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Model: DeepBrainz/DeepBrainz-R1-4B
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+  - en
+pipeline_tag: text-generation
+tags:
+  - deepbrainz
+  - reasoning
+  - mathematics
+  - code
+  - enterprise
+  - 4b
+  - long-context
+  - 32k
+library_name: transformers
+
+---
+
+### 🚀 Introducing DeepBrainz-R1 — Reasoning-First Small Language Models for Agentic Systems
+
+Today we’re releasing **DeepBrainz-R1**, a family of **reasoning-first Small Language Models (SLMs)** designed for **agentic AI systems in real-world production**.
+
+Agentic systems don’t ask once — they reason repeatedly. Tool calls, verification loops, schema-constrained outputs, retries, and long-context planning fundamentally change the economics and reliability requirements of language models. LLM-only stacks struggle under this load.
+
+DeepBrainz-R1 is built from the opposite premise:
+
+> **Reasoning is a trained behavior, not an emergent side-effect of scale.**
+
+#### What DeepBrainz-R1 is designed for
+
+* **Repeatable multi-step reasoning**, not one-shot chat
+* **Agent-compatible behavior**: tool use, structured outputs, low-variance reasoning
+* **Production economics**: lower latency, predictable cost, deployability
+* **Inference-time scalability**: compute where needed, not everywhere
+
+#### The R1 lineup
+
+* **[DeepBrainz-R1-4B](https://huggingface.co/DeepBrainz/DeepBrainz-R1-4B)** — *Flagship production model*
+  Best starting point for reliable agentic systems.
+* **[DeepBrainz-R1-2B](https://huggingface.co/DeepBrainz/DeepBrainz-R1-2B)** — *Balanced production model*
+  Strong reasoning with lower cost and latency.
+* **[DeepBrainz-R1-0.6B-v2](https://huggingface.co/DeepBrainz/DeepBrainz-R1-0.6B-v2)** — *Canonical small model*
+  Cost-efficient baseline for small-model agent workloads.
+* **[Long-context variants (16K / 40K)](https://huggingface.co/collections/DeepBrainz/deepbrainz-r1-reasoning-first-slms-for-agentic-systems)** — early and experimental
+* **[Research checkpoints](https://huggingface.co/collections/DeepBrainz/deepbrainz-r1-research-checkpoints)** — raw artifacts for ablation and evaluation
+* **[Community quantizations (GGUF, low-bit)](https://huggingface.co/collections/DeepBrainz/deepbrainz-r1-community-quantizations-gguf-and-low-bit)** — community-maintained, not officially supported
+
+We publish **supported releases, experimental variants, and research checkpoints separately** to keep expectations clear for builders, enterprises, and researchers.
+
+#### Why now
+
+2026 is the year agentic AI stops being a demo and starts becoming infrastructure. Infrastructure cannot rely on LLM-only economics or LLM-only reliability.
+**Reasoning-first SLMs are the only viable path to scaling agents sustainably.**
+
+— **DeepBrainz AI & Labs**
+
+---
+
+# DeepBrainz-R1-4B
+
+**DeepBrainz-R1-4B** is a compact, high-performance reasoning model engineered by **DeepBrainz AI & Labs**. It is part of the **DeepBrainz-R1 Series**, designed to deliver frontier-class reasoning capabilities in cost-effective parameter sizes.
+
+This variant offers an extended context window (up to 32,768 tokens), making it suitable for medium-length document and code analysis.
+
+---
+
+## 🚀 Model Highlights
+
+- **Parameter Count:** ~4B
+- **Context Window:** 32,768 tokens
+- **Context Type:** Extended (RoPE)
+- **Specialization:** STEM Reasoning, Logic, Code Analysis
+- **Architecture:** Optimized Dense Transformer
+- **Deployment:** Ready for vLLM, SGLang, and local inference
+
+---
+
+## 🎯 Intended Use Cases
+
+- **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.
+
+> **Note:** This model has undergone post-training to enhance reasoning quality and agentic reliability.  
+> It is not optimized for open-ended conversational chat without additional instruction tuning.
+
+---
+
+## 💻 Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "DeepBrainz/DeepBrainz-R1-4B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="bfloat16",
+    device_map="auto"
+)
+
+prompt = "Analyze the time complexity of the following algorithm:"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+---
+
+## 🏗️ Technical Summary
+
+The model has undergone **post-training** to improve reasoning quality, output stability, and robustness under agentic workloads.
+
+*Detailed post-training recipes and dataset compositions are not fully disclosed.*
+
+---
+
+## 🛡️ Limitations & Safety
+
+While this model demonstrates strong reasoning capabilities, it may still produce inaccurate information ("hallucinations"). Users should implement appropriate guardrails for production deployments.
+
+---
+
+## 📜 License
+
+This model is released under the **Apache 2.0** license, allowing for academic and commercial use.
+
+---
+
+<div align="center">
+  <b>DeepBrainz AI & Labs</b><br>
+  <i>Advancing General Intelligence through Scalable Reasoning</i>
+</div>

added_tokens.json

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+{
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+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
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+  "<|fim_pad|>": 151662,
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+  "<|vision_pad|>": 151654,
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+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "hidden_size": 2560,
+  "intermediate_size": 9728,
+  "num_hidden_layers": 36,
+  "num_attention_heads": 32,
+  "num_key_value_heads": 8,
+  "head_dim": 128,
+  "vocab_size": 151936,
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "model_type": "qwen3",
+  "max_position_embeddings": 32768,
+  "torch_dtype": "bfloat16",
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "tie_word_embeddings": false,
+  "rope_scaling": {
+    "type": "dynamic",
+    "factor": 2.0
+  }
+}

model.safetensors

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+oid sha256:d714b8badaee6a4c0e8c196e4bfb1ec226bcaf153e958318b9c57d252a264746
+size 8822894488

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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+      "lstrip": false,
+      "normalized": false,
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+    "<|im_start|>",
+    "<|im_end|>",
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+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
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+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
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+  "clean_up_tokenization_spaces": false,
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+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}