2, qwen2_audio, qwen2_audio_encoder, qwen2_moe, qwen2_vl, rag, realm, recurrent_gemma, reformer, regnet, rembert, resnet, retribert, roberta, roberta-prelayernorm, roc_bert, roformer, rt_detr, rt_detr_resnet, rwkv, sam, seamless_m4t, seamless_m4t_v2, segformer, seggpt, sew, sew-d, siglip, siglip_vision_model, speech-encoder-decoder, speech_to_text, speech_to_text_2, speecht5, splinter, squeezebert, stablelm, starcoder2, superpoint, swiftformer, swin, swin2sr, swinv2, switch_transformers, t5, table-transformer, tapas, time_series_transformer, timesformer, timm_backbone, trajectory_transformer, transfo-xl, trocr, tvlt, tvp, udop, umt5, unispeech, unispeech-sat, univnet, upernet, van, video_llava, videomae, vilt, vipllava, vision-encoder-decoder, vision-text-dual-encoder, visual_bert, vit, vit_hybrid, vit_mae, vit_msn, vitdet, vitmatte, vits, vivit, wav2vec2, wav2vec2-bert, wav2vec2-conformer, wavlm, whisper, xclip, xglm, xlm, xlm-prophetnet, xlm-roberta, xlm-roberta-xl, xlnet, xmod, yolos, yoso, zamba, zoedepth

import os

def inspect_saved_model():
    """Check what files were actually saved"""
    model_dir = "./uganda_clinical_model"
    
    if os.path.exists(model_dir):
        print("📁 Files in uganda_clinical_model:")
        for file in os.listdir(model_dir):
            file_path = os.path.join(model_dir, file)
            size = os.path.getsize(file_path) / 1024 / 1024  # MB
            print(f"  📄 {file} ({size:.1f} MB)")
        
        # Check for specific files
        expected_files = ["adapter_config.json", "adapter_model.bin", "adapter_model.safetensors"]
        for expected in expected_files:
            if os.path.exists(os.path.join(model_dir, expected)):
                print(f"✅ Found: {expected}")
            else:
                print(f"❌ Missing: {expected}")
    else:
        print("❌ Model directory not found")

inspect_saved_model()

📁 Files in uganda_clinical_model:
  📄 model_state_dict.safetensors (275.4 MB)
❌ Missing: adapter_config.json
❌ Missing: adapter_model.bin
❌ Missing: adapter_model.safetensors