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Model: prithivMLmods/Security-Llama3.2-3B-GGUF
Source: Original Platform

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README.md

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+---
+license: llama3.2
+base_model:
+- viettelsecurity-ai/security-llama3.2-3b
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+---
+
+# **Security-Llama3.2-3B-GGUF**
+
+> security-llama3.2-3b is a dense, decoder-only Transformer model with approximately 3 billion parameters. It is optimized for generating text, particularly in response to prompts in a chat format, with a context length of up to 4,000 tokens. The model is specialized toward cybersecurity content, drawing on a mixture of publicly available blogs, papers, reference datasets (e.g. from the PEASEC cybersecurity repository), synthetic “textbook-style” data, and academic Q&A sources to enhance performance in security-themed tasks. For usage, the model accepts chat-style inputs (e.g. alternating “user” / “assistant” messages) and can be deployed via the Hugging Face transformers library (e.g. via pipeline("text-generation", model="viettelsecurity-ai/security-llama3.2-3b")). The model weights are stored in safetensors format, configured with fp16 (half precision), and no inference provider currently hosts it by default.
+
+## Model Files
+
+| File Name | Quant Type | File Size |
+| - | - | - |
+| security-llama3.2-3b.BF16.gguf | BF16 | 6.43 GB |
+| security-llama3.2-3b.F16.gguf | F16 | 6.43 GB |
+| security-llama3.2-3b.F32.gguf | F32 | 12.9 GB |
+| security-llama3.2-3b.Q2_K.gguf | Q2_K | 1.36 GB |
+| security-llama3.2-3b.Q3_K_L.gguf | Q3_K_L | 1.82 GB |
+| security-llama3.2-3b.Q3_K_M.gguf | Q3_K_M | 1.69 GB |
+| security-llama3.2-3b.Q3_K_S.gguf | Q3_K_S | 1.54 GB |
+| security-llama3.2-3b.Q4_0.gguf | Q4_0 | 1.92 GB |
+| security-llama3.2-3b.Q4_1.gguf | Q4_1 | 2.09 GB |
+| security-llama3.2-3b.Q4_K.gguf | Q4_K | 2.02 GB |
+| security-llama3.2-3b.Q4_K_M.gguf | Q4_K_M | 2.02 GB |
+| security-llama3.2-3b.Q4_K_S.gguf | Q4_K_S | 1.93 GB |
+| security-llama3.2-3b.Q5_0.gguf | Q5_0 | 2.27 GB |
+| security-llama3.2-3b.Q5_1.gguf | Q5_1 | 2.45 GB |
+| security-llama3.2-3b.Q5_K.gguf | Q5_K | 2.32 GB |
+| security-llama3.2-3b.Q5_K_M.gguf | Q5_K_M | 2.32 GB |
+| security-llama3.2-3b.Q5_K_S.gguf | Q5_K_S | 2.27 GB |
+| security-llama3.2-3b.Q6_K.gguf | Q6_K | 2.64 GB |
+| security-llama3.2-3b.Q8_0.gguf | Q8_0 | 3.42 GB |
+
+## Quants Usage 
+
+(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
+
+Here is a handy graph by ikawrakow comparing some lower-quality quant
+types (lower is better):
+
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

config.json

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+{
+    "model_type": "llama"
+}

configuration.json

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

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