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Model: prithivMLmods/Oganesson-TinyLlama-1.2B
Source: Original Platform

.gitattributes

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README.md

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+---
+library_name: transformers
+tags:
+- text-generation-inference
+- code
+- llama-3.2
+- math
+- general-purpose
+license: llama3.2
+language:
+- en
+base_model:
+- meta-llama/Llama-3.2-1B
+pipeline_tag: text-generation
+---
+![8.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/K_bYZlzTOZjl5YJnjEy8j.png)
+
+# **Oganesson-TinyLlama-1.2B**
+
+> **Oganesson-TinyLlama-1.2B** is a lightweight and efficient language model built on the **LLaMA 3.2 1.2B** architecture. Fine-tuned for **general-purpose inference**, **mathematical reasoning**, and **code generation**, it’s ideal for edge devices, personal assistants, and educational applications requiring a compact yet capable model.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/Oganesson-TinyLlama-1.2B-GGUF](https://huggingface.co/prithivMLmods/Oganesson-TinyLlama-1.2B-GGUF)
+
+---
+
+## **Key Features**
+
+1. **LLaMA 3.2 1.2B Core**
+   Powered by the latest **TinyLLaMA (1.2B)** variant of Meta's LLaMA 3.2, offering modern instruction-following and multilingual capabilities in a very small footprint.
+
+2. **Modular Fine-Tuning**
+   Trained on a handcrafted modular dataset covering general-purpose reasoning, programming problems, and mathematical challenges.
+
+3. **Mathematical Competence**
+   Solves equations, explains concepts, and performs symbolic logic in algebra, geometry, and calculus—ideal for lightweight tutoring use cases.
+
+4. **Code Understanding & Generation**
+   Produces clean, interpretable code in Python, JavaScript, and more. Useful for micro-agents, code assistants, and embedded development tools.
+
+5. **Versatile Output Formats**
+   Handles JSON, Markdown, LaTeX, and structured data output, enabling integration into tools and platforms needing formatted results.
+
+6. **Edge-Optimized**
+   At only 1.2B parameters, this model is built for **local inference**, **on-device usage**, and **battery-efficient environments**.
+
+---
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Oganesson-TinyLlama-1.2B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Write a Python function to compute the Fibonacci sequence."
+
+messages = [
+    {"role": "system", "content": "You are a helpful coding and math assistant."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+---
+
+## **Intended Use**
+
+* Lightweight reasoning for embedded and edge AI
+* Basic math tutoring and symbolic computation
+* Code generation and explanation for small apps
+* Technical content in Markdown, JSON, and LaTeX
+* Educational tools, personal agents, and low-power deployments
+
+---
+
+## **Limitations**
+
+* Smaller context window than 7B+ models
+* Less suitable for abstract reasoning or creative writing
+* May require prompt engineering for complex technical queries
+* Knowledge is limited to pretraining and fine-tuning datasets
+
+---
+
+## **References**
+
+1. [LLaMA 3 Technical Report (Meta)](https://ai.meta.com/llama/)
+2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)

chat_template.jinja

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+{{bos_token}}{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|start_header_id|>system<|end_header_id|>
+
+You are MiniThinky, a helpful AI assistant. You always think before giving the answer. Use <|thinking|> before thinking and <|answer|> before giving the answer.<|eot_id|>' }}{% endif %}{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>
+
+' + message['content'] + '<|eot_id|>' }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>
+
+' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers.js_config": {
+    "kv_cache_dtype": {
+      "fp16": "float16",
+      "q4f16": "float16"
+    }
+  },
+  "transformers_version": "4.53.0.dev0",
+  "use_cache": true,
+  "vocab_size": 128256
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.53.0.dev0"
+}

model.safetensors

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|finetune_right_pad_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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