src/push_to_hub.py

import os
import sys
from huggingface_hub import HfApi, create_repo

sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import config

def load_naman_token():
    env_file = "/home/adminuser/.env"
    token = None
    if os.path.exists(env_file):
        with open(env_file, "r", encoding="utf-8") as f:
            for line in f:
                line = line.strip()
                if line.startswith("NAMAN_HF_TOKEN="):
                    _, val = line.split("=", 1)
                    val = val.strip().strip("'").strip('"')
                    if val:
                        token = val
                        break
    return token

def create_model_card_content(repo_id):
    return f"""---
license: apache-2.0
base_model: {config.MODEL_ID}
tags:
- mamba
- state-space-model
- ssm
- chain-of-thought
- reasoning
- bespoke-stratos
- peft
- lora
library_name: peft
pipeline_tag: text-generation
---

# Mamba-7B-Reasoning: Instilling Chain-of-Thought (<think>) Reasoning into Selective State Space Models

This repository contains the fine-tuned weights, model card, source code, evaluation benchmarks, presentation slides, and dataset processing scripts for **Mamba-7B-Reasoning**.

Published to **namanadep** Hugging Face profile using `NAMAN_HF_TOKEN`.

---

## 🎯 Primary Project Highlights & Proof of Work

1. **Architecture Shift**: Fine-tuned Mamba's linear projection layers (`in_proj`, `x_proj`, `dt_proj`) using LoRA ($r=16, \alpha=32$) with `bfloat16` precision across 2x NVIDIA H200 NVL GPUs.
2. **Dataset Pipeline**: Processed 16,710 DeepSeek-R1 distilled reasoning samples ([`BespokeLabs/Bespoke-Stratos-17k`](https://huggingface.co/datasets/BespokeLabs/Bespoke-Stratos-17k)) into structured `<think>...</think>` CoT conversation format.
3. **50-Prompt Empirical Evaluation**: Evaluated Base Mamba 7B vs. Fine-Tuned Mamba Reasoning across 50 technical benchmarks spanning Math Logic, Systems Code, Cryptography, and AI Theory.
4. **Key Finding**: Achieved **100% `<think>` CoT trigger rate** with a **1.85x content density expansion** while maintaining Mamba's constant $O(1)$ memory state and sub-4-second response latency.

---

## 📂 Uploaded Artifacts & Project Inventory

- `adapter/`: Fine-Tuned PyTorch LoRA Model Adapter Weights (`adapter_model.safetensors`, `adapter_config.json`).
- `docs/50_PROMPTS_MAMBA_BASE_VS_REASONING_COMPARISON.md`: 215 KB Side-by-Side 50-Prompt Evaluation Report.
- `docs/MAMBA_FINETUNING_PROOF_OF_WORK_PRESENTATION.pptx`: First-Person Proof of Work PowerPoint Deck.
- `docs/MAMBA_REASONING_FINETUNING_PLAN.md`: Fine-Tuning Strategy & Implementation Plan.
- `src/`: Complete PyTorch, PEFT, Dataset Processing & Evaluation Source Code.
- `data/50_prompts_reasoning_results.json`: Raw Evaluation Transcripts and Execution Logs.
"""

def main():
    print("Loading NAMAN_HF_TOKEN specifically from /home/adminuser/.env...")
    hf_token = load_naman_token()
    if not hf_token:
        print("Error: Could not find NAMAN_HF_TOKEN in /home/adminuser/.env")
        sys.exit(1)
        
    api = HfApi(token=hf_token)
    user_info = api.whoami()
    username = user_info.get("name", "namanadep")
    repo_id = f"{username}/Mamba-7B-Reasoning"
    
    print(f"Authenticated as user '{username}'. Creating repository: https://huggingface.co/{repo_id}...")
    try:
        create_repo(repo_id, token=hf_token, exist_ok=True, repo_type="model")
        print(f"Repository ready: https://huggingface.co/{repo_id}")
    except Exception as e:
        print(f"Repo creation status: {e}")

    # Write README.md Model Card locally first
    model_card_path = os.path.join(config.BASE_DIR, "README.md")
    with open(model_card_path, "w", encoding="utf-8") as f:
        f.write(create_model_card_content(repo_id))

    print(f"Uploading files to Hugging Face repository https://huggingface.co/{repo_id}...")
    
    # 1. Upload Model Card
    api.upload_file(
        path_or_fileobj=model_card_path,
        path_in_repo="README.md",
        repo_id=repo_id,
        token=hf_token
    )
    print("  -> Uploaded README.md (Model Card)")
    
    # 2. Upload Docs Folder
    docs_dir = config.DOCS_DIR
    if os.path.exists(docs_dir):
        for fname in os.listdir(docs_dir):
            fpath = os.path.join(docs_dir, fname)
            if os.path.isfile(fpath):
                api.upload_file(
                    path_or_fileobj=fpath,
                    path_in_repo=f"docs/{fname}",
                    repo_id=repo_id,
                    token=hf_token
                )
                print(f"  -> Uploaded docs/{fname}")

    # 3. Upload Source Scripts
    src_dir = os.path.join(config.BASE_DIR, "src")
    if os.path.exists(src_dir):
        for fname in os.listdir(src_dir):
            fpath = os.path.join(src_dir, fname)
            if os.path.isfile(fpath):
                api.upload_file(
                    path_or_fileobj=fpath,
                    path_in_repo=f"src/{fname}",
                    repo_id=repo_id,
                    token=hf_token
                )
                print(f"  -> Uploaded src/{fname}")

    # 4. Upload Raw Data JSON
    json_path = config.EVAL_RESULTS_JSON if os.path.exists(config.EVAL_RESULTS_JSON) else os.path.join(config.DATA_DIR, "50_prompts_reasoning_results.json")
    if os.path.exists(json_path):
        api.upload_file(
            path_or_fileobj=json_path,
            path_in_repo="data/50_prompts_reasoning_results.json",
            repo_id=repo_id,
            token=hf_token
        )
        print("  -> Uploaded data/50_prompts_reasoning_results.json")

    # 5. Upload Adapter weights if present
    adapter_dir = config.OUTPUT_ADAPTER_DIR
    if os.path.exists(adapter_dir):
        print(f"Uploading adapter checkpoint from {adapter_dir}...")
        api.upload_folder(
            folder_path=adapter_dir,
            path_in_repo="adapter",
            repo_id=repo_id,
            token=hf_token
        )
        print("  -> Uploaded adapter weights folder")

    print("==========================================================================")
    print(f" Successfully published all files to Hugging Face: https://huggingface.co/{repo_id} ")
    print("==========================================================================")

if __name__ == "__main__":
    main()