Official Release: Optimized & Sanitized

README.md

@@ -4,95 +4,80 @@ language:
   - en
 pipeline_tag: text-generation
 tags:
+  - deepbrainz
   - reasoning
-  - long-context
+  - mathematics
+  - code
   - enterprise
-  - research
+  - 2b
+library_name: transformers
 ---
 
 # DeepBrainz-R1-2B-16K
 
-**DeepBrainz-R1-2B-16K** is a compact, long-context reasoning model in the
-DeepBrainz-R series, designed for structured problem-solving, analysis,
-and enterprise research workflows.
+**DeepBrainz-R1-2B-16K** is a compact, high-performance reasoning model engineered by **DeepBrainz AI & Labs**. Designed for efficiency and scalability, it specializes in structured chain-of-thought reasoning, mathematical problem solving, and logical analysis.
 
-The model emphasizes **reasoning quality**, **instruction robustness**,
-and **stable behavior over long contexts**, while remaining highly
-cost-efficient to deploy.
+This model is part of the **DeepBrainz-R1 Series**, built to deliver frontier-class reasoning capabilities in cost-effective parameter sizes.
 
 ---
 
-## Model Highlights
+## 🚀 Model Highlights
 
-- **1.7B parameters**
-- **16K context length**
-- Optimized for reasoning-centric math and coding tasks
-- Designed for modern GPU inference runtimes
-- **Architecture:** Qwen3-compatible (DeepBrainz-R series post-trained and optimized for reasoning-centric workloads)
+- **Parameter Count:** ~2B
+- **Context Window:** 16,384 tokens
+- **Specialization:** STEM Reasoning, Logic, Code Analysis
+- **Architecture:** Optimized Dense Transformer (Qwen2.5/3 Compatible)
+- **Deployment:** Ready for vLLM, TGI, and local inference
 
 ---
 
-## Intended Use
+## 🎯 Intended Use Cases
 
-- Advanced reasoning systems
-- Math and Coding
-- Research and evaluation
-- Agentic workflows
-- Inference-time scaling and test-time compute experiments
+- **Agentic Workflows:** Reliability in multi-step planning tasks.
+- **Math & Science:** Solving complex word problems and equations.
+- **Code Generation:** Writing and debugging algorithms.
+- **Structured Data Extraction:** Parsing and reasoning over unstructured text.
 
-Not intended as a general-purpose chat replacement for large frontier models.
+> **Note:** This is a base reasoning model. For conversational chat, we recommend using a specific instruct template or fine-tuning on your domain data.
 
 ---
 
-## Usage
+## 💻 Usage
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 model_id = "DeepBrainz/DeepBrainz-R1-2B-16K"
 
-tok = AutoTokenizer.from_pretrained(model_id)
-mdl = AutoModelForCausalLM.from_pretrained(model_id)
-
-prompt = "Solve step by step: If x + 5 = 12, what is x?"
-inputs = tok(prompt, return_tensors="pt")
-
-out = mdl.generate(
-    **inputs,
-    max_new_tokens=256,
-    do_sample=True,
-    temperature=0.6,
-    top_p=0.95,
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="bfloat16",
+    device_map="auto"
 )
 
-print(tok.decode(out[0], skip_special_tokens=True))
-````
-
----
-
-## Training Summary
+prompt = "Analyze the time complexity of the following algorithm:"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 
-The model was produced using a multi-stage optimization process involving
-large-scale on-policy optimization and iterative refinement to improve reasoning
-quality and robustness. Specific training details are intentionally
-abstracted in this public release.
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
 ---
 
-## Limitations
+## 🛡️ Limitations & Safety
 
-Performance depends on task complexity and inference configuration.
-Larger models may outperform R1-2B-16K on extremely complex tasks.
+While this model demonstrates strong reasoning capabilities, it may still produce inaccurate information ("hallucinations"). Users should implement appropriate guardrails for production deployments.
 
 ---
 
-## License
+## 📜 License
 
-Apache 2.0
+This model is released under the **Apache 2.0** license, allowing for academic and commercial use.
 
 ---
 
-## About DeepBrainz
-
-DeepBrainz builds reasoning-first AI systems focused on efficiency,
-structure, and real-world problem-solving.
+<div align="center">
+  <b>DeepBrainz AI & Labs</b><br>
+  <i>Advancing General Intelligence through Scalable Reasoning</i>
+</div>

config.json

@@ -10,20 +10,9 @@
   "num_key_value_heads": 8,
   "head_dim": 128,
   "max_position_embeddings": 16384,
-  "rms_norm_eps": 1e-06,
-  "rope_theta": 1000000.0,
-  "rope_scaling": null,
-  "attention_bias": false,
-  "attention_dropout": 0.0,
-  "hidden_act": "silu",
-  "initializer_range": 0.02,
-  "tie_word_embeddings": false,
-  "torch_dtype": "bfloat16",
-  "transformers_version": "4.45.0",
-  "use_cache": true,
-  "use_sliding_window": false,
   "vocab_size": 151936,
+  "torch_dtype": "bfloat16",
   "bos_token_id": 151643,
   "eos_token_id": 151645,
-  "pad_token_id": 151643
+  "tie_word_embeddings": false
 }

model.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:f0e372a0e340be710b39af4b474bd224d0df9b6e813e907510ae0b5dea92f6e9
-size 8126995104
+oid sha256:9a0df1e48b33a5bcc4ec77820f8c6c3b778c4734d10cc11275cf241ee21cbb92
+size 4063515608