Correct README base model to Qwen3.5-4B

README.md

@@ -19,7 +19,7 @@ metrics:
 pipeline_tag: text-generation
 inference: false
 base_model:
-- Qwen/Qwen3.5-27B
+- Qwen/Qwen3.5-4B
 ---
 
 
@@ -27,7 +27,7 @@ base_model:
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 <strong>Update Notice</strong><br>
-This release has been retrained on the same dataset with the base model upgraded to <strong>Qwen3.5-27B</strong>. If any section below has not yet been fully refreshed, this notice takes precedence.
+This release has been retrained on the same dataset with the base model upgraded to <strong>Qwen3.5-4B</strong>. If any section below has not yet been fully refreshed, this notice takes precedence.
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@@ -65,12 +65,12 @@ The DMind lineage was born from a singular conviction that decentralized finance
 
 * **Model Name:** DMind-3-mini
 * **Organization:** DMind
-* **Base Architecture:** Qwen3.5-27B (Customized Transformer w/ RoPE)
-* **Parameter Count:** 27 Billion
+* **Base Architecture:** Qwen3.5-4B (Customized Transformer w/ RoPE)
+* **Parameter Count:** 4.2 Billion
 * **Precision:** **BF16 (Native)**
     * *⚠️ Note: We strictly advise against 4-bit quantization for financial logic tasks to preserve numerical precision in APY/IL calculations.*
 * **Context Window:** 128k tokens
-* **Hardware Requirement:** BF16 deployment is recommended on a single **80GB-class GPU** (such as A100/H100) or an equivalent multi-GPU setup.
+* **Hardware Requirement:** GPU with $\ge$ **12GB VRAM** (Recommended: NVIDIA RTX 4070Ti+, Apple M3/M4 Pro/Max).
 
 ## 3. 🔬 Methodology: C³-SFT
 
@@ -140,10 +140,10 @@ Evaluated on three key benchmarks: DMind Benchmark (Web3 Native Logic), FinanceQ
 
 ![Figure 3: Performance Benchmarks](./Figures/Figure3.png)
 
-The evaluation compares the current DMind-3-mini release, now based on Qwen3.5-27B, against top-tier frontier models (GPT-5.1, Claude Sonnet 4.5) and other strong baselines. This version prioritizes deeper reasoning capacity and domain performance over ultra-lightweight deployment, especially in specialized financial tasks.
+The evaluation compares DMind-3-mini (4B) against top-tier frontier models (GPT-5.1, Claude Sonnet 4.5) and other efficient models. Despite its compact size, the Mini model demonstrates exceptional efficiency, particularly in specialized domain tasks where it outperforms significantly larger generalist models.
 
 ## 7. ⚖️ Limitations & Disclaimer
 
-* **High Hardware Barrier:** Due to the decision to retain BF16 precision for financial accuracy, straightforward deployment typically requires **80GB-class GPU memory or an equivalent multi-GPU setup**. It is not suitable for standard office laptops.
+* **High Hardware Barrier:** Due to the decision to retain BF16 precision for financial accuracy, this model **requires >= 12GB VRAM**. It is not suitable for standard office laptops.
 * **Knowledge Cutoff:** While the logic is robust, specific protocol data is limited to the training cutoff. Use with RAG for real-time data.
 * **Legal Disclaimer:** This model is an **analytical tool**, not a financial advisor. The output (NFA) should never be the sole basis for investment decisions. The developers assume no liability for financial losses.