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Model: Glubbot-Team/glubbot1.1-7.5m
Source: Original Platform

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Modelfile

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+FROM F:\GlubBot1.1-19m\gits\models 

README.md

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+---
+license: apache-2.0
+tags:
+- text-generation
+- experimental
+- ultra-lightweight
+- tiny-stories
+model_type: llm
+language:
+- en
+datasets:
+- roneneldan/TinyStories
+metrics:
+- perplexity
+---
+
+# glubbot1.1-7.5m
+
+## Model Description
+glubbot1.1-7.5m is an experimental, ultra-lightweight text generation model developed by the **Glubbot-Team**. 
+
+With only 7.5 million parameters, this model is designed for experimental testing, low-resource inference benchmarking, and studying language acquisition in small-scale models. It was trained specifically to generate short, grammatically correct narratives.
+
+- **Developed by:** Glubbot-Team
+- **Model Type:** Transformer-based Causal Language Model
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Status:** Experimental / Testing
+
+## Intended Uses & Limitations
+This model is intended solely for research, architectural testing, and experimental evaluation. 
+
+### Intended Use
+- Synthetic text generation for short, child-like stories.
+- Benchmarking inference speeds on edge devices or CPUs.
+- Testing tokenization strategies and pipeline integration.
+
+### Limitations & Biases
+- **Domain Specificity:** Optimized purely for short stories; it cannot handle reasoning, coding, or factual factual queries.
+- **Limited Capacity:** Due to the 7.5M parameter size, the model may produce repetitive structures or drift logically during long generations.
+- **Not for Production:** Do not deploy this model for general-purpose assistant tasks.
+
+## Training Details
+
+### Training Data
+The model was trained on the **TinyStories** dataset (`roneneldan/TinyStories`). This dataset consists of short stories containing words typically understood by 3- to 4-year-olds, optimized to teach models basic grammar and reasoning.
+
+### Hyperparameters
+- **Context Length:** [e.g., 512 / 1024]
+- **Batch Size:** [Insert Batch Size]
+- **Learning Rate:** [Insert Learning Rate]
+- **Optimizer:** AdamW
+
+## Evaluation
+Evaluation metrics are monitored primarily to test convergence stability on the TinyStories split. The model is evaluated on its ability to maintain basic grammatical coherence.

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "dtype": "float32",
+  "eos_token_id": 3,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 256,
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "max_position_embeddings": 512,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 4,
+  "num_key_value_heads": 4,
+  "pad_token_id": null,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.8.0",
+  "use_cache": true,
+  "vocab_size": 8192
+}

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