kshitijthakkar
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loggenix-moe-0.12B-A0.08B-e5-lr5e4-b16-3060-v2-finetuned

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 ---
 library_name: transformers
-tags: []
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+tags:
+- qwen3
+- qwen3moe
+- mixture-of-experts
+- llm
+- text-generation
+- instruction-following
+- agentic-ai
+- tool-use
+- low-resource
+- edge-ai
+- from-scratch
+- causal-lm
+license: apache-2.0
+datasets:
+- kshitijthakkar/loggenix-mc-oraca-agentinstruct-1m-v1
+---
+# 🧠 LoggenixMoE133M: A Lightweight Mixture-of-Experts Language Model (8E2A)
+[![Model Size](https://img.shields.io/badge/Parameters-133M-blue)]()
+[![Experts](https://img.shields.io/badge/Experts-8-lightgrey)]()
+[![Routing](https://img.shields.io/badge/Active_Experts-2-orange)]()
+[![Model Size](https://img.shields.io/badge/ActiveParameters-80M-red)]()
+[![License: Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-green.svg)](https://www.apache.org/licenses/LICENSE-2.0)
+---
+## 📝 Model Card
+**LoggenixMoE133M** is a small Mixture-of-Experts (MoE) Causal Language Model trained **from scratch** on a custom dataset containing root cause analysis (RCA), code generation, and reasoning tasks.
+- **Architecture**: A lightweight transformer with Mixture-of-Experts routing, **inspired by the innovative architectural design of Qwen3 models.**
+- **Parameter Count**: 133M total, with 2 experts active per token (approx. 80M active per step).
+- **Experts**: 8 total, gated per token with router logits.
+- **Activation Strategy**: Top-2 routing with auxiliary routing loss.
+- **Tokenizer Features**: Crucially, the tokenizer includes dedicated special tokens for agentic capabilities: `<tool_call>` and `<think>`. These tokens are designed to facilitate advanced reasoning, planning, and interaction with external tools, enabling the model to serve as a foundational component for building robust AI agents.
+---
+## 📊 Training Details
+| Attribute               | Value                                          |
+|------------------------|------------------------------------------------|
+| Total Params           | 133M                                           |
+| MoE Config             | 8 experts, top-2 gating                        |
+| Dataset Type           | RCA, code, and logic prompts (15+ task splits) |
+| Training Epochs        | 5                                              |
+| Effective Tokens Seen  | 1.5 Billion                                    |
+| Train Loss (final)     | 3.263                                          |
+| Val Loss (final)       | 3.327                                          |
+| Mean Token Accuracy    | ~48%                                           |
+| Optimizer              | AdamW                                          |
+| Scheduler              | Linear Warmup + Cosine Decay                   |
+| Precision              | FP16 with GradScaler                           |
+| Checkpoint Format      | HF-compatible                                  |
+| Training Cost          | $94 across Modal (A100 40GB) + Hyperbolic (RTX 4090) |
+| Context Length         | 1024                                           |
+---
+## 🧪 Intended Use
+### ✅ Suitable for:
+- Instruction-following tasks
+- Root cause analysis (RCA) and structured summarization
+- Lightweight code generation (Python)
+- Chain-of-thought style reasoning prompts
+- **Fine-tuning for specific tasks on edge devices** (e.g., smart home voice assistants, mobile offline chatbots, industrial IoT anomaly detection)
+- **Building specialized AI agents** that can reason, plan, and interact with external tools (e.g., automated customer support, workflow automation, personalized learning agents)
+### 🚫 Not suitable for:
+- Long-context tasks (>4K tokens)
+- High-stakes factual QA
+- Safety-critical decision-making without oversight
+---
+## 🧨 Example Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("kshitijthakkar/loggenix-moe-0.12B-A0.08B-e5-lr5e4-b4-3060")
+messages = [
+   {
+       "content": "",
+       "role": "system"
+   },
+   {
+       "content": "Write a Python function to compute factorial.",
+        "role": "user"
+    }
+ ]
+# Tokenizer
+tokenizer.pad_token = tokenizer.eos_token
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
+model = AutoModelForCausalLM.from_pretrained("kshitijthakkar/loggenix-moe-0.12B-A0.08B-e5-lr5e4-b4-3060", device_map="auto")
+memory = model.get_memory_footprint() / 1e6
+print(f"Memory footprint: {memory:,.1f} MB")
+model
+outputs = model.generate(inputs, do_sample=True,use_cache=False,max_new_tokens=512)
+print(tokenizer.decode(outputs[0]))
+## Alternatively
+with torch.no_grad():
+  outputs = model.generate(
+                       inputs,
+                        max_new_tokens=50,  # Reduced for testing
+                        do_sample=True,
+                        temperature=0.5,
+                        top_p=0.95,
+                        return_dict_in_generate=True,
+                        use_cache=False  # Disable caching to avoid potential issues
+                    )
+  generated_text = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
+  print(generated_text)
+```
+---
+🔧 Expert Routing
+---
+This model uses a top-2 gating mechanism where, for each token, two of the eight experts are selected based on learned router logits.
+During training, a light auxiliary loss was applied to encourage balanced expert usage and improve routing stability.
+Note: Routing logits are optionally available in the model outputs via output_router_logits=True.
+---
+📃 License
+---
+This model is released under the Apache 2.0 License.
+---
+🙌 Acknowledgements
+---
+Trained using:
+---
+🧨 Hugging Face Transformers
+🧠 Custom training loop with gradient checkpointing
+🧮 NVIDIA RTX 4090 (24GB VRAM) / A100 (40GB)
+📦 Logged and tracked via Weights & Biases
+---
+### 🗣️ Citation
+---
+@misc{loggenix-moe-0.12B-A0.08B-e5-lr5e4-b4-3060,
+  title = {loggenix-moe-0.12B-A0.08B-e5-lr5e4-b4-3060: A Lightweight Mixture-of-Experts Model},
+  author = {kshitijthakkar},
+  year = {2025},
+  url = {https://huggingface.co/kshitijthakkar/loggenix-moe-0.12B-A0.08B-e5-lr5e4-b4-3060 },
+  note = {Trained from scratch on RCA + code + reasoning dataset.}
+}
+---