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Model: Alindstroem89/Llama-3.2-3B-Instruct_guardrail Source: Original Platform
2026-06-19 14:22:19 +08:00
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+Llama-3.2-3B-Instruct.Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-3.2-3B-Instruct.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-3.2-3B-Instruct.F16.gguf filter=lfs diff=lfs merge=lfs -text
+Llama-3.2-3B-Instruct.BF16.gguf filter=lfs diff=lfs merge=lfs -text
+Guardrail-Llama-3.2-3B-Instruct.BF16.gguf filter=lfs diff=lfs merge=lfs -text
--- a/Llama-3.2-3B-Instruct.F16.gguf
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+oid sha256:2aaaf20b7af7af225cf8d3f7b3a02daf5d23dd5f92a0f39a99dbf872e6e80bb2
+size 6433688512
--- a/Llama-3.2-3B-Instruct.Q3_K_M.gguf
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+version https://git-lfs.github.com/spec/v1
+oid sha256:b28a1edffa6d79ab81adda275996c8b7bfd473c61544bdc64e9f816bac2ff568
+size 1687159744
--- a/Llama-3.2-3B-Instruct.Q4_K_M.gguf
+++ b/Llama-3.2-3B-Instruct.Q4_K_M.gguf
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+version https://git-lfs.github.com/spec/v1
+oid sha256:8ace48b24e16e768fefcf798d67a5da4f8e7ae09321a6adcdc09a24c67d19f0a
+size 2019378112
--- a/57
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+
+FROM Llama-3.2-3B-Instruct.BF16.gguf
+TEMPLATE """{{ if .Messages }}
+{{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|>
+{{- if .System }}
+
+{{ .System }}
+{{- end }}
+{{- if .Tools }}
+
+You are a helpful assistant with tool calling capabilities. When you receive a tool call response, use the output to format an answer to the original use question.
+{{- end }}
+{{- end }}<|eot_id|>
+{{- range $i, $_ := .Messages }}
+{{- $last := eq (len (slice $.Messages $i)) 1 }}
+{{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|>
+{{- if and $.Tools $last }}
+
+Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.
+
+Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables.
+
+{{ $.Tools }}
+{{- end }}
+
+{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}
+{{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|>
+{{- if .ToolCalls }}
+
+{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}{{ end }}
+{{- else }}
+
+{{ .Content }}{{ if not $last }}<|eot_id|>{{ end }}
+{{- end }}
+{{- else if eq .Role "tool" }}<|start_header_id|>ipython<|end_header_id|>
+
+{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}
+{{- end }}
+{{- end }}
+{{- else }}
+{{- if .System }}<|start_header_id|>system<|end_header_id|>
+
+{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
+
+{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}{{ .Response }}{{ if .Response }}<|eot_id|>{{ end }}"""
+PARAMETER stop "<|start_header_id|>"
+PARAMETER stop "<|end_header_id|>"
+PARAMETER stop "<|eot_id|>"
+PARAMETER stop "<|eom_id|>"
+PARAMETER temperature 1.5
+PARAMETER min_p 0.1
--- a/README.md
+++ b/README.md
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+---
+datasets:
+- Alindstroem89/guardrail-training-dataset
+language:
+- en
+base_model:
+- unsloth/Llama-3.2-3B-Instruct
+pipeline_tag:  text-generation
+tags:
+- gguf
+- llama.cpp
+- unsloth
+---
+
+
+# Llama-3.2-3B-Instruct_guardrail : GGUF
+
+A fine-tuned Llama 3.2 model trained to resist prompt injection attacks. This model was created for the [Prompt Injection Challenge](https://github.com/Alexanderl89/Guardrail_finetuning) - an AI security challenge where users attempt to extract a hidden flag from a chatbot using prompt injection and social engineering techniques.
+
+This model was fine-tuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
+
+## Model Description
+
+Fine-tuned to:
+- Recognize and resist prompt injection techniques
+- Maintain boundaries and refuse to reveal protected information
+- Remain helpful and friendly for legitimate conversations
+- Politely explain refusals without being unnecessarily rigid
+
+## Training Details
+
+**Base Model:** [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct)
+
+**Training Configuration:**
+- LoRA Rank (r): 32
+- LoRA Alpha: 32
+- Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- Use RSLoRA: True
+- Optimizer: adamw_8bit
+- Learning Rate: 1e-4
+- Batch Size: 2 per device
+- Gradient Accumulation: 8 steps
+- Epochs: 1
+- Max Sequence Length: 8192
+
+**Dataset:** Custom dataset with guardrail conversations (prompt injection attempts with refusals) and normal helpful conversations.
+
+## Usage
+
+### With llama-cli
+
+```bash
+llama-cli -hf Alindstroem89/Llama-3.2-3B-Instruct_guardrail:F16 --jinja
+```
+
+### Download with Hugging Face CLI
+
+```bash
+# Download all GGUF files
+hf download Alindstroem89/Llama-3.2-3B-Instruct_guardrail --include "*.gguf" --local-dir ./models
+
+# Download specific quantization
+hf download Alindstroem89/Llama-3.2-3B-Instruct_guardrail --include "Llama-3.2-3B-Instruct.Q4_K_M.gguf" --local-dir ./models
+```
+
+### Ollama
+
+An Ollama Modelfile is included for easy deployment.
+
+## Available Model Files
+
+- Llama-3.2-3B-Instruct.Q3_K_M.gguf
+- Llama-3.2-3B-Instruct.Q4_K_M.gguf
+- Llama-3.2-3B-Instruct.F16.gguf
+- Llama-3.2-3B-Instruct.BF16.gguf
+
+## Use Cases
+
+- Chatbots requiring prompt injection resistance
+- AI assistants handling sensitive information
+- AI security research and education
+- Testing guardrail implementations
+
+## Limitations
+
+- Primarily tested on English language
+- Not a comprehensive security solution
+- May occasionally be overly cautious
+- Should not be the sole defense mechanism in production
+
+## Training Infrastructure
+
+- Framework: [Unsloth](https://github.com/unslothai/unsloth) (2x faster training)
+- Method: LoRA (Low-Rank Adaptation) with rank-stabilized optimization
+- Conversion: GGUF format for efficient inference
+
+## Finetuning repo
+
+[Guardrail_finetuning](https://github.com/Alexanderl89/Guardrail_finetuning)
+
+## License
+
+This model follows the license of the base Llama 3.2 model.
--- a/config.json
+++ b/config.json
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+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 128000,
+    "torch_dtype": "bfloat16",
+    "eos_token_id": 128009,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 3072,
+    "initializer_range": 0.02,
+    "intermediate_size": 8192,
+    "max_position_embeddings": 131072,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 24,
+    "num_hidden_layers": 28,
+    "num_key_value_heads": 8,
+    "pad_token_id": 128004,
+    "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,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.3.8",
+    "use_cache": true,
+    "vocab_size": 128256
+}