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Model: iselabvn/Tini-8B-A1B-GGUF
Source: Original Platform

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README.md

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+---
+base_model:
+- dungnvt/Tini-8B-A1B
+language:
+- en
+library_name: transformers
+tags:
+- gguf
+- lfm2_moe
+- reasoning
+- agent
+- function-calling
+pipeline_tag: text-generation
+---
+
+# Tini-8B-A1B-GGUF
+
+This repository contains GGUF format versions of the fine-tuned model **Tini-8B-A1B**, which is based on the **LiquidAI/LFM2.5-8B-A1B** architecture and optimized for agent reasoning and function-calling.
+
+## Files Available
+
+*   **Tini-8B-A1B-BF16.gguf** (15.78 GB): Unquantized Brain Float 16 base GGUF file.
+*   **Tini-8B-A1B-Q8_0.gguf** (8.39 GB): 8-bit standard quantization. High accuracy, recommended for general inference.
+*   **Tini-8B-A1B-Q6_K.gguf** (6.48 GB): 6-bit quantization. Good balance between size and perplexity.
+*   **Tini-8B-A1B-Q4_K_M.gguf** (4.80 GB): 4-bit Medium K-quantized model. Highly efficient resource usage.
+
+## Running the Model
+
+Since the `lfm2_moe` architecture is relatively new, make sure to use a recent version of `llama.cpp` or downstream tools (LM Studio, Ollama, etc.) that support this model type.
+
+### Using llama-cli
+
+You can run the model directly using `llama-cli`:
+
+```bash
+llama-cli -m Tini-8B-A1B-Q8_0.gguf -p "<|im_start|>user\nHello, how can you help me today?<|im_end|>\n<|im_start|>assistant\n"
+```
+
+## Model Architecture Details
+
+*   **Architecture**: `Lfm2MoeForCausalLM`
+*   **Experts**: 32 experts (MoE)
+*   **Experts per Token**: 4 active experts
+*   **Context Window**: Up to 128k tokens

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