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Model: Alindstroem89/Llama-3.2-1B-Instruct_guardrail Source: Original Platform
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Llama-3.2-3B-Instruct.F16.gguf filter=lfs diff=lfs merge=lfs -text
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Llama-3.2-3B-Instruct.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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Llama-3.2-1B-Instruct.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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Llama-3.2-1B-Instruct.F16.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:08846ba6f08669e931e810f642949e4bfd24cb5d097f6f7c07ce71d1489c5274
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size 2479596032
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Llama-3.2-1B-Instruct.Q4_K_M.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:a68a68de09b996bb9b4fb4616de3e494ffd384096c5f47b5322c466f2464fc93
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size 807694848
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FROM Llama-3.2-1B-Instruct.Q4_K_M.gguf
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TEMPLATE """{{ if .Messages }}
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{{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|>
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{{- if .System }}
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{{ .System }}
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{{- end }}
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{{- if .Tools }}
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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.
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{{- end }}
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{{- end }}<|eot_id|>
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{{- range $i, $_ := .Messages }}
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{{- $last := eq (len (slice $.Messages $i)) 1 }}
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{{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|>
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{{- if and $.Tools $last }}
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Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.
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Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables.
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{{ $.Tools }}
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{{- end }}
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{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
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{{ end }}
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{{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|>
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{{- if .ToolCalls }}
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{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}{{ end }}
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{{- else }}
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{{ .Content }}{{ if not $last }}<|eot_id|>{{ end }}
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{{- end }}
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{{- else if eq .Role "tool" }}<|start_header_id|>ipython<|end_header_id|>
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{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
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{{ end }}
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{{- end }}
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{{- end }}
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{{- else }}
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{{- if .System }}<|start_header_id|>system<|end_header_id|>
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{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
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{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
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{{ end }}{{ .Response }}{{ if .Response }}<|eot_id|>{{ end }}"""
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PARAMETER stop "<|start_header_id|>"
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PARAMETER stop "<|end_header_id|>"
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PARAMETER stop "<|eot_id|>"
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PARAMETER stop "<|eom_id|>"
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PARAMETER temperature 1.5
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PARAMETER min_p 0.1
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README.md
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README.md
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---
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datasets:
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- Alindstroem89/guardrail-training-dataset
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language:
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- en
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base_model:
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- unsloth/Llama-3.2-1B-Instruct-GGUF
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pipeline_tag: text-generation
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tags:
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- gguf
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- llama.cpp
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- unsloth
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---
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# Llama-3.2-1B-Instruct_guardrail : GGUF
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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.
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This model was fine-tuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
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## Model Description
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Fine-tuned to:
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- Recognize and resist prompt injection techniques
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- Maintain boundaries and refuse to reveal protected information
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- Remain helpful and friendly for legitimate conversations
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- Politely explain refusals without being unnecessarily rigid
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## Training Details
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**Base Model:** [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct)
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**Training Configuration:**
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- LoRA Rank (r): 32
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- LoRA Alpha: 32
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- Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
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- Use RSLoRA: True
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- Optimizer: adamw_8bit
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- Learning Rate: 1e-4
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- Batch Size: 2 per device
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- Gradient Accumulation: 8 steps
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- Epochs: 1
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- Max Sequence Length: 8192
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**Dataset:** Custom dataset with guardrail conversations (prompt injection attempts with refusals) and normal helpful conversations.
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## Usage
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### With llama-cli
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```bash
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llama-cli -hf Alindstroem89/Llama-3.2-1B-Instruct_guardrail:F16 --jinja
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```
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### Download with Hugging Face CLI
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```bash
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# Download all GGUF files
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hf download Alindstroem89/Llama-3.2-1B-Instruct_guardrail --include "*.gguf" --local-dir ./models
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# Download specific quantization
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hf download Alindstroem89/Llama-3.2-1B-Instruct_guardrail --include "Llama-3.2-1B-Instruct.Q4_K_M.gguf" --local-dir ./models
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```
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### Ollama
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An Ollama Modelfile is included for easy deployment.
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## Available Model Files
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- Llama-3.2-1B-Instruct.Q4_K_M.gguf
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- Llama-3.2-1B-Instruct.F16.gguf
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## Use Cases
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- Chatbots requiring prompt injection resistance
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- AI assistants handling sensitive information
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- AI security research and education
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- Testing guardrail implementations
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## Limitations
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- Primarily tested on English language
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- Not a comprehensive security solution
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- May occasionally be overly cautious
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- Should not be the sole defense mechanism in production
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## Training Infrastructure
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- Framework: [Unsloth](https://github.com/unslothai/unsloth) (2x faster training)
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- Method: LoRA (Low-Rank Adaptation) with rank-stabilized optimization
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- Conversion: GGUF format for efficient inference
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## Finetuning repo
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[Guardrail_finetuning](https://github.com/Alexanderl89/Guardrail_finetuning)
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## License
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This model follows the license of the base Llama 3.2 model.
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37
config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"torch_dtype": "bfloat16",
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"eos_token_id": 128009,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 16,
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"num_key_value_heads": 8,
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"pad_token_id": 128004,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": true,
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"unsloth_fixed": true,
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"unsloth_version": "2026.3.8",
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"use_cache": true,
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"vocab_size": 128256
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}
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